MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. We enable organizations of all sizes to easily build, scale, and run modern applications by helping them modernize legacy workloads, embrace innovation, and unleash AI. Our industry-leading developer data platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available in more than 115 regions across AWS, Google Cloud, and Microsoft Azure. Atlas allows customers to build and run applications anywhere—on premises, or across cloud providers. With offices worldwide and over 175,000 new developers signing up to use MongoDB every month, it’s no wonder that leading organizations, like Samsung and Toyota, trust MongoDB to build next-generation, AI-powered applications.

As a Staff Technical Program Manager, you will own the successful delivery of complex, cross-functional efforts. You will partner closely with stakeholders across the organization from Engineering, Product, Product Design, and even Field-facing teams to develop and execute against our overall product vision and business objectives, uncover solutions to technical challenges, and provide data-driven insights that power strategic decision-making.

We are looking to speak to candidates who are based in Atlanta, Austin, Baltimore, Boston, Charlotte, Chicago, Columbus, Dallas, Miami, Nashville, New York City, Philadelphia, Raleigh, Washington DC, Tysons for our hybrid working model.

Position Expectations

• Leverage technical acumen and analytical skills to drive engineering programs forward and maximize business value delivery
• Recognize patterns in a sea of information and take action accordingly
• Develop a strategic approach to program management that balances agility and process. This involves discerning when to provide direct guidance and when to empower teams by establishing clear frameworks and guidelines for ongoing initiatives
• Build strategic partnerships with Product, Design, and Engineering stakeholders
• Expand knowledge into new domains as called upon
• Act as a mentor to junior TPMs and lead by example
• Embody MongoDB's core values and leadership principles

The right candidate for this role will be

• Experienced with 10+ years of working in an engineering organization leading complex cross-functional technical programs
• Experienced with 5+ years of Software development background
• Able to partner with technical stakeholders in methodically breaking down engineering challenges to deliver against abstract goals
• Able to participate in technical design discussions and summarize trade-offs, outcomes, and next steps
• Skilled at communicating across a diverse set of engineers and stakeholders
• Capable of deep user empathy and adept at balancing product goals and engineering constraints to deliver impactful experiences
• Hyper-organized and capable of coordinating across multiple independent work streams and organizations
• A role model for effective execution practices, driven by an attuned sense of priority and urgency
• Able to leverage their experience in program delivery to influence improvements to our tools, operations, architecture
• Trained in working with project tracking software (e.g. Jira, Rally, MS Project)
• Familiar with MongoDB or a comparable technology

Nice to have!

• Exposed to generative AI and Machine Learning
• Interested in business automation work such as scripting in Python, Google Apps Script and Slack
• Familiar with tools and methodologies for code analysis, refactoring, and automated testing

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

REQ ID: 2263203305

Position Title: Corporates Ratings - Associate Director (New York / Chicago) 

Entity: Kroll Bond Rating Agency, LLC

Employment Type: Full-Time

Location: New York, New York; Chicago, Illinois 

Summary/Overview:

KBRA (Kroll Bond Rating Agency, LLC) is seeking an Associate Director to join our growing our Corporates Ratings team with a focus on conducting fundamental credit analysis and research across a wide range of sectors.

The Associate Director will work in a highly collaborative team that liaises with internal and external stakeholders to produce high quality and timely corporate credit opinions. The Associate Director will also help conduct credit research on a variety of industrial sectors, including analyzing and publishing trends, key rating factors, and evolving credit metrics. This individual will have significant interaction with senior management of corporate issuers, and their advisors. This position is based in our New York or Chicago office.

Job Responsibilities:

• Lead the credit rating process by preparing internal credit memos, analyzing cash flows, performing sector analysis, and publishing credit reports.
• Author sector research for investors and lenders across private credit, direct lending, and public debt markets.
• Assist in the development and evolution of rating processes and analytical tools.
• Participate in issuer and investor meetings to explain KBRA methodologies and demonstrate understanding of industry trends.

Key Job Qualifications:

Flourish in a fast-paced, deadline driven environment.  Effectively collaborate with your team members and are motivated to succeed and continually strive for consistency, accuracy and timeliness.

You will be successful in this role if you:

• Bachelor’s degree in finance, accounting, economics or management or any subject that prepared you to be a thoughtful qualitative and quantitative analyst and a strong writer. Master’s degree and/or CFA are preferred.
• A minimum of five (5) or more years of work experience performing credit analysis within Corporates in a rating agency, buy-side, sell-side, banking, or other similar environment within a bank or other financial institution.  Prior fundamental credit training (including financial statement analysis) is preferred.
• Conduct due diligence and interact with clients as part of the rating process.
• Possess a solid understanding of capital markets and relevant experience in credit, fixed income research or capital markets.
• Experience with corporate financial statements including 10-Ks, 10-Qs. 
• Work as an integral part of a team and across disciplines in a challenging and dynamic environment.
• Manage time and resources in a fast-growing company.
• Familiarity with Generative AI tools such as ChatGPT for research, data insights, and general productivity is a plus.
• Excellent communication (verbal and written) skills.
• Strong quantitative skills including advanced capabilities with Excel and proficiency in financial statement analysis.
• Ability to work, lead, and seamlessly integrate in a highly collegial, dynamic, and innovative work environment.

Salary Range:

The anticipated annual base salary range for this full-time position is $120,000 to $150,000. Offer amounts are determined by factors such as experience, skills, geography, and other job-related factors.

KBRA Benefits

• A hybrid work schedule (Tuesday, Wednesday and Thursdays in the office)
• Competitive benefits and paid time off
• Paid family and disability leave
• 401(k) plan, including employer match (100% vested)
• Educational and professional development financial assistance
• Employee referral bonus program

About Us:

Kroll Bond Rating Agency, LLC (KBRA) is a full-service credit rating agency registered with the U.S. Securities and Exchange Commission as an NRSRO. Kroll Bond Rating Agency Europe Limited is registered as a CRA with the European Securities and Markets Authority. Kroll Bond Rating Agency UK Limited is registered as a CRA with the UK Financial Conduct Authority pursuant to the Temporary Registration Regime. In addition, KBRA is designated as a designated rating organization by the Ontario Securities Commission for issuers of asset-backed securities to file a short form prospectus or shelf prospectus. KBRA is also recognized by the National Association of Insurance Commissioners as a Credit Rating Provider.

More Info

KBRA encourages applications from all qualified individuals without regard to race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, marital status, citizenship, disability, and veteran status or any other basis prohibited by federal, state or local law.

#LI-DG1

#LI-HYBRID

Position Title: ABS Surveillance Data Entry – Analyst (NY)

Entity: Kroll Bond Rating Agency, LLC

Employment Type: Full-time

Location: New York, New York

Summary/Overview:

KBRA (Kroll Bond Rating Agency) is seeking an analyst to join our Asset-Backed Securities (ABS) team in the New York, NY office. This individual will be responsible for monthly data input and other tasks associated with the surveillance of ABS sectors such as whole business, solar, aircraft, railcar leases, auto loans, consumer loans, credit cards, home improvement loans, and/or solar loans.

About the Job:

• Perform timely entry of data from monthly deal reports into excel spreadsheets and KBRA’s proprietary system. 
• Identify anomalies in the deal data and promptly alert the appropriate analysts.
• Engage in ongoing surveillance of rated transactions and assist in the preparation of surveillance credit memoranda and reports used for rating committees and publications.
• Manage a list of the sources of the monthly deal reports.

You will be successful in this role if you have:

• B.A. / B.S. degree.
• 0-2 years of business experience within Financial Services.
• Strong Excel skills are critical for this position.
• Exceptional attention to detail.
• Ability to take initiative, work independently, and work closely with partners in a collaborative environment. 
• Experience using financial modeling software (e.g. Intex) a plus.
• Familiarity with Generative AI tools such as ChatGPT for research, data insights, and general productivity is a plus.

Salary Range:

The anticipated annual base salary range for this full-time position is $70,000 to $85,000. Offer amounts are determined by factors such as experience, skills, geography, and other job-related factors.

Benefits:

• A hybrid work schedule (Tuesday, Wednesday, Thursday in the office)
• Competitive benefits and paid time off
• Paid family and disability leave
• 401(k) plan, including employer match (100% vested)
• Employee referral bonus program

About Us:

Kroll Bond Rating Agency, LLC (KBRA) is a full-service credit rating agency registered with the U.S. Securities and Exchange Commission as an NRSRO. Kroll Bond Rating Agency Europe Limited is registered as a CRA with the European Securities and Markets Authority. Kroll Bond Rating Agency UK Limited is registered as a CRA with the UK Financial Conduct Authority pursuant to the Temporary Registration Regime. In addition, KBRA is designated as a designated rating organization by the Ontario Securities Commission for issuers of asset-backed securities to file a short form prospectus or shelf prospectus. KBRA is also recognized by the National Association of Insurance Commissioners as a Credit Rating Provider.

More Info:

KBRA encourages applications from all qualified individuals without regard to race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, marital status, citizenship, disability, and veteran status or any other basis prohibited by federal, state or local law.

#LI-DG1

#LI-HYBRID

Role

We are looking for a Senior Solutions Consultant - AI Security to join our Sales and Go-to-Market team. This is a remote role based in the US, reporting to the Director of Solutions Consulting - AI Security. The Zscaler Sales and Go-to-Market team is a global group of professionals passionate about delighting customers and sharing expertise to drive a secure, cloud-enabled digital future. As a key technical partner, you will demonstrate the power and agility of Zscaler cloud transformation to cement our position as the world leader in cloud security.

What you’ll do (Role Expectations)

• Drive business growth by partnering with Account Executives to deliver compelling product demonstrations and presentations

• Plan, execute, and deliver Proof of Concept (POC) and Proof of Value (POV) projects demonstrating product capabilities and business ROI

• Form lasting customer relationships to ensure success, facilitate smooth onboarding, and cultivate Zscaler product champions

• Act as the voice of the customer by channeling insights to the development team to shape the product roadmap and feature scoping

• Maintain expert-level knowledge of Zscaler offerings, developing business value propositions, and monitoring competitor products

Who You Are (Success Profile)

• You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.

• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.

• You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.

• You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.

• You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

What We’re Looking for (Minimum Qualifications)

• Proven experience in a customer-facing technical role such as sales engineering or consulting

• Knowledgeable in Generative AI, cybersecurity, data science or adjacent technical domains

• Excellent communication skills for both technical deep-dives and clear executive briefings

• Proven ability in solving complex technical challenges and effectively managing multiple projects simultaneously

• BSc in Computer Engineering, Computer Science, or equivalent experience

What Will Make You Stand Out (Preferred Qualifications)

• Experience with Python or TypeScript and utilizing REST APIs with tools like cURL and Postman

• AI red teaming experience, agentic system development, or MCP server experience

• Familiarity in major cloud platforms: AWS, Azure, or GCP

#LI-AA5 #LI-Remote

About Sovrn

Every interesting company solves important problems for other people.  Sovrn is a Software and Data business that helps Open Web businesses be and remain independent.  We help them understand their business better, operate more efficiently, and make & keep more money.

• We believe in the freedom and free-flow of information.
• We believe the Open Web is the largest source of this information.
• We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

• Understand their business better, so they can make better decisions
• Operate their business more efficiently, so they can invest in what matters most
• Make (and Keep) more money, so they control their own destiny

About the Role

We're looking for a Principal Software Engineer with deep roots in adtech infrastructure and a genuine conviction about what AI-native engineering looks like in practice. This role is backend-focused at its core — you'll work on the systems that power our exchange, from bid request processing and traffic shaping to data pipelines and auction logic. But we're also at an inflection point in how we build software, and we want this person to help lead us through it.

From a generative/agentic AI capabilities standpoint, we already use LLMs and agentic tooling across our data stack and, we’re looking for is someone who can help us take that from general adoption to intentional practice — who has strong opinions about where AI creates real leverage in a high-throughput adtech environment, and who can bring the rest of the engineering organization along with them.

Languages/components/tools in our stack: Java, Kafka (Redpanda), AWS, Databricks, Datadog, ONYX.

What you'll be doing:

• Own the design and evolution of backend systems that operate at exchange scale — high throughput, low latency, always on
• Lead architectural decisions across auction infrastructure, bid processing, traffic shaping, and supply-side integrations
• Establish and champion AI engineering practices across the team — from prompt engineering and RAG patterns to agentic workflow design and LLM evaluation
• Identify high-leverage opportunities to apply AI in our adtech stack: traffic scoring and filtering, bid shading, yield optimization, anomaly detection, and automated decisioning
• Set standards for how we evaluate, trust, and operate AI-powered systems in production — including observability, fallback behavior, and model governance
• Drive technical standards, design reviews, and engineering best practices across a senior team
• Partner with product, data science, and platform teams to ship end-to-end
• Help the broader engineering team build fluency and confidence with AI tooling, not just tolerance of it
• Mentor and level up engineers through code review, design collaboration, and hands-on guidance

A successful candidate will have:

• 10+ years of software engineering experience, with a strong backend track record
• 5+ years working specifically in adtech infrastructure — SSP, DSP, exchange, or ad server environments
• Deep fluency in the programmatic ecosystem: OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar
• Experience building and operating systems at exchange scale — high QPS, strict latency budgets, fault tolerance
• Demonstrated experience leading AI or agentic engineering efforts in production environments — not just experimentation, but shipped, operated, and iterated on
• Hands-on experience with LLM integration patterns: RAG, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks
• Strong distributed systems fundamentals: messaging, caching, service architecture, observability
• Comfort driving technical decisions in ambiguous, fast-moving environments
• A point of view on where AI is and isn't the right tool — and the credibility to make that case

Location: This position is based in our Boulder Colorado or New York City office. Applicants need to be able to commute/travel to our Boulder or New York City location. For exceptional candidates we would consider remote locations in these states: AZ, CA, CO, GA, MD, MA, MI, MO, NJ, NY, NC, OK, TX, UT, WA. #LI-Hybrid #LI-Remote

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we’re looking for.

Sovrn Core Values: Candid, Customer Empathy, Learning, Scrappy, Second Order Thinking

Compensation and Benefits

The base pay range for this position is $200,000 to $240,000 annually. In addition to the base salary, the total compensation package includes bonus, equity, and benefits. Actual earnings may vary depending on the candidate’s direct experience, skills, industry knowledge, and location.

Sovrn offers a full slate of benefits from competitive salaries, stock options, medical, dental, and vision coverage, short and long-term disability, life insurance, 11 paid holidays, flexible vacation, commuter benefits, a 401(k) plan and match, and a paid parental leave program.

Job Posting

Applications will be accepted until July 1, 2026.

Equal Opportunity Employer

Sovrn is proud to be an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants regardless of race, color, religion, gender, gender identity, age, national origin, disability, parental or pregnancy status, marriage and civil partnership, sexual orientation, veteran status, or any other characteristic protected by law.

Recruitment Agencies

Sovrn does not accept agency resumes. Please do not forward resumes to our jobs alias or Sovrn employees. Sovrn is not responsible for any fees related to unsolicited resumes.

About Sovrn

Every interesting company solves important problems for other people.  Sovrn is a Software and Data business that helps Open Web businesses be and remain independent.  We help them understand their business better, operate more efficiently, and make & keep more money.

• We believe in the freedom and free-flow of information.
• We believe the Open Web is the largest source of this information.
• We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

• Understand their business better, so they can make better decisions
• Operate their business more efficiently, so they can invest in what matters most
• Make (and Keep) more money, so they control their own destiny

About the Role

We’re looking for a Principal Software Engineer (Data) with deep roots in adtech data infrastructure and a genuine conviction about what AI-native data engineering looks like in practice. This is a specialized principal-level engineering role — one that carries all the architectural ownership and technical leadership expectations of a Principal Software Engineer, focused on Sovrn’s Data Collective.

From a generative/agentic AI capabilities standpoint, we already use LLMs and agentic tooling across our data stack and, we’re looking for is someone who can help us take that from general adoption to intentional practice — who has strong opinions about where AI creates real leverage in a high-throughput adtech environment, and who can bring the rest of the engineering organization along with them.

Languages/components/tools in our stack: Python, Pyspark, Kafka, Databricks, AWS

What you'll be doing:

Data Platform & Architecture

• Own the design and evolution of data platform systems that operate at exchange scale; high throughput, real-time streaming, and always-on batch pipelines
• Lead architectural decisions across data infrastructure: pipeline design, data modeling, lakehouse architecture, and data services layers
• Specify data platform components and configurations required for pipeline implementation; define pipeline observability to understand and improve performance at massive scale
• Research, implement, and evolve methods to process and democratize data across the organization
• Drive technical standards, design reviews, and engineering best practices across a senior team
• Partner with product, data science, and platform teams to ship end-to-end

AI & Agentic Engineering Leadership

• Establish and champion AI engineering practices across the team, from prompt engineering and RAG patterns to agentic workflow design, LLM evaluation, and progressive implementation of agentic design patterns
• Identify high-leverage opportunities to apply AI in our data stack: intelligent pipeline optimization, anomaly detection, automated data quality, forecasting, and LLM-powered data services
• Lead the evolution of our existing LLM and agentic tooling from passive use to intentional, well-architected integration within our data platform
• Set standards for how we evaluate, trust, and operate AI-powered systems in production, including observability, fallback behavior, and model governance
• Help the broader engineering team build fluency and confidence with AI tooling, not just tolerance of it

Collaboration & Mentorship

• Provide domain expertise across the organization to enable business growth through data services and data models
• Provide counsel to all consumers and stakeholders of data to enable efficient and impactful use of our data assets
• Mentor and level up engineers through code review, design collaboration, and hands-on guidance; foster a culture of innovation and continuous learning
• Operate with high autonomy across ambiguous, high-impact problems

A successful candidate will have:

• 10+ years of software engineering experience, with a strong data engineering and backend track record
• 5+ years working specifically in adtech data infrastructure, SSP, DSP, exchange, or ad server environments
• Deep fluency in the programmatic ecosystem: OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar
• Excellent understanding of real-time streaming and batch pipelines, big data, and data lakes; hands-on experience in distributed data processing in the AWS ecosystem
• Strong understanding of second-layer big data platforms such as Snowflake and Databricks, applicable use cases, best practices, implementation, and support considerations
• Strong experience in structured, unstructured, and semi-structured data techniques; metadata management, data lineage, and data governance
• Experience with data security and compliance (PII, CCPA, GDPR, etc.)
• Demonstrated experience leading AI or agentic engineering efforts in production environments; not just experimentation, but shipped, operated, and iterated on
• Hands-on experience with LLM integration patterns: RAG, vector DBs, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks
• Ability to clearly document and communicate architectural concepts at multiple levels; ability to lead problem definition, solution designs, and implementation work plans
• Understanding of DevOps and SRE practices
• Comfort driving technical decisions in ambiguous, fast-moving environments
• A point of view on where AI is and isn’t the right too, and the credibility to make that case

Location: This position is based in our Boulder Colorado or New York City office. Applicants need to be able to commute/travel to our Boulder or New York City location. For exceptional candidates we would consider remote locations in these states: AZ, CA, CO, GA, MD, MA, MI, MO, NJ, NY, NC, OK, TX, UT, WA. #LI-Remote, #LI-Hybrid

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we’re looking for.

Sovrn Core Values: Candid, Customer Empathy, Learning, Scrappy, Second Order Thinking

Compensation and Benefits

The base pay range for this position is $200,000 to $240,000 annually. In addition to the base salary, the total compensation package includes bonus, equity, and benefits. Actual earnings may vary depending on the candidate’s direct experience, skills, industry knowledge, and location.

Sovrn offers a full slate of benefits from competitive salaries, stock options, medical, dental, and vision coverage, short and long-term disability, life insurance, 11 paid holidays, flexible vacation, commuter benefits, a 401(k) plan and match, and a paid parental leave program.

Job Posting

Applications will be accepted until July 1, 2026.

Equal Opportunity Employer

Sovrn is proud to be an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants regardless of race, color, religion, gender, gender identity, age, national origin, disability, parental or pregnancy status, marriage and civil partnership, sexual orientation, veteran status, or any other characteristic protected by law.

Recruitment Agencies

Sovrn does not accept agency resumes. Please do not forward resumes to our jobs alias or Sovrn employees. Sovrn is not responsible for any fees related to unsolicited resumes.

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

About the team

We’re looking for an experienced regulatory licensing professional to manage our Audit & Regulatory Affairs team. In this role, you will coordinate with Stripe’s Legal, Financial Crimes, Finance, Accounting, Product, and Security teams, to (i) coordinate and submit regulatory license applications; (ii) manage license renewals and licence maintenance items; (iii) manage US regulatory exams and inquiries; and (iv) oversee other aspects of the team’s day-to-day activities, with a focus on licensing and exam activities related to digital assets.

Stripe’s mission is to increase the GDP of the internet. We build the most powerful and flexible tools for running an internet business. We handle billions of dollars each year and enable millions of users around the world to scale faster and more efficiently by building their businesses on Stripe. More than 80% of American adults bought something through Stripe in the last year.

Namely, Stripe’s North America regulatory affairs program plays a critical role in ensuring Stripe effectively manages its regulatory requirements and ongoing regulatory obligations for certain licensed US and Canadian entities. The role provides an excellent opportunity for a talented candidate to deploy their cross-functional licensing and regulatory exam management skills in a fast-paced and rigorous environment, enhance their regulatory risk management expertise, learn about digital assets, carve out opportunities to grow and develop, as well as lead a team of skilled and talented professionals. The successful candidate will gain wide ranging exposure to Stripe's diverse range of business lines, senior leadership, and the governance and workings of the organization.

The ideal candidate will be a self-starter who is exceptionally organized, thorough and detail-oriented, with a background in financial services, payments, digital assets, or other regulated sectors. In this role, you will get a chance to help build, enhance, mature and evolve our regulatory licensing and exam programs and be a key cross-functional partner. Stripe is both a technology company and a financial services company, and you will need to be comfortable in both worlds.

What you’ll do

The Audit & Regulatory Management Lead will help to ensure that all of the relevant components of the regulatory affairs program for licensed US and Canadian entities remain current and compliant, oversee managing and executing US regulatory examinations, and support our Compliance Audit Strategy, with the expectation to build a team who, in due time, will manage and drive exam oversight globally.

Responsibilities

• Coordinate responses to state regulatory inquiries, filings, forms, applications, renewals, surety bonds, and anything else required for license maintenance
• Assist in managing regulatory exams and inquiries, responding to regularly exam matters and monitoring the implementation and closure of any remediation items
• Stay abreast of regulatory changes, particularly with respect to digital assets, and how they impact regulatory affairs’ licensing and exam management program activities and responsibilities
• Establish and maintain relationships with internal and external partners and business units, relevant state and local agencies, licensing consultants, and outside counsel 
• Work proactively with Legal, Finance, Financial Crimes, Affiliate Officers, Product, and other key stakeholders on managing the regulatory affairs’s licensing and examination program 
• Support any end-to-end licensing, examination and related ad hoc inquiries as required in partnership with key stakeholders 
• Engage in special projects in support of the regulatory affairs program in North America

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• Bachelor’s degree in Finance, Political Science, Economics, or a related field 
• 10+ years experience in financial services, with significant experience in payments— familiarity with money transmitter statutes, NMLS, the BSA, and other areas of financial services, such as digital assets, lending, loan brokering and collections
• A history of professional achievement, including excellent academic credentials and success at a payments startup, financial institution, public sector organization, law enforcement agency, or top in-house legal department 
• Demonstrated familiarity with leveraging Generative AI and agentic workflows to automate preparing responses to routine questions and requests
• The ability to effectively and credibly communicate with senior executives, regulators and other stakeholders 
• Great interpersonal and critical thinking skills, good judgment, and the ability to work effectively with a wide variety of people inside and outside the company
• Robust communication skills

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

About the team

The Finance & Strategy team is an integral thought partner to Stripe’s core functional leaders.  We bring understanding and predictability to Stripe’s bottom line financials.  We work cross functionally across many surfaces at Stripe and help steward Stripe’s resource investments to improve the durability of our long term financial performance.

What you’ll do

We’re hiring a seasoned finance leader to run Central GTM Finance & Strategy on the GTM F&S team. You will lead global planning, consolidation, metrics & analytics, and strategic initiatives that scale GTM finance operations. This role partners closely with Global Sales leadership to guide go-to-market strategy, improve forecasting accuracy, optimize investments, and accelerate enterprise growth as Stripe expands internationally.

Responsibilities

• Lead and develop a team of eight finance professionals supporting Global Sales; deliver insights across user-level performance, sales productivity, pipeline conversion, quota attainment, and strategic account planning to support recurring business reporting cadences.
• Provide finance leadership for global sales support functions including Solution Architecture, Customer Success, Renewals, and GTM Operations.
• Own global forecasting, planning, and budgeting for Sales; surface actionable insights that inform investment decisions and drive sales efficiency.
• Define and maintain key business metrics and KPIs to monitor health and proactively flag risks and opportunities.
• Use data-driven analysis to diagnose performance, recommend actions to accelerate revenue growth and margins, and optimize investments.
• Partner with Sales, Sales Operations & Strategy, and Marketing to evaluate ROI of GTM initiatives and customer acquisition strategies.
• Build dynamic financial and operating models to assess new sales motions, pricing changes, large enterprise deals, and regional expansion scenarios.
• Work with leadership to create long-range planning models that align team strategy with GTM and company priorities.
• Design, scale, and implement reporting and dashboards that track funnel metrics and primary investment drivers.
• Independently run complex SQL queries and perform hands-on analysis of raw data to validate models, uncover root causes, and produce actionable insights.
• Leverage AI and generative tools to speed analysis, automate repetitive tasks, surface insights, and synthesize recommendations.
• Drive process automation and data streamlining to improve reporting accuracy and decision velocity.
• Contribute to quarterly business reviews and long-range planning with scenario analysis and strategic recommendations.
• Translate complex financial and sales data into clear, concise narratives for executive audiences.

Who you are

You’re an analytical, execution-oriented finance leader with strong business acumen and a passion for GTM strategy. You thrive in fast-moving, ambiguous environments and balance strategic thinking with hands-on work. If you want to help Stripe scale its enterprise footprint and drive decisions through data, we want to hear from you.

Minimum requirements

• 10+ years in FP&A, Strategic Finance, or GTM Finance supporting enterprise sales teams.
• Proven ability to analyze pipeline health, forecast revenue, and model user and sales dynamics in complex B2B settings.
• Track record of partnering with senior GTM leaders and influencing decisions through financial insight.
• Strong financial modeling experience and familiarity with large, multi-variable planning tools.
• Proficiency in SQL to independently query and manipulate complex datasets.
• Analytical fluency at scale; comfortable working directly with large, complex datasets (not just financial models) to independently validate outputs, diagnose root causes, and drive conclusions in close partnership with Engineering, Data Science, and Analytics teams. 
• Ability to navigate ambiguous, evolving data environments; experience identifying non-obvious data pitfalls, understanding how data flows across systems, and building reliable analyses in settings where infrastructure and tooling are still maturing. 
• Minimum 3 years of people management experience with success leading high-performing finance teams.
• Excellent communication and presentation skills; ability to distill complex concepts into clear, actionable insights.
• Comfortable operating in fast-paced, ambiguous environments; able to balance strategy with tactical execution.

Preferred qualifications

• Experience working at a high-growth technology or financial services company
• Anaplan, Tableau or other equivalent application experience
• Advanced SQL skills
• Experience in data visualization and dashboarding

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a large amount of work ahead of us. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

At its core, Stripe is a treasury company, and the Treasury Finance team is key to building the Global Payments and Treasury Network (GPTN). We collaborate closely with product, engineering, sales, and finance teams to create innovative solutions that enhance Stripe’s financial capabilities. As part of Treasury Finance’s AI and Quantitative Analytics team, you’ll work at the intersection of finance, artificial intelligence, and product development, building autonomous agents and intelligent solutions that enhance treasury capabilities and enable us to better serve and scale with the global economy. 

What you’ll do

Within Treasury Finance AI and Quantitative Analytics, you’ll leverage your finance, artificial intelligence (AI) and technical expertise to  build intelligent solutions that enhance Stripe’s treasury capabilities. You’ll develop AI-powered tools and models, autonomous agents, and data products that automate complex workflows and provide actionable insights. Working at the intersection of artificial intelligence, finance, and product development, you'll collaborate with engineering and finance teams to deploy cutting-edge AI applications that scale with Stripe's growth and support our mission of increasing the GDP of the internet. 

You will:

• Apply your treasury and finance domain expertise to identify high-impact opportunities where AI can solve complex liquidity, financial risk management, and capital markets challenges
• Build AI-powered tools and artificial intelligence solutions to automate treasury workflows, enhance risk management, and scale our operations
• Design and deploy autonomous AI agents using frameworks like LangChain and LangGraph that can reason, plan, and execute complex multi-step problems and enhance decision-making processes
• Ship interactive data products and self-service analytics tools using Python frameworks (Streamlit, Dash, Gradio) that provide real-time insights and actionable intelligence
• Work across Treasury Finance and partner teams to scope AI-driven solutions that allow us to optimize and scale our most complex, data-driven financial workflows 
• Explore and prototype cutting-edge AI applications to address emerging treasury challenges

Who you are

We’re looking for a technical problem-solver with treasury and finance expertise who’s excited about leveraging artificial intelligence and who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• Bachelor’s degree in finance, mathematics, computer science, engineering or a related field with 5+ years of work experience in software development, data science, or treasury/finance roles 
• Working familiarity with financial risk and capital markets concepts
• Proficiency in Python and experience with AI/ML frameworks including PyTorch and TensorFlow
• Experience with Large Language Model (LLM) frameworks such as LangChain, LangGraph, or similar tools for building AI applications and agentic systems
• Solid understanding of AI fundamentals, including deep learning, natural language processing, and generative AI
• Results-oriented, with a focus on delivering impact in a fast-paced environment 
• Excellent communication skills for collaborating with product, engineering, and finance teams
• Highly organized, with attention to detail and the ability to manage tight deadlines

Nice to have

• • Familiarity with the payments industry and fintech landscape
  • Experience with Databricks and cloud platforms (AWS, GCP, Azure) 
  • Software engineering experience in Ruby

Data at Brex

The Data organization develops insights, models, and data infrastructure for teams across Brex, including Sales, Marketing, Product, Engineering, and Operations. Our Data Scientists, Analysts, and Engineers work together to make data—and insights derived from data—a core asset across the company.

What you’ll do

As a senior Data Analyst (DA III), you will own the end-to-end analytics lifecycle for one or more business areas at Brex. You’ll go beyond building dashboards—you’ll frame the right questions, design rigorous analyses, apply statistical methods, and translate your findings into clear recommendations for leadership. You will also serve as a technical leader on the Data Analytics team, mentoring more junior analysts and helping define the standards and best practices that elevate the team’s work.

This role sits at the intersection of analytics, analytics engineering, and business strategy. You’ll work in a modern data stack environment and partner closely with Data Scientists, Data Engineers, and senior leaders across the organization.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Own the analytics lifecycle for assigned business areas: from problem framing and data sourcing through analysis, insight generation, and stakeholder presentation.
• Build and maintain dashboards and self-service reporting tools that enable business teams to independently track performance, identify risks, and make data-driven decisions.
• Write production-quality SQL and Python code to extract, transform, and analyze data at scale.
• Collaborate with Data Engineers and Data Scientists to develop and maintain analytical data models, improve data pipelines, and ensure data quality across the organization.
• Partner with leadership across Sales, Operations, Product, Finance, and other departments to identify high-impact analytical opportunities and deliver actionable recommendations.
• Mentor other data analysts and contribute to the development of team standards, documentation, code review practices, and analytical frameworks.
• Proactively identify gaps in data infrastructure, propose improvements, and contribute to the evolution of the team’s tooling and processes.

Requirements

• 5+ years of experience in data analytics, business intelligence, or a related quantitative role.
• 3+ years of experience partnering directly with Sales, Operations, Product, or equivalent business teams as an embedded analytics partner.
• Advanced SQL proficiency, including CTEs, window functions, performance optimization, and working across complex data models.
• Proficiency in Python for data analysis, automation, and modeling (Pandas, NumPy, scikit-learn, or similar).
• Experience with cloud data warehouses, particularly Snowflake (BigQuery and Databricks also valued).
• Hands-on experience with BI and data visualization tools (Looker, Tableau, Hex, or similar).
• Strong stakeholder management skills—proven ability to present complex technical findings to non-technical audiences.
• Experience with generative AI and LLM-based tools (Claude Code, Cursor, GitHub Copilot) to perform and accelerate analyses, automated reporting, and build self-service data tools.

Bonus points

• Demonstrated experience applying statistical methods to business problems (e.g., regression, classification, A/B testing).
• Experience with dbt for data modeling and transformation.
• Experience building and maintaining data pipelines using orchestration tools such as Airflow.
• Experience working with APIs for data ingestion and integration.
• Familiarity with version control systems (Git).
• Experience in fintech, financial services, or payments.
• Track record of leading cross-functional analytics projects from scoping through delivery.

Compensation

The expected salary range for this role is $114,192 - $142,740. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

• Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
• Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
• Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
• Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
• Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
• Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
• Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

• 5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
• Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
• Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
• Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
• Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
• Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
• Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE) 
• Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

• Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
• Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
• Familiarity with Microsoft security, compliance, and AI governance ecosystems.
• Familiarity with trust and safety concepts for generative AI and autonomous systems.
• Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
• Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
• Experience working in highly regulated or audit-sensitive environments.

What We Offer:

• Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 
  The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

Job Applicant Privacy Notice 

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

• Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
• Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
• Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
• Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
• Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
• Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
• Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

• 5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
• Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
• Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
• Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
• Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
• Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
• Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE) 
• Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

• Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
• Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
• Familiarity with Microsoft security, compliance, and AI governance ecosystems.
• Familiarity with trust and safety concepts for generative AI and autonomous systems.
• Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
• Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
• Experience working in highly regulated or audit-sensitive environments.

What We Offer:

• Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 
  The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

Job Applicant Privacy Notice 

*Please note, we are open to remote candidates for this role.

Zocdoc’s most important asset is our people.  As a Senior Software Engineer on the Scheduling team, you’ll play a meaningful role in building the best healthcare journey for our patients and providers possible. You will help develop experiences not just in Zocdoc, but integrate with other ecosystems to reach patients where they are. 

At our best, we empower patients to make informed healthcare decisions with confidence while driving a significant, high-visibility impact on Zocdoc’s business. You’ll tackle complex technical challenges, simplifying an array of complex domains. These domains are across the tech stack; from our modern backend, to frontend experiences that are deployed on Zocdoc and on client’s own interfaces.. You will ensure scalability, resilience, and user experience are at the forefront of everything we build. 

As a Senior Software Engineer, you’ll design, build, and optimize Zocdoc’s applications, ensuring they are scalable, secure, and intuitive. You’ll collaborate closely with product, design, and backend teams, shaping the future of our scheduling experience while mentoring other engineers and championing best practices across the organization.

You’ll enjoy this role if you are…

• Personally motivated by building user facing applications that are changing the healthcare experience for patients
• A product-driven engineer who loves working in a highly collaborative and supportive environment
• Motivated by building products that make healthcare easier
• Passionate about building diverse and inclusive teams and culture
• Excited to work as part of a cross functional development team.

Your day to day is…

• Making a meaningful impact as you participate in all phases of product development, including the design, development, testing, and release of provider and patient-facing features 
• Manage deadlines and navigate ambiguity.
• Shipping scalable production code across the development stack
• Building and fostering relationships with our agile team of engineers, product managers, and designers
• Mentoring your peers, with the notion that helping others means learning and growing yourself
• Promote new technologies and new processes to improve the way we work. 
• Working with cutting edge GenAI tools and technology

You’ll be successful in this role if you have…

• Meaningful (5+ years) experience in full stack software development
• A results oriented mindset. You are always striving to deliver quickly and reliably.
• The ability to integrate generative AI tools into daily workflows to automate tasks, foster innovation, and maximize productivity
• Passion for technology and the ability to think critically about performance, scalability, and reliability of software 
• A compassionate but consultative, and confident approach to working with Product partners to deliver high quality products to our patients and providers.
• A strong understanding of system design and architecture
• The mentality of an entrepreneur/owner and a strong bias to action. 
• Superb communication skills! You believe in keeping all of your stakeholders in the loop and know that multiple mediums of communication are the key to success
• Passion for technology and the ability to think critically about performance, scalability, and reliability of software 
• Humility. You believe in treating all people with dignity and respect, regardless of title or tenure 
• An unrelenting desire to build more equitable, inclusive and diverse workplaces.  You view this role as an opportunity to create positive change

Benefits: 

• Flexible, hybrid work environment at our convenient Soho location
• Unlimited Vacation
• 100% paid employee health benefit options (including medical, dental, and vision)
• Commuter Benefits
• 401(k) with employer funded match
• Corporate wellness programs with Headspace and Peloton
• Sabbatical leave (for employees with 5+ years of service)
• Competitive paid parental leave and fertility/family planning reimbursement
• Cell phone reimbursement
• Catered lunch everyday along with beverages and snacks
• Employee Resource Groups and ZocClubs to promote shared community and belonging
• Great Place to Work Certified

The Problem

Every large advertiser runs the same loop: strategist reviews performance data, decides what to test next, briefs a designer, waits for assets, hands them to a media buyer who builds campaigns across Meta and Google and TikTok, launches, monitors, repeats. The process is slow because creative production, campaign management, and performance analysis happen in three separate systems, and insights travel between them through decks, Slack threads, and weekly syncs.

Companies like Nike, Uber, and PepsiCo spend millions per month on digital ads and employ entire teams just to keep this loop turning. Most teams run 10 to 15 creative tests per month because every test requires manual work across multiple tools and teams. The result: most ad spend runs against stale creative, performance degrades from fatigue, and the gap between "what's working" and "what gets made next" is measured in weeks.

Why Agents Change This

Advertising is uniquely high-leverage for AI agents because nearly every step in the workflow is now automatable, and the steps compound:

• Research. Agents can continuously monitor competitor ad libraries, auto-classify creative by format, hook, and message, and surface positioning gaps. What used to require a strategist spending hours in Meta Ad Library happens in the background, always current.
• Creative production. Generative models can now produce ad-quality images and video. The constraint has shifted from "can AI make this" to "can AI make this on-brand and on-spec for each platform." That is a solvable engineering problem, not a research problem.
• Campaign structure. Building properly structured campaigns (naming conventions, audience splits, placement targeting, budget allocation) across five ad platforms is tedious, rules-based work. Agents can do it faster and more consistently than humans.
• Optimization. Detecting creative fatigue, reallocating budget toward winners, and knowing when to rotate in fresh variants is pattern recognition across structured data. Agents excel at exactly this.
• The compounding effect. When one agent handles the full chain (research, production, activation, analysis, iteration) as a single workflow, the cycle time collapses from weeks to hours. A team that runs 15 tests per month can run 50+, not because any individual step got faster, but because the coordination cost between steps drops to nearly zero.

Today: A performance marketing team testing new concepts on Meta and TikTok spends 2 to 3 weeks per cycle.

With agents: The marketing lead describes the objective. An agent analyzes recent performance, identifies the highest-value concepts to test, generates on-brand variants across formats, builds campaigns, launches after human approval, monitors results, detects fatigue, and queues replacements. Same team, 5x to 7x the testing volume, because the human bottleneck becomes strategy and judgment, not execution.

Why Hightouch, Why Now

Hightouch built the Agentic Marketing Platform: one system where AI agents own entire marketing workflows across lifecycle (email, SMS, push through Braze, Iterable, Salesforce, Adobe), advertising (creative production and activation across paid channels), and product experiences (real-time web and mobile experimentation). All three surfaces share the same agent infrastructure, brand and customer context, and warehouse-native data foundation. An insight the ads agent learns about creative performance can inform what the lifecycle agent sends. That shared context across every marketing surface is the product.

Ads is already live, and demand is surging. Enterprise customers are generating ad creative today with agents that analyze performance across Meta, Google, TikTok, LinkedIn, and Snapchat, produce briefs grounded in competitive research and brand guidelines, and generate ready-to-ship ad concepts. Early adoption has been strong: customers who start using it quickly expand usage across campaigns and teams, and inbound interest from enterprise prospects is accelerating. The foundation works. The opportunity is building it into a platform that captures the full advertising workflow.

What Hightouch has that no competitor can replicate:

• Agent orchestration. 25+ specialized agent skills for audience building, content generation, campaign analysis, and multi-step workflow coordination, all composable into end-to-end workflows.
• Content assembly with brand trust. Agents search existing asset libraries for reusable on-brand content before generating anything new. This is what makes output trustworthy enough for enterprises to ship without heavy review cycles.
• Marketing and brand context. A persistent layer connecting customer data, brand guidelines, creative assets, competitive intelligence, and performance history so agents operate with full business context, not generic prompts.
• Warehouse-native data foundation. 62,000 workspaces, a million data syncs per day, 300+ integrations. Customer data stays in the warehouse. No proprietary data store, no duplication, no lock-in.

The Role

You will lead the Agentic Ads Platform. The foundation exists (customers generating ads today), and the opportunity is turning it into the platform that closes the full advertising loop. Four major areas of work:

• Multi-model creative generation. Today the platform generates static images. The roadmap includes video generation, motion graphics, format adaptation, and multi-model orchestration (routing to the best model for each task). Building a creative generation pipeline that produces broadcast-quality output across formats is a hard, high-value problem.
• Marketing context and brand adherence. Making agents genuinely trustworthy for enterprise creative teams. Better context engineering so agents follow brand guidelines, business rules, legal requirements, and channel-specific policies without constant human correction. The gap between "AI-generated ad" and "ad our brand team would actually approve" needs to shrink dramatically.
• Platform depth across ad channels. Deep integrations with Meta, Google, TikTok, LinkedIn, Snapchat, and others. Not just launching ads, but understanding each platform's creative specs, optimization levers, and best practices so agents can automatically adapt creative for each channel, syndicate content across platforms, and optimize placement-level performance. Think: an agent that knows a 9:16 video with text-safe zones works differently on TikTok Reels vs. Instagram Stories and adjusts accordingly.
• Best-in-class agentic creation experience. The interaction model for how marketers work with agents to go from objective to live campaign. This means the conversational interface, the review and approval workflows, the feedback loops that help agents learn what "good" looks like for each brand, and the progressive autonomy controls that let teams start with full human approval and graduate to agent-led execution as trust builds.

Day to day, you will:

• Run deep customer discovery with performance marketing and creative teams at enterprise companies. Understand their workflows, pain points, and what would earn their trust.
• Define the product roadmap tied to revenue milestones. Sequence what to build, what to compose from existing systems, and what to partner on.
• Ship product. Work directly with engineering and design on tight cycles.
• Co-own pipeline and go-to-market. Join customer calls, shape competitive positioning, partner with CS on implementation.

High-visibility role working directly with founders and executive team.

What We're Looking For

• Zero-to-one track record. You have built products from nothing to meaningful revenue, ideally at a B2B SaaS company. The domain matters less than evidence you can find product-market fit under uncertainty.
• Technical depth. You can evaluate architectural tradeoffs, reason about things like multi-model orchestration and API integration design at the level of product decisions, and hold your own in debates with senior engineers.
• Commercial instinct. You think in terms of pipeline, conversion, and expansion. You build roadmaps that map to revenue targets.
• Speed. Small team, high autonomy, tight cycles. We use AI heavily in our own workflows and expect you to do the same.
• Cross-functional trust. This role spans engineering, design, sales, CS, and leadership. You drive clarity from ambiguity.

About the Role

Customer Studio is Hightouch’s second major product that provides a purpose-built no-code suite of features that enables anyone, regardless of their SQL experience, to activate data directly from their warehouse. The product has become an essential part of the day-to-day operations for marketing and growth teams to build and measure audiences and campaigns, and it also serves as part of the underlying infrastructure that powers our agentic marketing workflows. The team blends product and customer-first thinking with challenges at high data scale, helping us reimagine marketing in an AI-native world.

We’re looking to add a product-minded backend engineer to the team. The ideal candidate will be comfortable diving deep into backend and distributed systems and think holistically about architecture, scalability, maintainability, and reliability. We also are looking for someone with strong customer and product thinking and who feels comfortable writing and engaging with product specs.

Some of the problems we’ll be working on include:

• Data Warehouse Performance: We’re consistently pushing the limits of what data warehouses can handle in terms of speed, performance, and interactivity. We explore optimizations generally (e.g. optimizing queries, storing pre-computed state), as well as investigate warehouse-specific improvements.
• Analytics and Experimentation: How can Hightouch help customers build segments and decide what data they want to sync? How can we facilitate experimentation to downstream tools, including tracking the performance of different audiences?
• Real-time Audience Syncing: Our users are always interested in faster syncing and fresher data. As warehouses become more real-time and adopt technologies like CDC, we’re excited to explore real-time audience computation and syncing.
• Generative AI: We’re continuously working on making the user experience of acting on data more seamless, and are optimistic generative AI can enhance user workflows.

We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $180,000 - $320,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window.

Interview Process

Our interview process focuses on evaluating fit for the most important dimensions of the role: product sense, ability to architect backend and distributed systems, and alignment with Hightouch’s values. Notably, we don’t do any programming interviews as we believe they are low signal to noise and aren’t a good evaluation mechanism.

• Recruiter Screen [30m]: Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit.
• System Design Screen [45m]: Designing a data processing feature end-to-end.
• Hiring Manager Interview [30m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.
• System Design Interview [90m]: Work with the interviewer to architect a system at a conceptual level. The problem will be at a pretty high level - and have both product and customer requirements as well as technical.

#LI-DNI

Hi, we're Oscar. We're hiring an Applied AI Engineer to join our AI team.

Oscar is the first health insurance company built around a full stack technology platform and a focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves—one that behaves like a doctor in the family.

About the role

Join Oscar’s AI team to build and launch transformative AI-powered products that redefine the healthcare experience for millions. As an Applied AI Engineer, you’ll take end-to-end ownership of high-impact projects—conceiving, prototyping, and deploying generative AI solutions that solve real-world problems across the healthcare ecosystem.You’ll work alongside a world-class team of engineers and data scientists, rapidly iterating on ideas and shipping production-ready applications that drive measurable business and user outcomes. You’ll have the autonomy to identify opportunities, experiment with the latest AI models and techniques, and deliver solutions that streamline operations, improve patient experiences, and move the needle for Oscar and our members.

You will report to the Sr. Director, AI.

Work Location: This position is based in our New York City office, requiring a hybrid work schedule with 3 days of in-office work per week. Thursdays are a required in-office day for team meetings and events, while your other two office days are flexible to suit your schedule. #LI-Hybrid

Pay Transparency: The base pay for this role is: $163,944 - $236,911 per year. You are also eligible for employee benefits, participation in Oscar’s unlimited vacation program, company equity grants, and annual performance bonuses.

Responsibilities

• Lead the design, development, and deployment of AI-driven products and features, from zero-to-one prototypes to robust production systems.
• Collaborate cross-functionally with product, design, and business teams to deeply understand user needs and deliver solutions that move key metrics.
• Rapidly experiment with new models, techniques, and tools (including LLMs, RAG, and agentic workflows), iterating based on data and user feedback.
• Build and scale full-stack applications and intelligent agents that automate complex healthcare workflows and deliver seamless user experiences.
• Stay on the cutting edge of the AI ecosystem, evaluating and integrating emerging technologies to keep Oscar at the forefront of healthcare innovation.
• Contribute to Oscar’s AI strategy and technical vision, helping shape the future of healthcare through technology.
• Other duties as assigned

Qualifications

• 7+ years of professional experience in software engineering, data science, or a related field, with a track record of impactful accomplishments
• Strong Python programming skills
• Experience solving complex, ambiguous problems 
• Experience designing and analyzing experiments or evaluations
• Experience or an interest in prototyping full-stack applications

Bonus Points

• Experience with the healthcare industry. Familiarity with claims or clinical data.
• Degree in math, statistics, data science, or engineering.
• Our ideal candidate is a data scientist with an engineering background, an engineer with a data science background, or a founder-type who combines product thinking, rapid prototyping, and hands-on coding to deliver real-world impact.
• Experience with NLP, neural networks, transformers, and modern AI frameworks.
• Knowledge of containerization and deployment tools (e.g., Docker, Kubernetes) or full-stack development (e.g., React, Golang).
• Experimental mindset: comfort with testing hypotheses and iterating on failures.

Realize your potential by joining the leading performance-driven advertising company and a proud Great Place to Work® Certified employer in the United States. 

As a Senior Human Resources Business Partner on the Global People Operations team in our New York City office, you will align with several business units across the globe to give our employees and leaders the tools and knowledge necessary for them to succeed at Taboola.

We’re looking for an experienced HR Business Partner to support select departments within our Global Sales & Partnerships organization. This role is strategic and hands-on—you’ll influence decisions early with business insight, help shape team effectiveness, and drive organizational capability. You’ll stay close to employees and managers, address issues proactively, guide leaders through complex situations, and build the HR practices and structures the organization needs to grow.

To thrive in this role, you’ll need: 

• 5+ years of experience as an HR Business Partner, with at least 1 year of experience partnering directly with Sales or GTM teams. Global experience is a plus.
• Strong background supporting heavily complex, matrixed organizations. 
• A data-driven mindset with proven experience using metrics to provide insights, inform decisions, and drive change. 
• Commercial mindset, with the ability to link people strategy directly to business needs. 
• Proven experience advising and constructively challenging leaders on organizational design, workforce planning, and leadership development. 
• Experience building trust with leaders and employees at all levels, using expert communication, coaching, influencing, and stakeholder management skills. 
• Ability to work effectively in ambiguity, while being hands-on, practical, and willing to build as you go with patience and persistence. 
• Track record of navigating complex employee relations situations with sound judgement and professionalism. 
• Curiosity about emerging workforce trends, including AI’s impact on roles and skills 
• Focus on creating an environment where employees feel supported, valued, and able to do their best work. 

How you’ll make an impact: 

• Serve as a strategic HR partner to leaders across selected global GTM groups, advising on people, talent, and organizational matters to support strong business decisions.
• Partner with managers to assess, manage, and improve employee performance, supporting them through complex people situations including reorganizations and employee relations matters.
• Provide managers with coaching, training, and resources to strengthen leadership capability and support effective team management.
• Stay close to employees and team dynamics, proactively identifying engagement risks, attrition concerns, or capability gaps. Act as a trusted advisor and mediator when needed, supporting informal or formal resolution of employee concerns.
• Use people data and metrics to identify trends, track HR impact, and provide clear, actionable recommendations to business leaders.
• Forecast current and future talent needs, helping develop strategies to attract, retain, and grow talent.
• Educate employees and managers on HR policies, processes, and employment practices to ensure clarity, consistency, and compliance.
• Monitor external trends, including labor market dynamics, competitor activity, and emerging technologies, to inform people and organization plans.
• Partner closely with regional HR leaders to ensure strong local HR support for the key global teams you support.
• Drive efficiency and process improvements through generative AI and automation.
• Reinforce a culture of trust, accountability, connection, and high performance across teams.

Why Taboola?

If you ask Taboolars what they love about working here, they’ll tell you that they’ve been empowered to realize their full potential while growing and learning from and with smart and talented people. They’ll also share more about:

• Adam Singolda, Taboola Founder and CEO says; “You can copy anything from another business but you can’t copy a company’s culture.
• Well-being: Enjoy comprehensive benefits (health, dental, vision, 401k matching), pre-tax commuter benefits, and a fully stocked kitchen.
• Flexibility: We offer a hybrid work schedule with 3 days in-office with an option to come in more often if desired.
• Work with some of the biggest names: Our publisher partners include Yahoo, Conde Nast, Fox Sports, NBCU, ESPN, CBS, and E! Online. Our advertiser clients include Wells Fargo, Honda, Pinterest, Expedia and Honda.

Ready to realize your potential?

Taboola is an equal opportunity employer and we value diversity in all forms. We are committed to creating an inclusive environment for all employees and believe such an environment is critical for success. Employment is decided on the basis of qualifications, merit, and business need.

Learn more about #TaboolaLife on LinkedIn, Facebook, Instagram, X, YouTube, & the Taboola Life Blog.

About Taboola

Taboola empowers businesses to grow through performance advertising technology that goes beyond search and social and delivers measurable outcomes at scale.

Taboola works with thousands of businesses who advertise directly on Realize, Taboola’s powerful ad platform, reaching approximately 600M daily active users across some of the best publishers in the world. Publishers like NBC News, Yahoo, and OEMs such as Samsung, Xiaomi and others use Taboola’s technology to grow audience and revenue, enabling Realize to offer unique data, specialized algorithms, and unmatched scale.

#LI-RT1

#LI-Hybrid

About Us:

We are an innovative AI startup focused on transforming professional services through cutting-edge Generative AI and deep domain expertise. Our agent-driven solutions automate complex workflows, engaging humans only when needed to maximize efficiency and accuracy. Join us at the forefront of AI innovation, where your expertise will directly shape the future of professional services.

Role Overview: 

We are hiring a tax product leader to build and train the next generation of tax AI products. This person should bring strong indirect-tax judgment and use that expertise to help our products perform accurately and effectively on real-world customer problems. The ideal candidate has a deep understanding of how sales and use tax works across multiple states, industries, and transaction types, and is excited to work hands-on with engineers, product teams, customers, and partners to train the product to perform at the highest level on real-world customer problems. 

This role is centered on applying domain expertise to train the product. The person in this role will research nuanced tax issues, test workflows and outputs, provide clear feedback to engineering, and lead customer and partner onboarding and implementation efforts. 

Key Responsibilities:

• Use deep sales and use tax expertise to train the product across a broad range of jurisdictions, industries, and transaction scenarios. 
• Research nuanced tax issues and apply that judgment to evaluate and strengthen how the product performs. 
• Partner closely with engineering and product teams to test outputs, identify gaps, and continuously improve product accuracy, consistency, and coverage. 
• Build and refine testing scenarios that push the product to perform at the highest level on complex customer problems. 
• Lead customer and partner onboarding and implementation efforts. 
• Own cross-functional workstreams and help prioritize improvements based on tax nuance, customer needs, and business impact. 
• Work hands-on with large data sets, tax scenarios, and product behavior to identify issues, surface patterns, and improve product performance. 
• Communicate clearly across tax, engineering, product, customers, and partners to align on priorities and drive product improvements. 

Qualifications

• 6–10 years of experience in indirect tax, with a strong focus on multi-state sales and use tax
• Experience beyond routine compliance, including exposure to substantive tax issues and operational tax considerations
• Background in Big 4, industry, or government (or similarly rigorous tax environment) preferred
• Experience working across multiple industries is a plus
• Proven experience interacting with tax authorities (e.g., audits, notices, refund claims, rulings, voluntary disclosures)

Skills & Capabilities

• Strong tax research skills and ability to analyze large datasets
• Comfortable testing systems/products and applying domain judgment to improve outputs
• Excellent communication skills, with the ability to work across technical and non-technical teams
• Experience managing or leading customer and partner relationships
• Technology-savvy with a willingness to work closely with product and engineering teams (no coding required)
• Willingness to travel occasionally for customer meetings and team collaboration

Benefits

• Competitive Compensation: Tailored to your experience and skill set.
• Flexible Work Arrangements: Hybrid working model for work-life balance.
• Full Benefits: Health, Dental, and Vision
• Career Growth: Opportunities for professional development and leadership roles.
• Innovative Culture: Work on transformative technologies and make an impact in the AI space.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Production Architecture & AIOps

• Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
• Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
• Design and maintain scalable software applications with integrated AI/ML capabilities.
• Develop software architecture and design patterns to ensure performance and scalability.
• Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
• Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
• Integrate AI/ML models into production environments and optimize for reliability and scalability.
• Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
• Conduct code reviews and provide technical guidance to junior developers.
• Stay current with advancements in software engineering and AI/ML technologies.
• Adhere to agile and lean software development best practices.
• Thoroughly document all developed models and processes according to relevant policies and standards.
• Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.

Cross-Functional Collaboration

• Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
• Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.

Scaling & Performance

• Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
• Optimize model serving using techniques like model compression, caching, and hardware acceleration.

Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).

Qualifications & Skills:

• 6+ years of experience
• Master’s or PhD in a relevant field (Computer Science, Software Engineering,  Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
• LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
• MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
• Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
• Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
• Strong understanding of AI/ML algorithms, application architecture, and design patterns.
• Excellent problem-solving, analytical, communication, and collaboration skills.

Preferred Qualifications & Skills

• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
• Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
• Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment.
• Excellent written and verbal communication skills.
• Strong listening and teaching abilities.
• Demonstrated analytical, troubleshooting, and problem-solving skills.
• Quick learner of new technologies.
• Self-directed with strong technology and communication skills.
• Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
• Collaborative team player with a practical and creative approach in a dynamic work environment.
• Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank is seeking a VP-level Applied AI & Machine Learning Scientist to design, build, and validate production-grade AI/ML and Generative AI solutions in a highly regulated banking environment. This role focuses on high-impact use cases—fraud detection, AML alert optimization, AI-assisted credit memo generation for underwriting decision support, contact center AI assistant/copilots, and personalization for treasury/commercial clients—delivered with rigorous governance, explainability, fairness testing, privacy-by-design, cybersecurity, and model lifecycle controls aligned to SR 11-7 and MCB’s Trustworthy & Responsible AI Principles. The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Applied AI/ML development:

• Design and implement models for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting and underwriting decision support, contact center AI assistants, and personalization for commercial/treasury use cases.
• Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.
• Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre-/post-deployment bias testing with documented remediation.

Model validation, documentation & governance (SR 11‑7):

• Produce audit-ready documentation (methodology, assumptions, data lineage, limitations, testing) and register models in the inventory with owners/materiality.
• Facilitate independent validation/effective challenge; obtain required approvals before deployment; maintain change management and periodic review cadence.
• Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.

Productionization & MLOps on Snowflake

• Package, deploy, and operate models via CI/CD, containerization, and model registry; instrument KPIs/KRIs and alerting dashboards. Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
• Partner with Engineering to integrate models via secure APIs/batch; ensure scalability, resiliency, and observability in cloud/on‑prem (e.g., Snowflake, Azure ML, Databricks).

Regulatory, privacy, and cybersecurity alignment:

• Design for ECOA/Reg B (adverse action specificity), UDAAP, FCRA, GLBA privacy, and NYDFS 23 NYCRR 500 cybersecurity requirements.
• Apply privacy-by-design (data minimization, purpose limitation, retention), strong access controls/segregation, and secure SDLC/red teaming for GenAI stacks.

Third‑party AI & data stewardship:

• Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
• Negotiate/verify contractual controls (no vendor training on MCB/NPI, subprocessors disclosure, audit rights, exit/portability).
• Ensure AEDT compliance (NYC Local Law 144) for any HR-related AI tools.

Cross‑functional partnership:

• Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.
• Communicate complex results, risks, and limitations clearly to technical and non‑technical stakeholders (management committees, examiners).

Innovation, coaching, and best practices:

• Evaluate emerging ML/GenAI methods, LLM evaluation techniques, Snowflake‑native capabilities (e.g., vector search, orchestration), and governance tooling; lead POCs within established control gates.
• Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.

Qualifications & Skills:

• 6+ years of relevant work experience.
• Expertise in Python (pandas, scikit‑learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• MLOps proficiency with CI/CD, containerization (Docker), registries, and observability; cloud ML (Snowflakes-native ML, Azure ML or Databricks preferred).
• Snowflake‑native ML proficiency: Snowpark Python, UDFs/UDTFs, Tasks/Streams; ability to build and operate ML workflows inside Snowflake.
• Data engineering competency (SQL, ETL/pipelines, Spark/PySpark); ability to work with structured/unstructured data.
• Explainability (e.g., SHAP) and fairness testing; ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
• Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
• Excellent communication; ability to translate technical detail to business/risk stakeholders and drive decisions.
• Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.

Preferred Qualifications & Skills

• Master’s or PhD in a relevant field (Computer Science, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML, NLP, or model interpretability.
• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions; experience with feature management/registry tooling a plus. model registry and pipeline orchestration; Kubernetes a plus.
• RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).
• Fairness toolkits and XAI frameworks; experience preparing models for validation, audit, or regulatory exam discussions.
• Familiarity with SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment
• Must have excellent written and verbal communication skills
• Must be a good listener and good teacher
• Demonstrate analytical, troubleshooting and problem-solving skills
• The ability to learn new technologies quickly
• Self-directed individual with technology and communication skills.
• Ability to take in multiple sources of information with an understanding of the bigger picture need, want, and operation of the Bank.
• Collaborative team-player who can find creative and practical solutions in a dynamic work environment.
• Ability to handle ambiguity, juggle multiple matters at once, and quickly and seamlessly shift from one situation or task to another.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking an Applied AI Analyst to support the design, configuration, testing, and monitoring of applied AI, Generative AI, machine learning, and agentic workflow solutions in a highly regulated banking environment. Working under the guidance of AI Scientists and in partnership with engineering, risk, and business teams, this role focuses on internal human-in-the-loop use cases such as AI-assisted loan documentation generation, fraudulent transaction detection models, policy and knowledge copilots, workflow automation, and other productivity solutions. A meaningful portion of the role is dedicated to AI solution intake and delivery support— learning new business domains, engaging business owners, preparing required documentation, and supporting vendor and risk reviews, so that solutions can be built and deployed under strict control gates and documentation standards aligned to internal and external regulations. The role emphasizes strong documentation, human oversight, privacy-by-design, cybersecurity, and disciplined lifecycle controls aligned to internal and external regulations. The role also emphasizes familiarity with Microsoft Foundry / Foundry Agent Service, approved Microsoft Copilot ecosystems, RAG architectures, and enterprise data platforms such as Snowflake.

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Applied AI solution development & analysis:

• Support AI Scientists in building, configuring, and testing applied AI/GenAI solutions for high-value banking use cases such as credit memo generation, policy/regulatory summarization, knowledge assistants, and internal productivity copilots.
• Develop and refine prompts, grounding instructions, structured outputs, evaluation datasets, and Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector search, document chunking, and metadata-driven retrieval.
• Prepare and transform structured and unstructured data; assist with API integrations, notebooks, and repeatable workflows that improve quality, traceability, and analyst productivity.

Agentic workflow design & orchestration:

• Assist in designing and testing agentic workflows using approved enterprise platforms and frameworks, including multi-agent patterns, human-in-the-loop approvals, handoffs, and subagent / agents-as-tools designs.
• Work with emerging interoperability patterns such as Model Context Protocol (MCP), tool / skill / plugin integration, and Agent-to-Agent (A2A) connectivity under supervision and within approved guardrails.
• Document workflow steps, tool schemas, fallback logic, escalation paths, and safe disablement / rollback procedures for agentic and GenAI solutions.

Microsoft Foundry, Copilot, and enterprise platform enablement:

• Support prototyping and evaluation in Microsoft Foundry / Foundry Agent Service and related Microsoft ecosystems (for example, Microsoft 365 Copilot) while partnering with Engineering and Data teams operating on Snowflake and other approved enterprise platforms.
• Assist with prompt and agent configuration, retrieval integration, logging, model / tool versioning, and operational runbooks.

Evaluation, testing, and monitoring:

• Create and execute test cases for accuracy, groundedness, hallucination, task completion, retrieval quality, bias/fairness, and basic adversarial or prompt-injection scenarios; escalate issues promptly.
• Track KPIs/KRIs, output quality, drift indicators, and user feedback; maintain evidence needed for pilot reviews, production monitoring, and periodic reassessment.

AI governance, intake documentation, and cybersecurity/privacy controls:

• Support AI use-case intake and governance processes by preparing required documentation (e.g., AI intake forms, model/system inventories, change-control artifacts, and audit-ready evidence packs) aligned to MCB’s Trustworthy & Responsible AI Principles and internal approval processes.
• Learn domain and process context for new use cases by speaking with business owners and control partners; translate requirements into clear problem statements, data needs, user journeys, control gates, and documentation required to comply with internal and external regulations.
• Support third-party / AI vendor due diligence by collecting and organizing evidence, reviewing vendor documentation, and helping risk-tier AI vendors and solutions in partnership with Third-Party Risk and Cyber/IT; act as an AI subject matter expert in providing an understanding of AI-specific risks.
• Apply privacy-by-design, data minimization, access-control, and secure prompt/data handling practices; follow approved tooling and build standards.

Cross-functional partnership and continuous learning:

• Communicate findings, limitations, and recommendations clearly to AI Scientists, managers, control partners (Model Risk, Compliance/Legal, Cyber/IT, Data Privacy), and business stakeholders; incorporate feedback quickly and accurately.
• Contribute to playbooks, standard operating procedures, and reusable templates for prompts, evaluations, agent patterns, and workflow controls.

Innovation and best practices:

• Stay current on practical applied-AI methods—including RAG, evaluation frameworks, agentic workflow design, MCP, A2A, and Microsoft Foundry capabilities—and recommend fit-for-purpose uses under established control gates.
• Demonstrate sound judgment, curiosity, and willingness to learn from senior AI Scientists while promoting responsible AI, reproducibility, and disciplined execution.

Qualifications & Skills:

• 2+ years of work experience.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, Information Systems, Mathematics, or a related field is preferred. Relevant internships, apprenticeships, or comparable hands-on AI, analytics, or automation project experience will be considered.
• Working knowledge of Python, SQL, notebooks, REST APIs, JSON/YAML, and version control (e.g., Git) for data analysis and AI workflow support.
• Understanding of traditional machine learning model development and lifecycle concepts, including model training, data leakage prevention, testing/validation, monitoring, and common performance metrics for classification and regression.
• Familiarity with LLM application patterns, including prompt engineering, structured outputs, function/tool calling, Retrieval-Augmented Generation (RAG), embeddings, and vector search.
• Familiarity with Microsoft Foundry (including Foundry Agent Service), Microsoft 365 Copilot / Copilot Studio concepts, and approved enterprise data/AI platforms such as Snowflake.
• Understanding of agentic workflow concepts such as MCP servers/tools, skills/plugins, subagents or agents-as-tools, handoffs, human-in-the-loop controls, and A2A integration patterns.
• Ability to test and evaluate AI outputs for accuracy, groundedness, hallucination, retrieval quality, bias/fairness, and basic security misuse scenarios.
• Working knowledge of model and data governance concepts, including documentation, monitoring, change control, inventories, audit trails, and three-lines-of-defense oversight in a regulated environment.
• Strong written and verbal communication skills; ability to turn technical findings into clear summaries, test evidence, and action items for business and risk stakeholders.
• Analytical, organized, and adaptable mindset; ability to learn quickly, manage multiple workstreams, and balance innovation with risk discipline.

Preferred Qualifications & Skills

• Financial services exposure in fraud, AML/KYC/CDD/EDD, underwriting, commercial banking, treasury, policy governance, or contact center analytics.
• Hands-on experience with Microsoft Foundry / Foundry Agent Service, Azure AI Search or equivalent retrieval tooling, M365 Copilot / Copilot Studio, Snowflake, or similar enterprise AI platforms is a plus.
• Experience with RAG architectures, vector databases / vector search, document chunking, metadata extraction, OCR/document understanding, and prompt engineering.
• Exposure to agentic workflow patterns such as MCP tool integration, skills/plugins, handoffs, subagents, A2A, and evaluation frameworks for task completion and tool-call quality.
• Familiarity with LangChain, Semantic Kernel, LangGraph, Microsoft Agent Framework, or similar orchestration libraries/frameworks is a plus.
• Awareness of external regulations including SR 11-7, SR 23-4, AI use-case intake / inventory processes, privacy, cybersecurity, and third-party risk expectations in a regulated environment.
• Ability to work in a constantly evolving environment.
• Must have excellent written and verbal communication skills.
• Demonstrate analytical, troubleshooting, and problem-solving skills.
• The ability to learn new technologies quickly.
• Self-directed individual with strong organizational and communication skills.
• Ability to synthesize multiple sources of information with an understanding of the bigger picture needs and operations of the Bank.
• Collaborative team player who can find practical solutions in a dynamic work environment.
• Ability to handle ambiguity, manage multiple tasks at once, and shift effectively between priorities.

Potential Salary: $120,000 - $150,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research, tools, and resources that serve all of our enterprise clients. As MLRE on the Data Foundation team, you’ll work on cutting edge research to define the data flywheel that makes the whole machine move. This includes research around synthetic environments from task definitions, building agents for trace analysis, and contributing to a cutting edge framework that automatically hill-climbs agent-building from an eval set. This will involve creating best-in-class Agents that achieve state of the art results through a combination of post-training + agent-building algorithms.

If you are excited about shaping the future of the modern GenAI movement, we would love to hear from you!

You will: 

• Build synthetic data pipelines to generate enterprise environments to use for RL post-training
• Create agents to convert traces from production into actionable insights to use to improve agents
• Contribute to our agent building product which can construct other agents using coding agents + proprietary algorithms
• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers. 

Ideally you’d have:

• 3+ years of building with LLMs in a production environment
• Clear experiences with constructing high quality data to use to improve an LLM/Agent
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field

Scale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. Our teams operate at the intersection of cutting-edge research, large-scale engineering, and real-world deployment, partnering with leading frontier labs, enterprises, and government agencies to push Generative AI into new capabilities and applications.

As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed.

As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

You will:

• Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  
• Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
• Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
• Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
• Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
• Stay deeply connected to the research community, understanding major trends, and helping set them.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.
• A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
• Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.

National Security Hackathon Attendees – Stay Connected with Scale AI

This posting is for candidates who attended National Security Hackathon and connected with a member of the Scale AI team.

It was great meeting you at the Hackathon! Whether we spoke at our booth or on the floor, we always enjoy connecting with people who are passionate about advancing AI and machine learning.

At Scale AI, our mission is to develop reliable AI systems for the world's most important decisions. For the past ten years, Scale has been the leading AI data foundry, supporting some of the most exciting advancements in AI, including generative AI, defense applications, robotics, and autonomous systems.

We’re continuing to grow our team across a range of technical and mission-focused roles.

If you're excited about the problems we’re tackling, feel free to share your information here. A member of our team will reach out if there’s a strong fit with one of our open opportunities. And even if the timing isn’t right today, we’d love to stay connected.

We look forward to continuing the conversation. In the meantime, you can learn more about our work at scale.com.

About Scale AI

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI) and building upon our prior model evaluation work with enterprise customers and governments to deepen our capabilities and offerings for public and private evaluations.

About Data Engine

Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we produce is some of the most critical work for how humanity will interact with AI.

About Our FDE Team

Generating high-quality data is the core problem our business solves. We aim to make producing and delivering high-quality data seamless and efficient for operators and customers. Our Team is building customer and operator-specific infrastructure to provide high-quality data with low turnaround time. You'll be exposed to the cutting edge of the Generative AI industry while directly interfacing with the leading model-building organizations in the space, including the top AI research labs and government agencies. 

Join us in shaping the future of Artificial General Intelligence. As a Forward Deployed Engineer, you'll be at the forefront of providing the critical data infrastructure that powers the most advanced AI models, directly influencing how humanity interacts with AI. You will work with the world’s leading AI companies and government agencies to solve their most complex AI data-related problems.

Responsibilities:

• Drive Impact: Directly contribute to the advancement of AI by delivering critical data solutions for leading AI innovators and government agencies.
• Customer Collaboration: Interact daily with our technical customers, understanding their unique challenges and translating them into impactful solutions.
• End-to-End Development: Design, build, and deploy features across the entire stack, from front-end interfaces to back-end systems and infrastructure.
• Rapid Experimentation: Deliver high-quality experiments quickly, iterating quickly to meet customer needs and drive innovation.
• Strategic Influence: Play a key role in shaping our engineering culture, values, and processes, contributing to the growth of our team and the evolution of our product.
• Diverse Projects: Engage in a dynamic mix of designing and deploying cutting-edge data solutions, collaborating with leading AI researchers, and directly influencing the product roadmap. You'll work on everything from large-scale system architecture to customer-facing front-end application design.
• Leadership Growth: This role offers a unique opportunity to lead critical projects, shape our engineering culture, and accelerate your career growth in the rapidly evolving field of Generative AI. You'll be positioned to become a future leader in a company defining the next era of technology.

Requirements:

• At least 2 years of relevant experience is preferred
• Proven track record of shipping high-quality products and features at scale.
• Strong problem-solving skills and the ability to work independently or as part of a collaborative team.
• Desire to thrive in a fast-paced, dynamic environment.
• Ability to turn business and product ideas into engineering solutions.
• Strong coding abilities and the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
• Ability to adapt quickly to the ever-changing world of generative AI.
• Excited to join a dynamic, hybrid team in either San Francisco or New York City.

Preferred Qualifications:

• Experience with large-scale data processing and distributed systems.
• Familiarity with machine learning and AI concepts.
• Experience working directly with enterprise customers.
• Experience with cloud-based infrastructure.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research, tools, and resources that serve all of our enterprise clients. As a Staff Agent Post-Training MLRE, you will build out our next-gen Agent RL training platform. You’ll build out the platform that will train best-in-class Agents that achieve state of the art results on real enterprise use-cases. 

You’ll integrate cutting edge research into our training stack, enabling MLREs on the Enterprise AI team to deploy use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models. If you are excited about shaping the future of the modern GenAI movement, we would love to hear from you!

You will: 

• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers. 
• Research cutting edge algorithms to integrate directly into our training stack.
• Design solutions that enable complex multi-agent systems to directly learn from both process + outcome based rewards.

Ideally you’d have:

• 5+ years of LLM training in a production environment
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field

Scale Labs, Research Scientist — Frontier Risk Evaluations

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: 

• Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities;
• Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems;
• Publish evaluation methodologies and write technical reports for policymakers.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience in crafting evaluations and benchmarks, or a background in data science roles related to LLM technologies.
• Experience with red-teaming or adversarial testing of AI systems.
• Familiarity with AI safety policy frameworks (e.g., NIST AI RMF, EU AI Act, Korea AI Basic Act).

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

Scale AI is at the forefront of the AI revolution, helping the U.S. government unlock the potential of AI across national security missions. We’re building enterprise-grade generative AI solutions and delivering them into operational use cases that matter.

The Global Forward Deployments (GFD) organization operates as a critical horizontal layer across Scale, sitting at the intersection of Engineering, Product, Design (EPD), Delivery, and GTM. As our business scales, we are growing the GFD Project Management Office (PMO) to act as the connective tissue that brings structure to ambiguity by aligning teams, organizing cross-functional workstreams, and keeping priorities coordinated across the organization.

As a Technical Program Manager within this expanding PMO, you will drive execution across diverse initiatives by partnering closely with Infrastructure & Security, Product, Software Engineering, Machine Learning, Mission/Field Engineering, Delivery, and Go-To-Market (GTM) teams. Your focus will be integrating engineering roadmaps, customer delivery timelines, and security constraints to enable successful deployments in complex, isolated environments.

What You Will Do

• Lead as a Horizontal PMO Partner: Serve as a central node within the growing GFD PMO, driving alignment across Engineering, Product, and Design (EPD) initiatives and ensuring priorities remain top-of-mind across the organization.
• Spearhead Niche Initiatives: Take on Scale’s difficult-but-ambiguous problems, breaking them down in a structured way and driving them to successful outcomes. You will dive into both technology and organizational processes to craft effective solutions and gain efficiencies, track resourcing and vendor management, and align internal resources to solve unique organizational, and technical challenges. 
• Bridge EPD, Delivery, and GTM: Act as the primary technical program manager uniting GFD with Product, ML, Mission & Field Engineering, Delivery, and GTM teams to wrangle contractual deliverables across EPD, unblock complex custom integrations, and drive technical milestones against critical contract checkpoints.
• Optimize Cross-Functional Processes: Collaborate with stakeholders across the company to improve processes and procedures, ensuring Scale's continued success.
• Foster Communication: Create an effective feedback loop between all teams within EPD as well as with partner teams like Legal, BizOps, and Partnerships.
• Navigate Ambiguity: Transition AI/ML technologies and processes into working Public Sector products/solutions even when requirements are undefined or ambiguous.

Ideally You’d Have

• Current or past Security Clearance (TS/SCI preferred).
• Proven experience partnering closely with Product and cross-functional Engineering teams to align technical roadmaps, manage dependencies, and drive complex, multi-team initiatives.
• Experience in project management, including planning, organizing, and managing resources.
• A demonstrated capacity to operate effectively and maintain high-quality output within a high-tempo, ambiguous, or chaotic environment.
• Experience in security and compliance, with a focus on cross-departmental collaboration.
• Experience in delivering secure and reliable solutions within classified, isolated, and air-gapped environments.
• Demonstrated understanding of security practices within cloud environments (e.g., AWS, Azure, GCP).
• Experience in a high growth, fast-paced technology company.
• An action-oriented mindset, balancing creative problem-solving with a strong drive to achieve outcomes.

Nice to Haves

• Experience in microservice design and Kubernetes-based container orchestration.
• Experience applying AI or Machine Learning.
• Strong analytical skills, with experience using data analysis tools or software.
• Demonstrated history of fostering cross-functional collaboration with Delivery, Sales, and Engineering teams.
• Backlog refinement & roadmapping experience with tools like Linear and JIRA.
• Proven experience translating complex SOW requirements into tactical engineering deliverables 
• Experience maintaining alignment with organizational capacity planning and delivery timelines.
• Background in Software, Security, or System Engineering.

(Note this role must be based within the continental United States and requires current US Citizenship to support Scale’s Public Sector business.)

Scale is a vital part of bringing AI-enabled technologies to the world, from autonomous driving to drones, robots, and large language models. For example, Scale works with the world's top self-driving car and robotics ML teams as well as the largest companies in the generative AI space. 

As our customer base is growing, you will be on the front lines of our field engineering efforts for our federal AI projects, having the opportunity to meaningfully impact millions of dollars in revenue by working closely with our largest public sector customers and ensuring that they become passionate, lifelong Scale customers.

Our Field Engineers ensure customers' experiences with Scale's technology are seamless and optimized to build a successful, long-term partnership. Field Engineers partner closely with Product, Engineering, and Operations to lead existing customers through building production-grade data pipelines. We do this by knowing our customer’s use-cases inside and out, and creating the tooling and documentation needed to scalably support their growth.

Field Engineers in general are experts on Scale's products and codebase, and have general technical knowledge on web development, using APIs, and working with large volumes of data. Field Engineers are strong relationship builders, great project managers, and always fun to work with. 

The ideal person is a software engineer generalist who enjoys working with clients directly, taking ambiguous requirements and issues, and quickly designing and building sustainable solutions in close collaboration with the Product and Engineering teams. This role is at the intersection of customer delivery, product, and engineering, giving you a breadth of experience and fully leveraging both sides of your brain. If this sounds like you, we invite you to apply!

You will:

• Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back
• Work closely with our customer’s engineering teams to optimize data pipelines, including providing input towards designing API endpoints and input/output schemas
• Design, develop and maintain playbooks, internal tools, Scale’s documentation and SDKs to quickly get customers set up for long-term success
• Partner with Software Engineers and Operations to remove any technical hurdles customers may face / Debug technical issues impacting delivery and own technical escalations coming from the customer
• Be accountable for the customer’s technical experience throughout their time with Scale
• Work with AEs and PMs to identify customer-specific feature requests
• Develop technical domain expertise in areas of Large Language Models, 2D Image Annotation, and working with API integrations
• Evangelize Scale and interact with customers at major events
• Have an active TS/SCI clearance

Ideally you'd have:

• Track record of success as a hybrid customer-facing engineer or similar function, wearing multiple hats along the way
• Prior technical hands-on experience working with clients in a pre or post-sales capacity to realize business goals
• Prior experience developing with Python and JavaScript, or other modern software languages
• Strong engineering background preferably in Computer Science, Mathematics, or other quantitative fields
• Strong project management and interpersonal skills
• Presentation skills with a high degree of technical credibility when speaking with executives and front-line engineers
• High level of comfort communicating effectively across internal and external organizations
• Intellectual curiosity, empathy, and ability to operate with high velocity

Nice to haves:

• Experience in AI
• Experience working in classified environments
• Previous experience as a technical go-to-market resource
• Understanding of DevSecOps principles, including secure software delivery, infrastructure as code, and continuous integration/deployment
• Hands-on knowledge of container orchestration with Kubernetes and experience with Helm charts for application deployment
• Experience automating infrastructure and configuration management using Ansible or similar tools

As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities.

Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As a Staff Machine Learning Research Scientist on the LLM Evals team, you will lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier LLMs. You will help define what "good" looks like in generative AI, driving research that informs both our internal roadmap and the broader research community. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs.

You will:

• Drive research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. 
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.
• Mentor and guide research scientists and engineers, providing technical leadership across cross-functional projects.
• Stay deeply engaged with the ML research community, tracking emerging work and contributing to the advancement of LLM evaluation science.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience tech leading a team of research scientists and research engineers
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.

At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

At the foundation of these products is the Platform Engineering team. In this role, you will lead the design and development of core data storage, streaming, caching, and indexing platforms and underlying systems.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.

You will:

• Drive the architecture, design, implementation, and reliability of our foundational data platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements.
• Collaborate with cross-functional teams to define, design, and deliver new features.
• Proactively identify opportunities for, and driving improvements to, current programming practices, including process enhancements and tool upgrades.
• Present technical information to teams and stakeholders, providing guidance and insight on development processes and technologies.
• Provide technical leadership, including: upholding and upleveling engineering standards across the organization, mentoring junior engineers. 

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialties in back-end systems, specifically related to building large-scale data storage, streaming, and warehousing systems.
• Extensive experience in various database technologies (MongoDB, Postgres), streaming/processing solutions (Kinesis, Flink, Spark), indexing/caching (ElasticSearch, Redis), and various data query engines (Trino, Presto, Snowflake, etc.).
• Show a track record of mentoring and leading teams in successful projects.
• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes and various public cloud offerings.
• Extensive experience in software development and a deep understanding of distributed systems, cloud platforms and data systems.
• Experience driving cross functional collaboration and communication at an organizational or broader level. 

Nice to haves:

• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI).
• Experience with performance tuning and cost optimizations of cloud based data platforms. 
• Experience defining a data lifecycle strategy and designing/implementing tooling for data privacy (i.e. GDPR) needs.  
• Experience scaling products at hyper-growth startups.
• Excitement to work with AI technologies.

Scale is looking for a Quality Lead to ensure top-tier data quality across our Generative AI programs. In this role, you will partner directly with Scale’s Delivery teams to ensure that the Quality Team’s core operation runs smoothly and that only high-quality data is delivered to our customers.

You will:

• Lead and develop teams of quality control auditors.
• Partner with delivery teams to interpret and improve customer requirements.
• Oversee day-to-day operations, including workload allocation and reporting insights.
• Establish and uphold scalable best practices.
• Drive initiatives that improve quality, efficiency, and customer satisfaction.

Ideally you'd have:

• A Bachelor’s degree in Computer Science, Engineering, Operations, or a related field.
• 2+ years improving operational/technical processes.
• Strong analytical and problem-solving skills (particularly in SQL and Google Sheets / Excel) with attention to detail.
• Experience building systems and processes
• A hands-on background in product management, software development, or top-tier management consulting.
• Proven experience taking initiative in cross-functional technical environments.

Nice to haves:

• Master’s degree in Computer Science, Engineering, Operations, or a related field.
• A hands-on background in quality assurance
• Experience in AI/ML services.
• Experience in a fast-growing, less-structured startup environment.
• Experience building teams from the ground up.

Scale AI is seeking a highly technical and strategic Staff / Senior Staff Machine Learning Engineer to act as the Tech Lead (TL) for our next generation of deep research agents for the Enterprise. This high-impact role will drive the technical direction and oversight for Deep Research Agent Development, translating cutting-edge research in Generative AI, Large Language Models (LLMs), and Agentic Frameworks into robust, scalable, and high-impact production systems that enhance enterprise operations, analytics, and core efficiency.

The ideal candidate thrives in a fast-paced environment, has a passion for both deep technical work and mentoring, and is capable of setting a long-term technical strategy for a critical domain while maintaining a strong, hands-on delivery focus.

Responsibilities

Technical Leadership & Vision

• Set the Technical Roadmap: Define and own the technical strategy, architecture, and roadmap for Deep Research Agents for the Enterprise, ensuring alignment with Scale AI’s overall AI strategy and business goals.
• Drive Breakthrough Research to Production: Lead the end-to-end development, from initial research to production deployment, to landing on customer impact, with a focus on integrating diverse data modalities.
• Core Agent Capabilities Development:
• Advanced Knowledge Retrieval: Architect and implement state-of-the-art retrieval systems to ensure the agents provide accurate and comprehensive answers from public and proprietary data sources from enterprises.
• Data analysis: Design and champion the development of data analysis agents that accurately translate complex natural language queries into executable SQL/code against diverse enterprise data schemas.
• Multimodal Intelligence: Lead the integration of Multimodal AI capabilities to process and extract structured information from visual documents, tables, and forms, enriching the agent's knowledge base.
• Architecture & Design: Design and champion highly scalable, reliable, and low-latency infrastructure and frameworks for building, orchestrating, and evaluating multi-agent systems at enterprise scale.
• Technical Excellence: Serve as the technical authority for the team, leading design reviews, defining ML engineering best practices, and ensuring code quality, security, and operational excellence for all agent systems.

Team Leadership & Mentorship

• Lead and Mentor: Technically lead and mentor a team of Machine Learning Engineers and Research Scientists, fostering a culture of innovation, rigorous engineering, rapid iteration, and technical depth.
• Recruiting & Growth: Partner with management to hire, onboard, and grow top-tier talent, helping to shape the long-term structure and capabilities of the team.
• Cross-Functional Influence: Collaborate effectively with Product Managers, Data Scientists, and other engineering/science teams to translate ambiguous, high-level business problems into concrete, executable technical specifications and impactful agent solutions.

Basic Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, a related field, or equivalent practical experience.
• 8+ years of experience in software development, with at least 6 years focused on Machine Learning, Deep Learning, or Applied Research in a production environment.
• 2+ years of experience in a formal or informal Technical Leadership role (Team Lead, Tech Lead) with a focus on setting technical direction for a domain.
• Deep expertise in Generative AI and Large Language Models (LLMs).
• Demonstrated experience designing, building, and deploying AI Agents or complex Agentic systems in production at scale.
• Experience with large-scale distributed systems and real-time data processing.

Preferred Qualifications

• Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, or a related quantitative field.
• Demonstrated experience designing and deploying production-grade Text-to-SQL systems, including handling complex schema linking and query optimization.
• Practical experience with Multimodal AI, specifically integrating OCR and vision-language models for document intelligence and structured data extraction from images/forms.
• Proven experience in one or more relevant deep research areas: Reinforcement Learning (RL), Reasoning and Planning, Agentic Systems.
• Experience with vector databases and advanced retrieval techniques.
• A track record of publishing research papers in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR, KDD).
• Excellent written and verbal communication skills, with the ability to articulate complex technical vision to executive stakeholders and technical peers.
• Experience driving cross-team technical initiatives that have delivered significant business impact.

About Scale AI

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

Role Overview

As a Forward Deployed AI Engineering Manager on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Key Responsibilities

Customer Integration & Deployment

• Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• Deploy and configure AI models and agents within customer security and compliance boundaries

AI Agent Development

• Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• Architect multi-agent systems that orchestrate between different models, tools, and data sources
• Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement

Prompt Engineering & Optimization

• Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• Build and maintain prompt libraries, templates, and best practices for customer use cases
• Conduct systematic prompt experimentation and A/B testing to improve model outputs
• Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate

Leadership & Collaboration

• Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts
• Lead a team that is collaborating with customer data scientists, ML engineers, and software developers to ensure smooth integration
• Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• Document technical architectures, integration patterns, and best practices

Problem Solving & Innovation

• Debug complex technical issues across the entire stack, from data pipelines to model outputs
• Rapidly prototype solutions to unblock customers and prove out new use cases
• Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• Identify opportunities for productization based on common customer patterns

Required Qualifications

• 5+ years of software engineering experience with 2+ yrs of  Management experience with strong fundamentals in data structures, algorithms, and system design
• Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
• Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences

Preferred Qualifications

Agent Development Wiz

• Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
• Experience building and deploying AI agents or autonomous systems in production
• Knowledge of vector databases and semantic search systems
• Contributions to open-source AI/ML projects

Infrastructure Guru

• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
• Previous work in a devops, platform, or infra role
• Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)

Customer Product Whisperer

• Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
• Domain expertise in verticals like finance, healthcare, government, or manufacturing
• Experience with technical enablement or teaching programs

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

Deep Research for Due Diligence

For a global professional services firm, we developed a sophisticated deep research agent to assist in due diligence. This agent employs a multi-agent architecture for robust fact-checking, integrates several internal MCP tools, and processes complex, unstructured data sources. This solution reliably saves employees hundreds of hours weekly.

Churn Prediction

Working with a TelCo organization, we built a model utilizing customer data to predict churn likelihood. The system then curates personalized offers based on this prediction. This model was integrated into a "next best action" copilot, enabling call center agents to proactively surface relevant offers to customers, leading to a significant reduction in churn.

Data Extraction Voice Agent

We partnered with a healthcare organization to create a lifelike voice agent and avatar designed to gather unstructured health information from patients. Engineered for low latency, the agent adeptly manages conversational flow, adheres to safety guardrails, and efficiently handles data extraction. This automation saves the organization's nurses hundreds of hours each week.

Role:

Scale is at the forefront of the AI revolution, working across the US government, partners and allies around the world to unlock the potential of generative AI (GenAI).

We are seeking a product leader to join our team and play a pivotal role in building Agentic AI platforms to support national-level decisions, including for some of the nation’s most important national security challenges. The ideal candidate will have a strong understanding of product leadership, software engineering principles practices and deep experience with ML/AI application development, coupled with proven experience in managing complex projects with multiple stakeholder or AI-related projects within a government or highly regulated setting, emphasizing ethical AI deployment and robust risk management practices. 

This role requires a strategic leader adept at navigating the complexities of government GenAI projects, ensuring Scale’s public sector AI solution aligns with agency objectives and adheres to stringent security and compliance mandates. The product manager will be responsible for the entire lifecycle of the generative AI platform, including product design, cross-program execution, capability prioritization, stakeholder engagement with various government entities, defining and managing engineering scope, developing detailed project plans, and overseeing resource allocation and budget management. A key focus will be on ensuring that Scale’s public sector AI solution operates securely within controlled network environments, and is configured properly to support government workflows, specifically those that relate to national defense.

Some examples of GenAI applications we build are:

• Agentic warfare and scenario planning
• Indications and warnings integration for the protection of critical continental level assets
• Deep research capability that can help evaluate thousands of pages of classified information 
• Report generation for multiple customized report templates 
• Text2SQL intelligence applications to make analysts more efficient and embed a culture of data-driven decision-making

You will:

• Develop enterprise grade solutions that leverage cutting edge AI and AI agents to drive value for public sector customers 
• Work with executives at Scale and our customers to determine and execute the product strategy of the business.
• Own end-to-end product development by understanding customer pain points, defining product requirements, managing development, testing, and launches
• Lead cross-functional teams including engineering, product design, operations, marketing, go-to-market and finance.
• Develop a point of view and execute on turning the solutions we build into scalable software that we can commercialize across the industry 
• Maintain a Top Secret security clearance 

Ideally you'd have:

• Technical degree in computer science, engineering, or equivalent experience
• 4+ years of experience in building ML-powered and / or enterprise-facing products
• Strong understanding of generative AI technologies and their applications in public or large-scale private sector settings
• Experience operating in a fast-paced environment with high ambiguity
• Exceptional leadership, presentation, and communication skills with the ability to influence cross-functional teams
• Data literacy and experience with data analytics
• Prior military or government experience 
• Coding experience (e.g. Python)

Nice to haves:

• Experience building infrastructure and tooling to develop and support agentic applications.
• Experience working in startup environments building solutions for public sector / federal customers.
• Understanding of public / federal networks, infrastructure, and deployment constraints.
• TS/SCI Security Clearance

Scale GP (Scale Generative AI Platform) is an enterprise-grade AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our core infrastructure in a fast-paced environment. 

The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will implement solutions across multiple cloud providers (GCP, Azure, AWS) for customers in diverse, highly-regulated industries like healthcare, telecom, finance, and retail.

What You’ll Do:

• Architect multi-cloud systems and abstractions to allow the SGP platform to run on top of existing Cloud providers
• Implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Collaborate with platform, product teams and our customers directly to develop and implement innovative infrastructure that scales to meet evolving needs.
• Deliver experiments at a high velocity and level of quality to engage our customers
• Work across the entire product lifecycle from conceptualization through production
• Be able, and willing, to multi-task and learn new technologies quickly

What We’re Looking For:

• 4+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)
• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the usage of frontier data and models by building complex agents for enterprises around the world through our Scale Generative Platform (SGP).

The SGP ML team works on the front lines of this AI revolution. We interface directly with clients to build cutting edge products using the arsenal of proprietary research and resources developed at Scale. As an Applied AI Engineer, you’ll work with clients to create ML solutions to satisfy their business needs. Your work will range from building next-generation AI cybersecurity firewalls to creating transformative AI experiences in journalism to applying foundation genomic models making predictions about life-saving drug proteins. Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you’ll use to improve your product’s performance. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

You will:

• Own, plan, and optimize the AI behind our Enterprise customer’s deepest technical problems
• Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
• Have experience gathering business requirements and translating them into technical solutions 
• Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
• Push production code in multiple development environments, writing and debugging code directly in both our customer’s and Scale’s codebases.
• Be able and willing to multi-task and learn new technologies quickly

Ideally you'd have:

• 7+ years of full-time engineering experience, post-graduation
• A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client’s business goals
• Strong engineering background: a Bachelor’s degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background.
• Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
• Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)

Nice to haves:

• Strong knowledge of software engineering best practices
• Have built applications taking advantage of Generative AI in real, production use cases
• Familiarity with state of the art LLMs and their strengths/weaknesses

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About Data Engine

Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

Our Approach 

As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale.

Responsibilities:

• Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and infrastructure layers
• Implement high-impact features using modern technologies such as TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal
• Collaborate closely with internal operators (your users are your neighbors) to identify bottlenecks and ship fast, pragmatic solutions
• Own core systems critical to our contributor platform, with direct impact on Scale’s GenAI data pipeline and business outcomes
• Architect and scale infrastructure capable of handling millions of tasks per week with high reliability and low latency
• Partner cross-functionally with ML teams, Forward Deployed Engineers, and Product to ensure data quality and operational excellence
• Contribute to a strong engineering culture while setting best practices for teammates through mentorship, code reviews, and process improvements

Requirements:

• 5+ years of software engineering experience, ideally in high-growth, product-focused environments
• Proven track record of shipping production systems at scale
• Drive reliability and performance across critical infrastructure systems, ensuring our platforms scale predictably and operate with high availability.
• Strong technical depth in one or more areas: front-end frameworks, distributed systems, data infrastructure, or developer tooling
• Experience working across the stack, ideally with React, TypeScript, Node.js, Python, MongoDB, Elasticsearch, and/or Temporal
• Strong product sense and ability to translate ambiguous problems into technical solutions
• Comfortable working in a fast-paced, high-ownership environment with a bias toward execution
• Excited to join a dynamic hybrid team based in San Francisco or New York City

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

About the General Agents Team

The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.

About the Role

As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments.

You will:

• Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
• Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
• Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
• Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
• Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
• Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
• Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.

Ideally you’d have:

• 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
• Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience building systems that integrate models with external tools, APIs, databases, and services.
• Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional environments.

Nice-to-haves:

• Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
• Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
• Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
• Experience deploying ML systems in cloud environments and operating them at scale.
• Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
• Interest in shaping the future of general-purpose enterprise agents and their real-world impact.

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About Data Engine

Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

About our AgentOps team: 

The vision for the AgentOps team is to build the best Agent Development Platform in the AI Industry.

Agent Development is in its nascent stages in a rapidly changing industry, with limited tooling making it hard for agent developers to manage agent lifecycles. As a team that has a front-row seat to what Enterprise customers need and want, we want to build an opinionated but flexible platform for all Agent operations ("AgentOps"), including building, deploying, monitoring, evaluating and improving agents to solve customer needs.

With the AI industry currently moving towards RL workflows that need verifiable rewards, we further want to gear this platform towards knowledge capture that create compounding effects to make Agents more effective and capable. We want to create a virtuous data flywheel where agents built using this platform see continuous performance improvements and increase customer value over time.

We are a rapidly growing team with ambitious goals, and are looking for motivated individuals to join us to make an impact in a green-field domain.

Responsibilities:

• Manage the engineering team and drive technical delivery
• Comfortable working cross functionally, whether that be internal or external customers
• Work across the entire product lifecycle from conceptualization through production
• Build features end-to-end: back-end, system design, debugging and testing
• Deliver experiments at a high velocity and level of quality to engage our customers
• Influence the culture, values, and processes of a growing engineering team
• Inspire and mentor engineers
• Collaborating with cross-functional teams to define, design, and ship new product features and experiences

Requirements:

• At least 5 years of relevant experience and at least 2 years of experience managing engineers is preferred
• Proven success in leading, managing, and developing high-performing Engineering teams
• Expertise in identifying product engagement patterns and trends for large scale consumer products
• Track record of shipping high-quality products and features at scale
• Desire to work in a very fast-paced environment
• Ability to turn business and product ideas into engineering solutions
• Excellent problem-solving skills, and be able to work independently or as part of a team.
• Excited to join a dynamic, hybrid team in either San Francisco or New York City.

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability.

You will:

• Own large new areas within our product
• Work across backend, frontend, and interacting with LLMs and ML models
• Deliver experiments at a high velocity and level of quality to engage our customers
• Work across the entire product lifecycle from conceptualization through production
• Be able, and willing, to multi-task and learn new technologies quickly

Ideally you'd have:

• 4+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About the ACE team 

The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. 

About This Role

This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Ideally you’d have: 

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.
• Strong written and verbal communication skills and the ability to operate cross-functionally. 

Nice to have:

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax. 
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH 
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and  unique access to massive datasets to deliver improvements to our customers.

Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.

You will:

• Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
• Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
• Work with massive datasets to develop both generic models as well as fine tune models for specific products
• Build scalable machine learning infrastructure to automate and optimize our ML services
• Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
• Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
• Comfortable with light travel (approximately 10%) for customer interaction and team needs
• This role will require an active security clearance or the ability to obtain a security clearance

Ideally You’d Have:

• Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
• Solid background in algorithms, data structures, and object-oriented programming
• Strong programing skills in Python, experience in Tensorflow or PyTorch

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
• Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
• Experience with computer vision, generative AI models, large language models, or agentic systems
• Familiarity with ML evaluation frameworks and agentic model design

Scale Labs, Research Scientist — Agent Robustness

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist working on Agent Robustness you will work on the fundamental challenges of building AI agents that are safe and aligned with humans. For example, you might: 

• Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them;
• Design and build harnesses to test AI agents’ tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment;
• Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use;
• Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and leveraging agent scaffolding, designing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools.
• Experience with red-teaming, prompt injection, or adversarial testing of AI systems.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

Staff Software Engineer, Full-Stack - Enterprise Gen AI

Scale GP (Scale Generative AI Platform) is an enterprise-grade AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a frontend-focused full-stack engineer to help build AI-powered applications that redefine enterprise workflows and push the boundaries of interactive AI. This role is ideal for someone who thrives in a fast-paced environment, enjoys working on a diverse set of projects, and has a passion for crafting high-quality, intuitive user experiences.

At Scale, you'll work on a mix of cutting-edge customer-facing AI applications and internal SaaS products. Our engineering team powers projects like TIME’s Person of the Year AI experience (see it in action), where our AI technology helped shape one of the most iconic features in media. You'll also contribute to Scale’s GenAI Platform (SGP), a powerful system that enables businesses to build and deploy AI agents at scale. Whether it’s developing interactive AI assistants, enterprise-grade web applications, or refining our core SaaS platform, you’ll play a crucial role in shaping how AI integrates into real-world applications.

You Will:

• Build and enhance user-facing AI applications for major enterprise customers, including high-profile media and Fortune 500 companies
• Develop and refine features for Scale’s GenAI Platform, empowering businesses to build, deploy, and manage AI-driven agents
• Design, build, and optimize polished, high-performance UIs using Next.js, React, TypeScript, and Tailwind
• Work closely with product managers, designers, and AI/ML teams to create seamless, intuitive, and impactful user experiences
• Integrate frontend applications with backend services, working with APIs, authentication systems, and cloud-based infrastructure
• Ship features at a rapid pace while maintaining a high level of code quality, performance, and accessibility

Ideally, You Have:

• 5+ years of experience developing frontend or fullstack applications in a modern tech stack
• Strong proficiency in Next.js, React, TypeScript, and Tailwind, with an eye for building polished, user-friendly interfaces
• Experience working on high-visibility, customer-facing applications and making trade-offs between speed and quality in fast-paced environments
• A passion for AI and experience working on interactive AI applications, agent-based systems, or data-rich web platforms
• Familiarity with backend technologies such as FastAPI, PostgreSQL, GraphQL, and cloud infrastructure like AWS, Azure, or GCP
• A track record of collaborating cross-functionally with design, product, and ML teams to bring AI-powered applications to life

This role is a unique opportunity to shape the future of AI-powered user experiences, working on projects that impact millions of users while developing tools that empower businesses to deploy AI at scale. If you’re excited by the intersection of AI, frontend engineering, and product design, we’d love to hear from you.

The base salary range for this full-time position in our hub locations of San Francisco, New York, or Seattle is $248,400—$310,500 USD

. Compensation packages at Scale include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Scale employees are also granted Stock Options that are awarded upon board of director approval. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Scale Labs, Research Scientist — AI Controls and Monitoring

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments. For example, you might:

• Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs;
• Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected;
• Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps;
• Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable designing control and monitoring experiments for AI systems, building prototype systems, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience with runtime monitoring, anomaly detection, or observability for ML systems.
• Familiarity with AI control or alignment research (e.g., scalable oversight, interpretability, debate).
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

The Enterprise ML team works on the front lines of the AI revolution, partnering deeply with customers to identify high-impact business problems and build cutting-edge AI systems using Scale’s proprietary research, data, and infrastructure—unlocking domain expertise through high-quality data and expert feedback.

As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.

What You’ll Do

• Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  
• Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
• Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
• Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
• Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
• Stay deeply connected to the research community, understanding major trends, and helping set them.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

What We’re Looking For

Core Qualifications

• 8+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.
• A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
• Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.
• Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners

Nice to Have

• Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments
• Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale
• Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes

Job Description: Distinguished Engineer, Enterprise AI 

About Scale AI

Our mission is to develop reliable AI systems for the world's most important decisions. The Enterprise AI business delivers performant, reliable, and production-grade AI applications to enterprises across all industries by providing Generative AI expertise and infrastructure.

The Role

We’re seeking a Distinguished Engineer - a hands-on technical leader operating at the VP level - to shape the vision and technical roadmap of our core AI/ML infrastructure that powers enterprise AI applications. Reporting directly to the SVP of Engineering, Enterprise AI, this individual will drive long-term technical direction for our Scale GenerativeAI Platform (SGP), influence architectural decisions across the company, and partner closely with engineering and product leaders to bring advanced AI capabilities to enterprise customers.

You’ll serve as a cross-organizational thought leader - setting standards for technical excellence, mentoring senior engineers, and ensuring our systems and models meet the demands of global-scale deployment. This is a rare opportunity to influence both foundational AI infrastructure and the enterprise AI applications built on top of it.

Responsibilities

• Define and drive the technical strategy for Scale’s AI/ML infrastructure and SGP platform, balancing short and long-term investments.
• Partner with senior engineering and product leadership to ensure scalable, secure, and performant enterprise AI systems.
• Lead architecture and design reviews across multiple teams, ensuring technical consistency and innovation.
• Serve as a trusted advisor and mentor to principal engineers and technical leads across the organization.
• Evaluate and integrate emerging technologies in AI, distributed systems, and data infrastructure to keep Scale at the frontier of innovation.
• Represent Scale externally in the AI community - through speaking engagements, partnerships, and thought leadership.
• Drive technical execution and accountability for critical cross-functional initiatives that advance Scale’s enterprise AI capabilities.

Qualifications

• 15+ years of experience as a technical software engineering leader.
• Proven record of technical leadership at AI-native companies, hyperscalers, or equivalent high-scale environments.
• Deep technical expertise in AI/ML infrastructure, knowledge of ML models/algorithm design/implementation and their application to real-world problems; experience with GenAI preferred.
• Demonstrated success in setting technical vision and leading cross-organizational initiatives with measurable business impact.
• Experience influencing and mentoring engineering teams in complex, matrixed environments.
• Ability to communicate and collaborate effectively to create a shared sense of vision or purpose cross-team and cross-functionality.
• Advanced degree in Computer Science, Engineering, or related field preferred but not required.

Culture & Impact

At Scale, we believe that AI should amplify human potential - and our engineering culture reflects that belief. Our teams operate at the intersection of innovation, rigor, and impact, solving some of the hardest problems in AI infrastructure and deployment.

The Distinguished Engineer will play a key role in shaping how AI systems are built, deployed, and governed within enterprise environments. This role represents the highest bar of technical excellence at Scale - a trusted voice in setting direction, enabling innovation, and ensuring that our technology scales responsibly and effectively to meet the evolving needs of our customers.

You’ll have the opportunity to influence company-wide strategy, contribute to industry-leading work in generative AI infrastructure, and mentor the next generation of engineering talent pushing the boundaries of what’s possible with AI.