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 the Role

We’re looking for a Forward Deployed Marketing Data Scientist to partner closely with our AI Decisioning customers and internal engineering teams to ensure that AI-driven marketing campaigns deliver measurable, compounding impact. This role is uniquely cross-functional: you’ll spend time diagnosing model behavior, tuning ML levers, analyzing incrementality, exploring customer data, and explaining insights to marketers and executives.

Marketing teams come to Hightouch to transform how they operate. Instead of planning campaigns weeks ahead on a calendar, AI Decisioning continuously learns customer preferences and executes 1:1 messaging that adapts in real time. Your mission is to make sure that these AI agents perform at their best—and to help customers understand why they are performing the way they are.

You’ll work alongside ML engineers, product managers, Solutions Consultants, and some of the world’s most recognizable brands to improve campaign performance, debug experiments, and identify opportunities for additional lift. Roughly 30% of your time will be customer-facing and 70% deep analytical and modeling work.

No two days are the same, but you can expect to:

Own diagnostics, insights, and tuning for AI Decisioning campaigns

• Explain why AI Decisioning is driving lift using counterfactuals, incrementality breakdowns, and cohort analysis.
• Debug performance issues, iterate on reward functions, and ensure the agent’s recommendations align with customer goals.
• Investigate experiment setups (send volumes, reachability, channel constraints) and surface actionable recommendations.

Work deeply with data in notebooks and customer warehouses

• Pull down historical data to run exploratory analyses using Polars / Pandas in Jupyter notebooks
• Modify and improve customer feature matrices to unlock deep personalization.
• Conduct deeper warehouse-level SQL analyses when insights aren’t available in the UI.

Build lightweight tooling that enables scale

• Create templates, notebooks, scripts, and repeatable workflows that improve how we analyze performance across customers.
• Identify systemic gaps and influence the direction of ML reporting and introspection.

Communicate ML concepts clearly to non-technical stakeholders

• Present model insights and recommendations to marketers, analysts, and executives.
• Explain how the decision engine handles cold start, message transfer learning, exploration vs. exploitation, and more.
• Partner closely with Solutions Consultants to identify and drive new opportunities for uplift.

What We’re Looking For

• Strong ability to perform deep exploratory data analysis in Python (Polars / Pandas, Jupyter notebooks).
• Ability to write and interpret SQL for customer warehouse analysis.
• High-level understanding of ML modeling concepts (features, hyperparameters, reward functions, training windows).
• Excellent communication skills; able to explain technical reasoning simply and confidently to marketers.
• A customer-first attitude with high ownership and urgency when resolving issues.

Bonus Points

• Experience setting up and analyzing marketing experiments such as A/B, multivariate tests.
• Prior experience in an applied ML, data science, analytics engineering, or forward-deployed role.
• Experience building lightweight internal tools or scripting solutions.

We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. We focus on impact and potential for growth more than years of experience. The salary range for this position is $140,000 - $220,000 USD per year, which is location independent in accordance with our remote-first policy.

Interview Process

We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for.

Our interview process evaluates the most important dimensions of the role: analytical depth, customer communication, first-principles problem solving, and alignment with Hightouch’s values.

Intro Call [15–30m]

Introductory call with either a member of our recruiting team or the hiring manager to get to know each other and see if the role could be a good mutual fit.

Take Home Data Analysis Exercise + Review Session [45m]

You’ll complete a short take-home assignment focused on exploratory data analysis of an example dataset. In the live review, you’ll walk through your approach, explain your reasoning, and present your findings as if you were guiding a customer through your analysis. We’re looking for clarity of communication, rigor of thinking, and the ability to turn data into actionable insights.

Experiment Design & Analysis Session [90m]

A hands-on session where we collaboratively design and evaluate an experiment end-to-end. This includes:

• Defining the experimental goal and how success should be measured
• Identifying the most relevant context and features to use in modeling
• Interpreting example results and diagnosing performance patterns
• Recommending next steps and communicating tradeoffs

Hiring Manager Interview [30m]

Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.

About Wholesail ⛵️

Wholesale trade, a $55T global market, still operates like the world before the internet and modern payment networks: collection processes are manual and run on paper, short-term credit risk is assumed by the vendor, and businesses rely heavily on legacy ERP systems that don't talk to each other. The result is large, measurable drag — we estimate that vendors spend over $500B a year globally across industries on an inefficient mix of processing, bad debt, lending, credit insurance, software, and labor costs to get paid.

Wholesail is building a financial network from the ground up that connects the systems of vendors and buyers involved in wholesale trade to enable streamlined payment and the transfer of risk to third parties. This will allow vendors to offload risk and eliminate tens of billions of waste — while giving creditworthy buyers better terms and third-party capital, unlocking hundreds of billions (ultimately trillions) in additional sales. The primitives of this network scale across industries and geographies: a universal approach to ERP integrations, modern payment rails, and a live trade-credit bureau to underwrite risk — which we're calling Lighthouse.

The Opportunity: Risk & Capital Products 🚢

Credit is the load-bearing beam of our network. Every time a vendor ships goods before getting paid, someone is taking a risk — today it's the vendor, tomorrow it should be a third party at a fair price. Getting that transfer right is what unlocks the next order of magnitude of sales across the wholesale economy, and the only way to get it right is to underwrite buyers more accurately than anyone else in the industry. Listen to the Visa episode of the Acquired podcast to learn how credit card networks did this for retail trade. 

We think we're uniquely positioned to do this. Through Lighthouse, we're building a live, reciprocal trade-credit bureau: vendors on our network contribute real-time payment behavior on a long tail of SMB buyers that no traditional bureau sees. That data — combined with the bank, ERP, and transaction signals already flowing through Wholesail — is a modeling dataset that doesn't exist anywhere else. The first MLE on this team gets to decide what we build with it.

The problems are real and the stakes are significant. Our models directly shape the terms buyers are offered and the losses Wholesail and our capital partners absorb. There's no established playbook here and no legacy stack to inherit — you'll be setting the direction for how we do modeling, data engineering, and production ML at Wholesail for years to come.

The Role ⚒️

As the founding MLE on the Risk Engineering & Capital Products team, you'll own credit risk modeling end-to-end — from problem framing and data pipeline design, through model development and validation, through production serving and monitoring. You'll work directly with the hiring manager, our engineering and product leadership, and our hands-on advisor Dan Massoni (former Chief Commercial Credit Officer of American Express) to decide what to build, explore features to model and how you’ll scale our approach risk scoring. .

Expect your work to span:

• Modeling. Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing) against our proprietary reciprocal-bureau data and external signals. Choose the right tool for the job — gradient-boosted trees, deep models, LLMs, or classical statistical models — and defend the choice.
• Data engineering. Build the pipelines, feature logic, and training datasets that your models (and future models) run on. At this stage, the MLE owns the full data path; over time we'll grow a team around you.
• Productionization. Ship models into production systems where they make real decisions on real dollars. Own deployment, monitoring, drift detection, and iteration.
• Partnering cross-functionally. Work closely with product, engineering, capital markets, and external lending and insurance partners. Explain your models to people who care about the outcomes but not the math, and translate their constraints back into modeling decisions.
• Shaping the team. Help define the hiring bar and interview process for the data scientists and MLEs who will join after you. You'll have a strong voice in what this team becomes.

Qualifications

• 5+ years of experience building models for production use cases.
• Deep modeling skill: strong command of supervised learning on tabular data, including feature engineering, model selection, hyperparameter tuning, calibration, and rigorous offline and online evaluation.
• Strong data engineering skills — comfortable owning ETL and feature pipelines end-to-end against real, messy production data (SQL and a modern data-processing stack).
• Proficiency in Python and the modern ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM, Jupyter, etc.).
• Solid statistical reasoning — you can spot leakage, selection bias, label noise, and spurious correlations, and you know why your offline metric doesn't always predict online performance.
• Track record of owning models end-to-end: not just prototyping in a notebook, but taking something from a problem statement to a production system that makes decisions.
• Excellent written and spoken English communication skills; ability to explain model behavior, tradeoffs, and limitations to non-ML audiences (product, ops, capital partners, leadership).
• The spirit of a team player who believes in fostering a healthy and supportive work environment.

Bonus Qualifications

• Direct experience in credit risk modeling — PD/LGD/EAD models, scorecards, underwriting models, exposure management, or collections modeling — particularly for SMB or subprime segments.
• Experience in fintech, lending, payments, fraud, or insurance, or with regulated modeling environments (model risk management, adverse action, fair lending).
• Experience building or operating ML platform components — feature stores, model registries, training orchestration, online serving, monitoring/drift detection.
• Backend engineering depth: comfortable writing production services, designing APIs, and reasoning about distributed systems that consume your models.
• Strong data analysis background — fluency in exploratory analysis, experimentation, and translating data into product and business decisions.
• Experience with LLMs and agentic systems applied to operational problems (document understanding, KYB, entity resolution, agent tooling).
• Experience as the first or founding ML hire at a company, or early on a team that had to build ML infrastructure from scratch.
• Published research, Kaggle placements, or prominent OSS contributions in the ML or data ecosystem.
• BA or BS in Computer Science, Statistics, Mathematics, a related technical field, or equivalent practical experience.
• Advanced degree (MS or PhD) in a quantitative field.

Equal Employment Opportunity

Wholesail is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, pregnancy, or any other characteristic protected by federal, state, or local law. Employment decisions at Wholesail are based on qualifications, merit, and business needs. We encourage applicants from all backgrounds to apply.

About the Role Scale is at the frontier of GenAI and human-AI collaboration. The Gen AI Ops Trust and Safety team is focused on safeguarding human authenticity and genuineness in AI training. We are looking for a highly analytical Technical Program Manager (TPM) who leans heavily into fraud analytics and data-driven strategy to protect our ecosystem.

This isn't a project management role. You will act as the lead investigative analyst and program owner for our fraud defense portfolio. Your day-to-day will involve diving deep into complex datasets to uncover hidden fraud vectors, and then translating those analytical insights into scalable rules, policies, and operational programs. By utilizing AI coding tools at high velocity, you will build out analytics pipelines, dashboards, and detection logic to shift Trust and Safety from a reactive function to a strategic one that balances safety and growth.

You will:

• Analyze large, messy behavioral events to identify ambiguous and constantly evolving fraud patterns across the contributor lifecycle.
• Translate your analytical findings into actionable detection logic. You will redesign rules, optimize thresholds, and decision flows to catch bad actors while minimizing friction for high-quality contributors.
• Establish robust KPIs, build tracking dashboards, and define offline evaluation frameworks (e.g., false positive monitoring, precision/recall analysis) to continuously measure the health of our risk strategy.
• Act as the connective tissue between data, operations, and engineering. You will use your analytical findings to implement technical execution, taking new detection capabilities from data prototype to production deployment.
• Leverage AI-assisted IDEs daily to rapidly write complex SQL queries, automate data pulls, and streamline your analytical workflows.
• Connect signals, data, and operations to see the full picture. Provide clear, direct communication regarding fraud trends and strategy shifts to both technical and non-technical partners.

Ideally, you’d have:

• 5-8 years of experience in risk strategy and fraud analytics. You have a battle-tested track record of reverse-engineering adversarial patterns, dismantling complex fraud vectors, and driving highly analytical Trust & Safety or TPM programs.
• Expert proficiency in SQL skills. You must be highly comfortable extracting insights from large datasets in noisy, adversarial environments.
• An execution-driven mindset focused on delivering measurable results, not just theoretical analysis. You are comfortable working in ambiguity and taking ownership from data problems to operational solutions.
• Strong proficiency with AI coding assistants to accelerate data exploration and query writing.
• Deep understanding of how to balance aggressive fraud detection with marketplace growth. You make decisions based on what's right for the business, not what's convenient.

Nice to haves:

• Solid Python skills (e.g., Pandas, NumPy) for advanced data manipulation and scripting
• Experience working in marketplace or gig-economy platforms is a plus

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

Location: San Francisco, CA (Hybrid) 

What is Verse? 

Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world’s largest energy buyers, managing energy has never been more complex or more critical.

Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs.

The Role

Verse is seeking a Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and energy risk management. You will shape the machine learning and data modeling foundations that Verse's software is built on. For example, you might spend a cycle deploying electricity market price forecasting pipelines for new regions, developing solar production anomaly detection models, or creating scalable tools for benchmarking energy project financial performance.

Key Responsibilities

• Lead End-to-End Data Science Projects: Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions.
• Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities.
• Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets.
• Software Development & Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems in a cloud-based environment while leveraging AI coding tools to accelerate development.
• MLOps: Contribute to Verse’s machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production.
• Cross-Functional Collaboration: Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows.

What We're Looking For (Minimum Qualifications)

• Master’s degree in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field and 2+ years of professional experience in data science or machine learning; or
• Bachelor’s degree and 4+ years of professional experience in data science or machine learning
• Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation
• Experience deploying and maintaining models in cloud-based environments (e.g., AWS, GCP, or Azure) using MLOps practices
• Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
• Hands-on experience in orchestrating complex data transformations (e.g. Airflow, Dagster, dbt)
• Strong software engineering practices (version control, testing, code reviews, CI/CD)
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders

What Will Make You Standout (Preferred Qualifications)

• Experience in energy, climate tech, or related domains (not required)
• Familiarity with optimization methods or operations research
• Experience developing probabilistic forecasting models and quantifying uncertainty
• PhD in a quantitative field

What Makes Verse a Great Place to Work? 

• Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first
• Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders
• Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
• Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$146,000 - $172,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks 

• Competitive compensation and equity grant at a high-growth start up 
• Comprehensive benefits package including medical, dental and vision insurance, and 401k 
• Flexible hours and unlimited PTO 
• Diverse and inclusive working environment 

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.

Location: San Francisco (Hybrid in SF highly preferred, US only)

What is Verse? 

Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world’s largest energy buyers, managing energy has never been more complex or more critical.

Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs.

The Role

Verse is seeking a Quantitative Analyst to join our Data Science Team. In this position, you will own research projects end-to-end from problem formulation through model development to production integration. Your work feeds directly into Aria and into decisions our customers make about how to procure, dispatch, and hedge their energy assets. For example, you might spend a cycle backtesting financial performance of renewable energy projects, encoding bespoke energy contract terms into reusable and scaled models, or developing a probabilistic model for medium-term energy price scenarios.

Key Responsibilities

• Model & Forecast: Build and improve models for renewable generation, storage dispatch, and energy price prediction using machine learning, statistical methods, and optimization techniques.
• Own Research Projects: Scope problems with product, engineering and customer teams, design solutions, validate results against industry benchmarks, and communicate findings clearly to both technical and non-technical audiences.
• Software Development & Productionization: Integrate research outputs into Verse’s cloud-based production environment; write clean, well-documented Python code and leverage AI coding tools to accelerate development.
• Develop Domain Expertise: Build foundational knowledge of electricity markets and renewable energy trends; develop an understanding of key drivers (e.g., load growth, market rule changes, and renewable deployment) and learn to incorporate relevant insights into renewable energy PPA modeling and analysis.

What We're Looking For (Minimum Qualifications)

• Master's degree in a quantitative field (engineering, computer science, economics, mathematics, operations research, physics, or similar); or
• Bachelor’s degree and 2+ years of energy sector experience
• Solid foundations in machine learning and statistical modeling
• Proficiency in Python, including scientific computing libraries (NumPy, pandas, scikit-learn)
• Familiarity with wholesale electricity market fundamentals and renewable energy technologies
• Ability to communicate technical findings clearly to both technical and non-technical audiences

What Will Make You Standout (Preferred Qualifications)

• Direct experience with US or European wholesale electricity markets or utility tariff structures
• Experience with energy procurement, power purchase agreements, or risk management
• Experience modeling the operational and/or economic behavior of solar, wind, or energy storage
• Foundational knowledge of mathematical optimization
• Experience shipping models to production in a commercial or research environment
• Experience developing probabilistic forecasting models and quantifying uncertainty
• A PhD in a relevant quantitative field.

What Makes Verse a Great Place to Work? 

• Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first
• Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders
• Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
• Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$120,000 - $155,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks 

• Competitive compensation and equity grant at a high-growth start up 
• Comprehensive benefits package including medical, dental and vision insurance, and 401k 
• Flexible hours and unlimited PTO 
• Diverse and inclusive working environment 

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.

About the Role

Our Engineering team creates innovative product experiences that power personalized marketing at massive scale. We develop cutting-edge applications and systems that process 100s of billions of messages and events per year, enabling marketers to connect effectively with hundreds of millions of consumers. Joining our team offers a high-growth career opportunity to work with some of the world’s most talented engineers in a high-performance and high-impact culture.

As a Senior Software Engineer in our Intelligent Messaging, you will pioneer the future of personalized 1:1 marketing by architecting robust, scalable backend systems powered by advanced AI and LLMs, including cutting-edge Generative AI and GPT technologies. Collaborating with top-tier engineers, data scientists, product managers, and designers, you’ll craft the foundation and reshape the platform of multi-channel messaging experimentation to support innovative, AI-driven applications that empower marketers to forge deep, meaningful connections with consumers at scale. Your impactful work will drive the delivery of billions of tailored messages, transforming consumer shopping experiences and fueling success for the world’s leading brands.

What You’ll Accomplish

• Develop and maintain scalable backend systems for our customer-facing products, ensuring high performance and reliability.
• Collaborate with cross-functional teams to build applications that support multi-channel communications for marketers.
• Implement and optimize systems that process and deliver billions of messages daily.
• Improve code quality through code reviews, testing, and advocating for best practices.
• Identify and address technical debt to ensure the long-term health of our codebase.
• Drive architectural design and technical decisions, and stay current with emerging technologies to enhance our products.

Your Expertise

• 5+ years of professional experience in software development focusing on backend systems.
• Proficiency in Java, Python, or Go, with a strong understanding of object-oriented programming.
• Experience building and maintaining scalable, high-performance applications.
• Proven ability to collaborate effectively with cross-functional teams.
• Solid understanding of software development best practices, including code reviews, writing tests, and continuous integration.

Nice to Haves

• Experience with service-oriented architecture and distributed systems.
• Familiarity with AWS services and cloud infrastructure.
• Knowledge of databases such as DynamoDB, Postgres, or Redis.
• Experience with messaging systems or streaming platforms (e.g., Kafka, Pulsar).
• Familiarity with frontend development with React and TypeScript.
• Experience with DevOps practices and tools such as Docker and Kubernetes.

What We Use

• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS.
• Infrastructure tooling includes Istio, Datadog,Terraform, CloudFlare, and Helm.
• Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS.
• Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and PlaywrightOur automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas.

You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.

For US based applicants:

• The US base salary range for this full-time position is $190,000 - $230,000 annually + equity + benefits
• Our salary ranges are determined by role, level and location

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As a Sr. Solutions Architect at Databricks within the Field Engineering org, you will partner with our customers to design scalable data architectures using Databricks technology and services. You have technical depth and business knowledge, and can drive complex technology discussions that express the value of the Databricks platform throughout the sales lifecycle. In partnership with our Account Executives, you will engage with our customers' technical leads, including architects, engineers, and operations teams, with the goal of establishing yourself as a trusted advisor to achieve tangible outcomes. You will work with teams across Databricks and our executive leadership to represent your customers' needs, build valuable customer engagements, and report to the Field Engineering Manager.

The impact you will have:

• You will work with Sales and other essential partners to develop account strategies for your assigned accounts to grow their usage of the platform.
• Establish the Databricks Lakehouse architecture as the standard data architecture for customers through excellent technical account planning.
• You will build and present reference architectures and demo applications for prospects to help them understand how Databricks can be used to achieve their goals to land new users and use cases.
• Capture the technical win by consulting on big data architectures, data engineering pipelines, and data science/machine learning projects; prove out the Databricks technology for strategic customer projects; and validate integrations with cloud services and other 3rd party applications.
• Become an expert in and promote Databricks-inspired open-source projects (Spark, Delta Lake, MLflow, and Koalas) across developer communities through meetups, conferences, and webinars.

What we look for:

• 8+ years in a customer-facing pre-sales or consulting role with expertise in at least one of the following technologies:
• Big data engineering (Ex: Spark, Hadoop, Kafka)
• Data Warehousing & ETL (Ex: SQL, OLTP/OLAP/DSS)
• Data Science and Machine Learning (Ex: pandas, scikit-learn, HPO)
• Data Applications (Ex: Logs Analysis, Threat Detection, Real-time Systems Monitoring, Risk Analysis and more)
• Experience translating a customer's business needs to technology solutions, including establishing buy-in with essential customer stakeholders at all levels of the business.
• Experienced at designing, architecting, and presenting data systems for customers and managing the delivery of production solutions of those data architectures.
• Fluent in SQL and database technology.
• Debug and development experience in at least one of the following languages: Python, Scala, Java, or R.
• [Desired] Built solutions with public cloud providers such as AWS, Azure, or GCP
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Travel to customers in your region up to 30% of the time.

Compliance is critical to Klaviyo’s success. Our mission is to provide a reliable sending platform for customers by serving as an integral part of Klaviyo’s abuse prevention, detection, and response ecosystem. We balance risk mitigation with customer experience through proactive abuse detection and policy adherence with an educational mindset. The Compliance Operations Analyst plays a key role in reducing risk of abuse on the Klaviyo platform while enabling an optimal customer experience.

In this role, you’ll monitor and investigate compliance operations and abuse issues, partner with industry players and vendors, and work cross-functionally with R&D, Deliverability, and Customer teams to improve long-term outcomes across Klaviyo’s sending footprint.

Ideal candidates are deeply curious, data-driven, and excited to contribute to systems that scale. You thrive in ambiguous environments and know how to balance short-term incident response with long-term process improvement.

How You'll Make a Difference:

Monitoring & Incident Response

• Monitor anti-abuse signals, logs, and alerts to identify likely abuse and separate it from normal behavior; recommend mitigations within established guardrails.
• Investigate and address compliance and deliverability-impacting abuse events, engaging and escalating for timely response and mitigation.
• Perform customer-impacting incident triage to determine scope, urgency, and potential impact; document timelines, key facts, and execute assigned tasks with high follow-through.
• Generate and review reporting related to threat alerts and user log behavior (e.g., unwanted mail rate, time to detection, response time).

Systems & Signal Ownership

• Monitor the health of assigned signals (noise levels, failure modes) and raise issues when behavior deviates from expectations.
• Partner with Engineering and Security to test and implement signal tuning; develop in-depth knowledge of Klaviyo’s sending infrastructure and compliance policies.

Process & Operational Improvement

• Follow standard compliance workflows reliably; flag recurring pain points, failure modes, and opportunities for incremental improvement (e.g., playbook updates, dashboard filters, macro refinements).
• Enhance techniques, workflows, and tools to drive compliance team operations and support strategic growth.
• Contribute to cross-functional projects and initiatives, representing the operational voice of compliance in incident response and program design.
• Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up.

Cross-Functional Collaboration & Ecosystem Engagement

• Collaborate with R&D, Deliverability, Security, Customer Success, Support, and Sales; share clear summaries, route work to the right partners, and keep stakeholders informed.
• Coordinate with external vendors (e.g., Sift, aggregators, inbox providers) on day-to-day issues using established channels; document outcomes and surface recurring patterns to senior owners.
• Represent Klaviyo in the anti-abuse community by participating in conferences, forums, and mailing lists (e.g., M3AAWG).
• Stay current on industry trends and emerging threats; bring insights back to influence Klaviyo’s infrastructure strategy.

Knowledge Sharing & Team Contribution

• Write clear case notes, runbook steps, and dashboard annotations; contribute to internal guides and training content.
• Share observations from alerts and incidents with Specialists and peers; participate actively in team reviews and standups.
• Drive knowledge-sharing through documentation, cross-functional training, and thought leadership (e.g., blogs, whitepapers, internal guides).

Who You Are

• Thoughtful communicator—skilled at explaining technical processes clearly in both written and verbal channels, including to non-technical audiences.
• Self-motivated and curious—eager to dive deep into data and infrastructure to understand and solve root causes.
• Calm under pressure—with a track record of navigating fast-paced, ambiguous environments with changing priorities.
• Structured problem-solver—known for technical investigation skills and a logical approach to triage and resolution.
• Process-oriented—constantly thinking about how to improve, automate, or streamline repetitive work.
• Collaborative by nature—thriving on cross-functional partnerships and comfortable engaging directly with customers, internal stakeholders, and technical vendors.
• Growth-oriented—actively seeks feedback, practices new tools and techniques, and applies lessons learned to subsequent work.

Required

• 3+ years in a technical SaaS company, anti-abuse, trust & safety, or compliance operations role—preferably at an ESP or high-volume marketing communications provider.
• Data analysis proficiency—comfortable working with infrastructure-level and behavioral data (e.g., unwanted mail rates, time-to-detection, log behavior); able to pull and review dashboards, spot anomalies, and summarize findings into clear observations and recommendations. SQL or equivalent query experience is strongly preferred.
• AI fluency—understands how AI and automation are used in detection systems and operational workflows; comfortable using approved AI tools (e.g., LLMs, AI-assisted log analysis) to accelerate investigation, summarize incidents, or draft communications, while maintaining human judgment in the loop.
• Anti-abuse and fraud prevention tools—proven experience with platforms such as Sift, Stripe Radar, eHawk, or similar.
• Strong understanding of fraud and abuse landscapes, security protocols, and messaging compliance (email, SMS, privacy standards, and global regulations).
• Monitoring & alerting systems—familiarity with log analysis, signal monitoring, or SIEM-style tools (e.g., Splunk, Datadog, or equivalent); able to read and interpret system logs and alert outputs.
• Familiarity with APIs, email and text message marketing platforms, and e-commerce ecosystems.
• Proficiency with Google Workspace, Zendesk (or equivalent case management), Salesforce, and ticketing systems.
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Bonus

• Presence or active participation in anti-abuse industry groups (M3AAWG, etc.).
• Experience with SMS/MMS technologies, technical writing, training, or web development.
• Python, pandas, or BI tool experience (e.g., Looker, Tableau) for deeper data analysis.
• Familiarity with workflow automation tools (e.g., Zapier, internal scripting) for building repeatable, scalable processes.

Compliance is critical to Klaviyo’s success. Our mission is to provide a reliable sending platform for customers by serving as an integral part of Klaviyo’s abuse prevention, detection, and response ecosystem. We balance risk mitigation with customer experience through proactive abuse detection and policy adherence with an educational mindset. The Compliance Operations Analyst plays a key role in reducing risk of abuse on the Klaviyo platform while enabling an optimal customer experience.

In this role, you’ll monitor and investigate compliance operations and abuse issues, partner with industry players and vendors, and work cross-functionally with R&D, Deliverability, and Customer teams to improve long-term outcomes across Klaviyo’s sending footprint.

Ideal candidates are deeply curious, data-driven, and excited to contribute to systems that scale. You thrive in ambiguous environments and know how to balance short-term incident response with long-term process improvement.

How You'll Make a Difference:

Monitoring & Incident Response

• Monitor anti-abuse signals, logs, and alerts to identify likely abuse and separate it from normal behavior; recommend mitigations within established guardrails.
• Investigate and address compliance and deliverability-impacting abuse events, engaging and escalating for timely response and mitigation.
• Perform customer-impacting incident triage to determine scope, urgency, and potential impact; document timelines, key facts, and execute assigned tasks with high follow-through.
• Generate and review reporting related to threat alerts and user log behavior (e.g., unwanted mail rate, time to detection, response time).

Systems & Signal Ownership

• Monitor the health of assigned signals (noise levels, failure modes) and raise issues when behavior deviates from expectations.
• Partner with Engineering and Security to test and implement signal tuning; develop in-depth knowledge of Klaviyo’s sending infrastructure and compliance policies.

Process & Operational Improvement

• Follow standard compliance workflows reliably; flag recurring pain points, failure modes, and opportunities for incremental improvement (e.g., playbook updates, dashboard filters, macro refinements).
• Enhance techniques, workflows, and tools to drive compliance team operations and support strategic growth.
• Contribute to cross-functional projects and initiatives, representing the operational voice of compliance in incident response and program design.
• Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up.

Cross-Functional Collaboration & Ecosystem Engagement

• Collaborate with R&D, Deliverability, Security, Customer Success, Support, and Sales; share clear summaries, route work to the right partners, and keep stakeholders informed.
• Coordinate with external vendors (e.g., Sift, aggregators, inbox providers) on day-to-day issues using established channels; document outcomes and surface recurring patterns to senior owners.
• Represent Klaviyo in the anti-abuse community by participating in conferences, forums, and mailing lists (e.g., M3AAWG).
• Stay current on industry trends and emerging threats; bring insights back to influence Klaviyo’s infrastructure strategy.

Knowledge Sharing & Team Contribution

• Write clear case notes, runbook steps, and dashboard annotations; contribute to internal guides and training content.
• Share observations from alerts and incidents with Specialists and peers; participate actively in team reviews and standups.
• Drive knowledge-sharing through documentation, cross-functional training, and thought leadership (e.g., blogs, whitepapers, internal guides).

Who You Are

• Thoughtful communicator—skilled at explaining technical processes clearly in both written and verbal channels, including to non-technical audiences.
• Self-motivated and curious—eager to dive deep into data and infrastructure to understand and solve root causes.
• Calm under pressure—with a track record of navigating fast-paced, ambiguous environments with changing priorities.
• Structured problem-solver—known for technical investigation skills and a logical approach to triage and resolution.
• Process-oriented—constantly thinking about how to improve, automate, or streamline repetitive work.
• Collaborative by nature—thriving on cross-functional partnerships and comfortable engaging directly with customers, internal stakeholders, and technical vendors.
• Growth-oriented—actively seeks feedback, practices new tools and techniques, and applies lessons learned to subsequent work.

Required

• 3+ years in a technical SaaS company, anti-abuse, trust & safety, or compliance operations role—preferably at an ESP or high-volume marketing communications provider.
• Data analysis proficiency—comfortable working with infrastructure-level and behavioral data (e.g., unwanted mail rates, time-to-detection, log behavior); able to pull and review dashboards, spot anomalies, and summarize findings into clear observations and recommendations. SQL or equivalent query experience is strongly preferred.
• AI fluency—understands how AI and automation are used in detection systems and operational workflows; comfortable using approved AI tools (e.g., LLMs, AI-assisted log analysis) to accelerate investigation, summarize incidents, or draft communications, while maintaining human judgment in the loop.
• Anti-abuse and fraud prevention tools—proven experience with platforms such as Sift, Stripe Radar, eHawk, or similar.
• Strong understanding of fraud and abuse landscapes, security protocols, and messaging compliance (email, SMS, privacy standards, and global regulations).
• Monitoring & alerting systems—familiarity with log analysis, signal monitoring, or SIEM-style tools (e.g., Splunk, Datadog, or equivalent); able to read and interpret system logs and alert outputs.
• Familiarity with APIs, email and text message marketing platforms, and e-commerce ecosystems.
• Proficiency with Google Workspace, Zendesk (or equivalent case management), Salesforce, and ticketing systems.
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Bonus

• Presence or active participation in anti-abuse industry groups (M3AAWG, etc.).
• Experience with SMS/MMS technologies, technical writing, training, or web development.
• Python, pandas, or BI tool experience (e.g., Looker, Tableau) for deeper data analysis.
• Familiarity with workflow automation tools (e.g., Zapier, internal scripting) for building repeatable, scalable processes.

Compliance is critical to Klaviyo’s success. Our mission is to provide a reliable sending platform for customers by serving as an integral part of Klaviyo’s abuse prevention, detection, and response ecosystem. We balance risk mitigation with customer experience through proactive abuse detection and policy adherence with an educational mindset. The Compliance Operations Analyst plays a key role in reducing risk of abuse on the Klaviyo platform while enabling an optimal customer experience.

In this role, you’ll monitor and investigate compliance operations and abuse issues, partner with industry players and vendors, and work cross-functionally with R&D, Deliverability, and Customer teams to improve long-term outcomes across Klaviyo’s sending footprint.

Ideal candidates are deeply curious, data-driven, and excited to contribute to systems that scale. You thrive in ambiguous environments and know how to balance short-term incident response with long-term process improvement.

How You'll Make a Difference:

Monitoring & Incident Response

• Monitor anti-abuse signals, logs, and alerts to identify likely abuse and separate it from normal behavior; recommend mitigations within established guardrails.
• Investigate and address compliance and deliverability-impacting abuse events, engaging and escalating for timely response and mitigation.
• Perform customer-impacting incident triage to determine scope, urgency, and potential impact; document timelines, key facts, and execute assigned tasks with high follow-through.
• Generate and review reporting related to threat alerts and user log behavior (e.g., unwanted mail rate, time to detection, response time).

Systems & Signal Ownership

• Monitor the health of assigned signals (noise levels, failure modes) and raise issues when behavior deviates from expectations.
• Partner with Engineering and Security to test and implement signal tuning; develop in-depth knowledge of Klaviyo’s sending infrastructure and compliance policies.

Process & Operational Improvement

• Follow standard compliance workflows reliably; flag recurring pain points, failure modes, and opportunities for incremental improvement (e.g., playbook updates, dashboard filters, macro refinements).
• Enhance techniques, workflows, and tools to drive compliance team operations and support strategic growth.
• Contribute to cross-functional projects and initiatives, representing the operational voice of compliance in incident response and program design.
• Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up.

Cross-Functional Collaboration & Ecosystem Engagement

• Collaborate with R&D, Deliverability, Security, Customer Success, Support, and Sales; share clear summaries, route work to the right partners, and keep stakeholders informed.
• Coordinate with external vendors (e.g., Sift, aggregators, inbox providers) on day-to-day issues using established channels; document outcomes and surface recurring patterns to senior owners.
• Represent Klaviyo in the anti-abuse community by participating in conferences, forums, and mailing lists (e.g., M3AAWG).
• Stay current on industry trends and emerging threats; bring insights back to influence Klaviyo’s infrastructure strategy.

Knowledge Sharing & Team Contribution

• Write clear case notes, runbook steps, and dashboard annotations; contribute to internal guides and training content.
• Share observations from alerts and incidents with Specialists and peers; participate actively in team reviews and standups.
• Drive knowledge-sharing through documentation, cross-functional training, and thought leadership (e.g., blogs, whitepapers, internal guides).

Who You Are

• Thoughtful communicator—skilled at explaining technical processes clearly in both written and verbal channels, including to non-technical audiences.
• Self-motivated and curious—eager to dive deep into data and infrastructure to understand and solve root causes.
• Calm under pressure—with a track record of navigating fast-paced, ambiguous environments with changing priorities.
• Structured problem-solver—known for technical investigation skills and a logical approach to triage and resolution.
• Process-oriented—constantly thinking about how to improve, automate, or streamline repetitive work.
• Collaborative by nature—thriving on cross-functional partnerships and comfortable engaging directly with customers, internal stakeholders, and technical vendors.
• Growth-oriented—actively seeks feedback, practices new tools and techniques, and applies lessons learned to subsequent work.

Required

• 3+ years in a technical SaaS company, anti-abuse, trust & safety, or compliance operations role—preferably at an ESP or high-volume marketing communications provider.
• Data analysis proficiency—comfortable working with infrastructure-level and behavioral data (e.g., unwanted mail rates, time-to-detection, log behavior); able to pull and review dashboards, spot anomalies, and summarize findings into clear observations and recommendations. SQL or equivalent query experience is strongly preferred.
• AI fluency—understands how AI and automation are used in detection systems and operational workflows; comfortable using approved AI tools (e.g., LLMs, AI-assisted log analysis) to accelerate investigation, summarize incidents, or draft communications, while maintaining human judgment in the loop.
• Anti-abuse and fraud prevention tools—proven experience with platforms such as Sift, Stripe Radar, eHawk, or similar.
• Strong understanding of fraud and abuse landscapes, security protocols, and messaging compliance (email, SMS, privacy standards, and global regulations).
• Monitoring & alerting systems—familiarity with log analysis, signal monitoring, or SIEM-style tools (e.g., Splunk, Datadog, or equivalent); able to read and interpret system logs and alert outputs.
• Familiarity with APIs, email and text message marketing platforms, and e-commerce ecosystems.
• Proficiency with Google Workspace, Zendesk (or equivalent case management), Salesforce, and ticketing systems.
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Bonus

• Presence or active participation in anti-abuse industry groups (M3AAWG, etc.).
• Experience with SMS/MMS technologies, technical writing, training, or web development.
• Python, pandas, or BI tool experience (e.g., Looker, Tableau) for deeper data analysis.
• Familiarity with workflow automation tools (e.g., Zapier, internal scripting) for building repeatable, scalable processes.

THE COMPANY:

Juul Labs's mission is to transition the world’s billion adult smokers away from combustible cigarettes, eliminate their use, and combat underage usage of our products. We have the opportunity to address one of the world’s most intractable challenges through a commitment to exceptional quality, research, design, and innovation. Backed by leading technology investors, we are committed to the same excellence when it comes to hiring great talent.

We are a diverse team that is united by this common purpose and we are hiring the world’s best engineers, scientists, designers, product managers, operations experts, and customer service and business professionals. If the opportunity to build your career is compelling, read on for more details.

ROLE AND RESPONSIBILITIES:

The Mechatronics Engineer is a hands-on role that will be responsible for designing new products that are innovative, cost efficient, reliable, and ship on schedule. This role will drive the validation of concepts via early analysis, prototyping and testing, and work with cross-functional teams to ensure that product requirements are managed to achieve optimal experiences for our customers. In addition, the Mechatronics Engineer will work closely with internal and external partners to technically define designs and ensure that all product and project requirements are met. You will troubleshoot and make improvements to existing products and new products as well as test instrumentation. You will  work closely with other members of the engineering staff to create optimal solutions to a myriad of product related topics such as but not limited to mechanical design, thermal, and data analytics. You will be comfortable sketching and prototyping new ideas and be familiar with engineering best practices, concepts and procedures. We are looking for someone who is flexible and can respond quickly, energetically, and enthusiastically to changes. The person in this role should be a strong communicator who is able to lead effectively and work effectively with peers and management alike in a high-paced global environment. 

• Experienced with embedded control systems and adept at deriving meaningful insights through data analytics to drive improvements in existing algorithms.
• Leverage existing tools, systems, and/or processes for data collection and analysis.
• Takes initiative and ownership of high level work. Negotiates and drives alignment with product architects, managers, and other key stakeholders across multiple disciplines.
• Design, implement, and drive to completion complex system DoEs spanning several engineering disciplines including EE, ME, FW, and SW. 

PERSONAL AND PROFESSIONAL QUALIFICATIONS: 

• Min 2 years working experience in Python and C++ software development, including system design, data structures & algorithms.
• Experienced with Python and Jupyter Notebooks; specifically, numpy, pandas, scipy, and sklearn.
• Prototyping background: Have a maker mentality that allows you to prototype quickly while also producing robust systems that sustain through heavy use and have well-documented construction and troubleshooting guidelines
• 3-5 years of relevant engineering experience in one or more of the following areas: product design, new product release, test engineering.
• Conduct rapid feasibility studies to design for mechanical, fluidic, and thermal robustness.
• Experienced in developing and testing both software applications and mechanical systems, ensuring they work together.
• Oversee internal and external prototyping.
• Work directly with our CM or prototyping partners in support of system assembly build activities.
• Expertise in one (or more of) the areas of: Statistical Analysis, Design of Experiments, Thermal Design, etc.
• Expertise in 3D MCAD, Solidworks preferred.

EDUCATION 

The ideal candidate will possess a Bachelor's degree or higher in Mechatronics, Mechanical Engineering, or a related field

JUUL LABS PERKS & BENEFITS:

• A place to grow your career. We’ll help you set big goals - and exceed them
• People. Work with talented, committed and supportive teammates
• Equity and performance bonuses. Every employee is a stakeholder in our success
• Cell phone subsidy, commuter benefits and discounts on JUUL products
• Excellent medical, dental and vision, disability, and life insurance, plus family support, wellness, legal, and employee assistance program benefits
• 401(k) plan with company matching
• Plus biannual discretionary performance bonuses

Juul Labs is proud to be an equal opportunity employer and is committed to creating a diverse and inclusive work environment for all employees and job applicants, without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. We will consider for employment qualified applicants with arrest and conviction records, pursuant to the San Francisco Fair Chance Ordinance. Juul Labs also complies with the employment eligibility verification requirements of the Immigration and Nationality Act. All applicants must have authorization to work for Juul Labs in the US.

Project Overview

This is a cutting-edge initiative to integrate Large Language Models (LLMs) with WorldQuant's proprietary strategy management tools. The project aims to provide AI-assisted insights, automate routine analytical tasks, and enable natural language interactions for Portfolio Managers to more efficiently analyze, maintain, and enhance their trading strategies. This involves building foundational infrastructure, developing robust data pipelines for metrics and features, and creating a secure, user-centric interface with a strong focus on protecting intellectual property.

The Role:

We are seeking a talented and motivated Full-Time Software Engineer to play a key role in the development and enhancement of this AI-driven platform. You will be responsible for designing, building, and maintaining critical components of the system, primarily focusing on the Model Context Protocol (MCP) servers that form the backbone of the platform. This includes enhancing existing MCPs, such as our proprietary command-line interface for strategy data, developing new MCPs for internal data system integration, strategy analysis, and performance metrics, and contributing to the secure and effective use of LLMs.

Key Responsibilities

• Design, develop, test, and deploy robust and scalable MCP servers and related tools in Python.
• Enhance existing MCPs, including our internal data access tools, to ensure reliable access to strategy metadata, historical PnL data, and PnL attribution by risk factors.
• Develop new MCPs, including but not limited to MCPs for direct data system integration, MCPs for strategy analytics, and MCPs for performance reporting.
• Design and implement secure prompt engineering techniques to interact with LLMs, ensuring effective information retrieval while minimizing the risk of intellectual property leakage.
• Collaborate with project managers, technical leads, and other developers to define technical specifications and integrate various data sources.
• Work on API development and integration, ensuring seamless data flow between different systems (internal knowledge management systems, simulation systems, data warehouses).
• Implement data parsing and manipulation logic for various formats (JSON, CSV).
• Contribute to the design and implementation of data pipelines for strategy metrics and features.
• Address and resolve bugs and issues in existing MCP tools and infrastructure.
• Participate in code reviews, and contribute to unit and integration testing efforts.
• Create and maintain technical documentation for developed components.
• Stay updated with emerging technologies and methodologies in AI, LLMs, prompt engineering, and financial technology.

What You’ll Bring:

Core Skills:

• Strong proficiency in Python programming.
• Proven experience in API development, integration, and consumption.
• Solid understanding of database interaction, data access patterns, and data modeling.
• Experience with data parsing and manipulation libraries and techniques (e.g., pandas, JSON, CSV).
• Understanding of or experience with prompt engineering principles for Large Language Models
• Awareness of data security and intellectual property protection considerations in the context of AI and LLM applications

Domain-Specific Knowledge (Highly Beneficial):

• Familiarity with financial data (e.g., PnL, Sharpe ratio, alpha, risk factors, AUM).
• Experience with quantitative trading systems, market data systems, or strategy management platforms (familiarity with internal data systems or command-line data tools is a significant plus).

Technical Environment Familiarity (Helpful):

• Experience working in a Linux environment.
• Understanding of Command Line Interface (CLI) tools and development.
• Familiarity with version control systems (e.g., Git).

Soft Skills

• Excellent problem-solving and analytical skills.
• Strong collaboration and communication skills, with the ability to work effectively in a team.
• High attention to detail and commitment to delivering high-quality software.
• Ability to learn quickly and adapt to new technologies and complex systems.

Education

• Bachelor's or Master's degree in Computer Science, Engineering

Position

Manifold's AI team is actively training protein foundation models on our proprietary experimental datasets. Our generative antibody design model, mBER, has already demonstrated controllable de novo binder design across multiple million-scale screening campaigns, and the team is now scaling foundation model capabilities to push well beyond current performance. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifold's data, bringing additional depth in pre-training methodology, architecture development, and large-scale training. Your work will directly improve mBER's design capabilities and unlock new modeling paradigms for the broader team. You'll own foundation model projects end-to-end, from architecture selection and training infrastructure to evaluation against real experimental outcomes, while contributing to the team's shared research agenda.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Advance the team's ongoing foundation model training efforts—pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data
• Bring depth in training methodology, architecture selection, and optimization to complement the existing team's expertise
• Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs
• Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities
• Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis
• Establish best practices for mixed-precision training, gradient checkpointing, and computational efficiency at scale
• Produce clear documentation and analysis supporting architecture and training decisions

Required Qualifications

• Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results
• Strong familiarity with AlphaFold architecture and training methodology
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning
• Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing
• Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques
• Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets
• Strong statistical analysis and experimental design skills
• Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Self-directed researcher who can balance guidance with independence
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)
• Experience with protein language models (e.g., ESM family)
• Published research in computational biology, protein design, or structural biology
• Experience training on proprietary or domain-specific biological datasets
• Familiarity with Ray for distributed computing
• Experience with Kubernetes (EKS) and cloud computing platforms (AWS)
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments
• Previous biotech/pharma industry experience

This Role Might Be Perfect For You If:

• You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field
• You're excited about pushing beyond public model performance by leveraging unique, large-scale in vivo screening data
• You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-knit, world-class team
• You want your models to directly impact real drug discovery programs

If you're excited to train the next generation of protein foundation models on uniquely powerful experimental data, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

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.

What you’ll do

About the internship

Stripe's Applied ML, Data Science, Risk, and Payments organizations are excited to offer PhD machine learning engineering internships for the summer of 2026. This is an exceptional opportunity to contribute to critical projects that directly enhance Stripe's suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.

As an intern, you'll tackle challenging problems at the intersection of finance, technology, and data. You'll have the chance to work on creative projects like the Stripe Assistant and the Stripe Foundation Model, which leverage machine learning to revolutionize how businesses interact with financial services and data.

Responsibilities

• Develop and deploy large-scale machine learning systems that drive significant business value across various domains.
• Engage in the end-to-end process of designing, training, improving, and launching machine learning models.
• Write production-scale ML models that will be deployed to help Stripe enable economic infrastructure access for a diverse range of businesses globally.
• Collaborate across teams to incorporate feedback and proactively seek solutions to challenges.
• Rapidly learn new technologies and approaches, demonstrating a strong ability to ask insightful questions and communicate the status of your work effectively.

Who you are

Minimum requirements

• A deep understanding of computer science, obtained through the pursuit of a PhD in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2026 or spring/summer 2027.
• Practical experience with programming and machine learning, evidenced by projects, classwork, or research. Familiarity with languages such as Python, Scala, Spark and libraries such as Pandas, NumPy, and Scikit-learn.
• Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models or Reinforcement Learning.
• Demonstrated ability to work on collaborative projects, with experience in receiving and applying feedback from various stakeholders.
• A proactive approach to learning unfamiliar systems and a demonstrated ability to understand complex systems independently.
• Intent to return to the degree-program after the completion of the internship/co-op.

Preferred qualifications

You Might Also Have:

• Two years of university education or equivalent experience, with in-depth knowledge in specific domains of machine learning.
• Published and presented peer-reviewed articles in top-tier venues.
• Experience in writing high-quality pull requests, maintaining good test coverage, and completing projects with minimal defects.
• Familiarity with navigating new codebases and managing work across different programming languages.
• Excellent written communication skills to clearly articulate your work to both team members and wider Stripe audiences.

Application requirements

Please submit the following with your application:

• A detailed resume or LinkedIn profile showcasing your work history.
• Examples of relevant work and your approach to learning, such as GitHub repositories, StackOverflow contributions, or other project portfolios.

Join us for an unforgettable summer internship and help shape the future of global commerce. At Stripe, you won't just be working on theoretical projects; you'll make a tangible impact on the world's economic infrastructure.

Your Impact at LILA

Join us in shaping the future of science! We are seeking Sr Principal, and Principal Software Engineers with full stack experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!

Our Software Teams

The Application Team (App)- They build the operating system for AI-driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI-native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

The Scientific System of Record Team (SSR) - They build the memory layer for Lila's operations. It answers two questions:what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop.

The Lab as Software Team (LaS)- They act as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

The Data Platform Team (Data)- They build and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

What You'll Be Building

• Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
• Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
• Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
• Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
• Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You'll Need To Succeed  

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 8-15 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
• Hands on experience using AI coding assistants to drive productivity is required.
• Communication & Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
• Experience with laboratory devices, robotics, or hardware drivers.

About the Role 

In this role, you will build and lead our forward-deployed engineering (FDE) team, working directly with leading labs and enterprises to scope, build and deliver high quality datasets to support their most critical AI initiatives. 

You’ll lead a team that will own quality in the end-to-end data pipeline. This will include working with customers to define what “good” data looks like to implement the relevant workflows in their platform. You will design innovative ML approaches to enhance human-in-the-loop (HITL) techniques and improve the efficiency of data generation and review processes. Your team will own systems and tools that enable consistent, scalable, and high-quality data delivery to our customers.

Sitting at the critical intersection of data engineering, ML engineering, operations, and customer engagement— leading scoping and preselling efforts.  You'll also partner closely with the Snorkel delivery team and cross-functional stakeholders to define quality standards, develop measurement frameworks, drive ML-based workflows to improve data pipelines and unblock projects through technical innovation. As the founding member, you’ll also roll up your sleeves to define and own the workflows and processes that are needed to deliver exceptional data at scale.

Main Responsibilities

• Build and lead the Forward Deployed Engineering DaaS organization, setting a clear vision, defining the operating model and scaling its impact across Snorkel’s Expert Data-as-a-Service workflows
• Build, mentor, and motivate high performing teams, including cultivating skills and culture needed to consistently deliver exceptional outcomes and transformative impact. 
• Own and evolve the data pipeline components of the DaaS stack, including model-assisted labeling and data generation, quality estimation, and data-centric feedback loops that guide human input
• Partner with customers - including research and engineering teams at Frontier AI Labs - to scope requirements for complex, novel AI datasets and translate needs into delivery-ready workflows 
• Develop robust systems for request intake, task orchestration, SLA tracking, and progress monitoring to ensure seamless execution and prevent critical delivery gaps
• Collaborate cross-functionally with research and engineering teams to innovate, develop, and productionize HITL data generation methods, advanced quality techniques, and improve internal delivery tooling
• Drive continuous improvement by developing reusable workflows, surfacing operational insights, and enabling the organization to scale faster while maintaining high quality

What We’re Looking For

• 10+ years of experience in applied data or ML engineering roles, including 5+ years leading high-performing technical teams in hands-on management capacity
• Demonstrated success in customer facing roles, with a strong enthusiasm for data pipelines and LLM-based workflows.
• Proven track record of managing technical field teams in fast-paced, delivery-focused environments with competing priorities
• Experience as a player-coach—comfortable being hands-on while supporting and scaling the team
• Proven ability to thrive in fast-paced, ambiguous environments with cross-functional stakeholders
• Strong practical experience with LLM-based workflows, Python, SQL, and data tooling (e.g., pandas, Plotly, Streamlit, Dash)
• Bonus: experience working with data annotation workflows or internal tooling for data delivery orgs

Why Join Snorkel AI?

At Snorkel AI, we're building the future of data-centric AI. Our Expert Data-as-a-Service organization partners with world-class customers to solve some of the hardest data challenges — creating training and evaluation data that power the next generation of LLMs and AI systems. You'll work directly on projects that impact real production systems, while shaping how internal teams deliver faster, better, and more intelligently. This is a rare opportunity to found a function and have a broad, lasting impact across the company.

Your Impact at LILA

Join us in shaping the future of science! We are seeking Staff, Senior II, and Senior I Software Engineers with either frontend or backend experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!

Our Software Teams

The Application Team (App)- They build the operating system for AI-driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI-native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

The Scientific System of Record Team (SSR) - They build the memory layer for Lila's operations. It answers two questions:what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop.

The Lab as Software Team (LaS)- They act as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

The Data Platform Team (Data)- They build and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

What You'll Be Building

• Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
• Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
• Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
• Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
• Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 5-10 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
• Hands on experience using AI coding assistants to drive productivity is required.
• Communication & Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
• Experience with laboratory devices, robotics, or hardware drivers.

Position

Manifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production-ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting-edge ideas into robust, scalable systems.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Implement and optimize machine learning models for protein design
• Build and maintain scalable data processing pipelines for large-scale protein and molecular datasets
• Develop and deploy ML infrastructure for distributed training and inference across GPU clusters
• Collaborate with research scientists to translate experimental ML approaches into production-ready code
• Design and execute ML experiments with clear hypotheses and rigorous analysis
• Optimize model performance and computational efficiency for large-scale protein design tasks
• Build tools and utilities to support rapid prototyping and experimentation by the research team

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications
• Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Experience with distributed computing and GPU optimization techniques
• Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences
• Understanding of modern deep learning architectures and optimization techniques
• Experience implementing research papers or translating ML approaches to production systems
• Proficiency with version control (Git), testing frameworks, and software engineering best practices
• Strong problem-solving skills and ability to work independently on technical challenges
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience training LLMs or diffusion generative models
• Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)
• Background in computational biology, bioinformatics, or structural biology
• Experience with large-scale data engineering and ETL pipelines
• Familiarity with MLOps practices and model deployment frameworks

This Role Might Be Perfect For You If

You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas

• You enjoy translating research ideas into high impact, productionized, scalable code
• You have rich AI/ML experience and are looking to pivot into biotech

If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

What You'll Do

You will be at the center of informing Zipline on the critical environmental conditions that our aircraft must fly in to achieve business goals, whilst owning the automated system that enforces those limits operationally to ensure flight safety and protecting an excellent and informed customer experience.  You will lead the team that counts the following among its core deliverables:

• Design and develop our automated weather monitoring systems to make real-time go/no-go decisions for missions, as well as forecast in-coming weather conditions that would require pausing deliveries; 
• Define the requirements on flyable weather conditions to achieve business performance goals that are achievable in the context of automated risk system performance;
• Set the pace on simulation- and real-world-based weather-related validation strategies, including performance of the weather system risk decisions, operations simulations to determine the role of the weather system on customer experience dynamics, and developing representative but tractable weather models in Zipline’s high fidelity flight simulation tools;
• Enable and maintain strong and unambiguous communication with customer, fulfilment and flight operations teams, setting expectations for the system uptime, on both seasonal and daily timescales
• Drive the development of ground- and air-based sensing to support the risk posture calculations in a scalable and cost-effective way

What You'll Bring

This is a leadership role on a team that is core to Zipline’s success, and is therefore not an entry level position. Success in this role will require a disciplined and first-principles approach, with a deep connection and understanding at the system and product levels.  You’ll be naturally curious and able to work seamlessly with individuals and teams both inside and outside of your core engineering / scientific discipline, as well as regulatory, operations and business partners.  Experience in many of the following areas are critical to your ability to contribute at the right level:

• You will have a minimum of 5+ years of industry experience, and either be a proven people leader or clearly ready to own and develop a small team (3-5 direct reports).
• Advanced proficiency with scientific python and libraries commonly used for data analysis, e.g., Pandas, Numpy, Scipy, and Matplotlib, XArray, NETCDF.  In addition, you’ll be able to learn new languages and libraries for use in daily work (eg. Rust, C++), and ideally have cloud-native experience with Kubernetes and/or AWS.
• Familiarity with geospatial and meteorological datasets, formats, and tools; in particular you’ll understand how to efficiently process large datasets across distributed systems and ideally have experience with kafka or other streaming data systems
• As well as a strong understanding of descriptive statistics, probability and linear algebra, you will have mastered a wide range of machine learning and statistical techniques, and developed an intuition for what tool to apply in every situation
• Adept at summarizing large datasets visually that make complex results understandable at a glance to different audiences.  You will be able to find creative ways to take the uncertainty of weather prediction and turn it into certainty for the business.
• Excellent understanding of meteorology and climatology, and an ability to communicate the underlying physics and their challenges to a broad audience
• You’ll be highly self-directed; able to drive a roadmap and ruthlessly prioritize, balancing a core roadmap against the emerging needs of a fast-scaling and complex system.  You’ll also be proven in setting the pace of a small team, striving to continuously improve the development tools and processes and help folks around you work smarter
• Experience with aircraft flight and aviation weather and regulation would be helpful, although you will work closely with other leaders of our flight sciences teams so a willingness to learn this rapidly will suffice.  An ability to manipulate the weather would be advantageous, but is not essential.

What Else You Need to Know  

This role is based out of our South San Francisco HQ with minimum of 5 days per week in office. Must be eligible to work in the US and travel globally as needed

The starting cash range for this role is $235,000 to $255,000. Please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more.

Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, ancestry, national origin, religion or religious creed, mental or physical disability, medical condition, genetic information, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity, gender expression, age, marital status, military or veteran status, citizenship, or other characteristics protected by state, federal or local law or our other policies.

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply!

Doximity is transforming the healthcare industry. Join our mission to help every physician be more productive and provide better care for their patients. As medicine's largest network in the United States, there's an elevated level of responsibility in everything we do. We don't take that responsibility lightly and are committed to building diverse teams with an inclusive culture that can make a direct impact on the healthcare system.

One of Doximity's core values is stretching ourselves. Even if you don't check off all the boxes below we encourage you to apply. Doximity is full of exceptional people who bring their own unique experiences to work everyday and make us all better for it!

This role can be filled in our San Francisco headquarters OR remotely in the U.S.

As a Business Intelligence Analyst, you’ll work within cross-functional delivery teams alongside other analysts, engineers, and commercial stakeholders in discovering data insights to help improve healthcare.

How you’ll make an impact:

Compensation

The US total compensation range for this full-time position is $94,000-$150,000 (inclusive of salary + equity). Our ranges are determined by role and level. The range displayed on each job posting reflects the approximate total target compensation for the position across the US. Within the range, individual pay is determined by factors including relevant skills, experience, and education/training. Please note that the compensation listed does not include benefits.

More on Benefits/Perks 

Doximity is proud to offer industry-leading benefits to our full time employees. Some of our offerings include: 

• Medical, dental, vision offerings for you and your family
• 401k with matching program
• Employee stock purchase plan
• Family planning support, Childcare FSA, and parental leave
• Life, AD&D, and Disability
• Generous time off, holidays and paid company trips
• Wellness benefits…plus many more!

More About Doximity…

For the past decade, it’s been our mission to help every physician be more productive so they can provide better care for their patients. We believe that when doctors are connected, the healthcare system works better and patients benefit. Doximity enables our verified clinician members to collaborate with colleagues, stay up-to-date with the latest medical news and research, manage their careers, and conduct virtual patient visits. Today, Doximity is the leading digital platform for U.S. medical professionals, with over 80% of physicians, 50% of all nurse practitioners and physician assistants, and 90% of graduating medical students as members. 

Joining Doximity means being part of an incredibly talented and humble team passionate about improving inefficiencies in our $4.3 trillion U.S. healthcare system. We are a team of doers who solve problems everyday by treating obstacles like an adventure, and we love creating technology that has a real, meaningful impact on people’s lives. Doxers are committed to working towards a more equitable world both within and beyond our office walls. This starts by fostering an inclusive and diverse work environment where differences are valued and all employees are encouraged to bring their full, authentic selves to work daily. To learn more about our team, culture, and users, check out our careers page, company blog, and engineering blog. We’re growing fast, and there’s plenty of opportunity for you to make an impact—join us! For more information, visit Doximity.com.

____________________________________________

EEOC Statement

Doximity is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.

As a Hunter Solutions Architect on the Digital Natives team, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Work directly with prospective, greenfield, customers on their 0 to 1 journey with Databricks 
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems at scale 
• Comfortable programming in, and debugging, Python and SQL
• Experience using agentic coding harnesses and demonstrable experience scaling your work leveraging them 
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
• Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

WHAT YOU'LL DO

• Use robust software engineering best practices to design, implement, and improve modular components in a cutting-edge product
• Collaborate closely with other teams (e.g., UI/UX, Product, Research, Implementation) to create scalable and dynamic Braze product components 
• Apply your extensive knowledge of TypeScript, JavaScript and their ecosystem to build a clean, reliable, and extensible user interface for the web
• Influence Braze’s product strategy and roadmap
• Support teams implementing Braze for customers to ensure their success
• Tech stack:
  • Web: TypeScript, JavaScript, Vue.js (v3) and its ecosystem (VueCLI with webpack), Node.js, Strapi, PostgreSQL, HTML5, CSS3
  • Data Science/ML Pipeline: Python (Pandas, TF, Fast API, Great Expectations), SQL (not required for this role although some experience with Python would be helpful)
  • We write well-tested, type-hinted, documented, modular code and use pre-commit hooks, CI/CD, and issue tracking for development

WHO YOU ARE

• Exceptional coder: you write clean code and you care about good design and development best practices
• Tinkerer: you regularly explore and learn new technologies and methods
• Problem solver: you recognize patterns, synthesize information and come up with creative, simple, and practical solutions
• Entrepreneurial: you proactively identify opportunities and risks, work around obstacles, and always seek creative ways to improve processes and outcomes
• Structured and organized: you can structure a plan, align stakeholders, and see it through to execution
• Clear communicator: you are able to express yourself clearly and persuasively, both in writing and speech

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $129,600 and $232,200/year with an expected On Target Earnings (OTE) between $144,000 and $258,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

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 Data Analyst II (DA), you will play a central role in enhancing the operational tracking and reporting capabilities of different business teams across Brex. You will work closely with Data Scientists, Data Engineers, and partner teams to drive meaningful insights for the business through visualizations, self-service tools, and ad-hoc analyses. This is a high-impact role in a fast-paced fintech environment where your work will directly influence strategic decisions.

Where you’ll work

This role will be based in our São Paulo 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

• Apply data visualization and storytelling skills in creating business intelligence solutions (such as Looker and/or Hex dashboards) that enable actionable insights.
• Perform ad-hoc analyses and deep dives to investigate business questions, surface trends, and provide data-driven recommendations.
• Develop self-service data tools and processes that empower business stakeholders to independently monitor the performance and health of their respective areas.
• Collaborate closely with Data Scientists and Data Engineers to identify data sources, enable data pipelines, and support the development of analytical data models that operationalize reports and dashboards.
• Implement and maintain rigorous data quality checks to ensure the integrity and robustness of datasets used across dashboards, reports, and analyses.
• Partner with various departments—including Sales, Operations, Product, and Finance—to understand their data needs and deliver tailored analyses and reporting that support strategic planning.
• Contribute to the automation of recurring analyses and reporting workflows using Python.

Requirements

• 4+ years of experience in data analytics or a related role in a professional setting.
• 3+ years of experience working directly with Sales, Operations, Product, or equivalent business teams.
• Fluency in SQL to manipulate data and perform complex analyses (CTEs, window functions, joins across large datasets).
• Proficiency in Python for data analysis, automation, and scripting (Pandas, NumPy, and similar libraries).
• Experience with business intelligence and data visualization tools (Looker, Hex, Tableau, or similar).
• Strong quantitative and analytical skills with a demonstrated ability to translate data into business insights.
• Strong communication skills and the ability to work effectively with stakeholders across different functions and levels of technical fluency.
• 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

• Familiarity with cloud data platforms (e.g., Snowflake, BigQuery, Databricks).
• Familiarity with dbt for data modeling and transformation.
• Exposure to data pipeline orchestration tools (e.g., Airflow).
• Experience in fintech, financial services, or payments.
• Comfort operating in a fast-paced, high-growth environment with evolving priorities.

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

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 New York 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.

While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered. #LI-REMOTE

As a Solutions Architect on the Digital Natives team, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customer stakeholders, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide one of our largest AI native customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 7+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems
• Comfortable programming in, and debugging, Python and SQL
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
  • Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
  • Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

Doximity is transforming the healthcare industry. Join our mission to help every physician be more productive and provide better care for their patients. As medicine's largest network in the United States, there's an elevated level of responsibility in everything we do. We don't take that responsibility lightly and are committed to building diverse teams with an inclusive culture that can make a direct impact on the healthcare system.

One of Doximity's core values is stretching ourselves. Even if you don't check off all the boxes below we encourage you to apply. Doximity is full of exceptional people who bring their own unique experiences to work everyday and make us all better for it!

As a Data Analyst, you'll work within cross-functional delivery teams alongside other analysts, engineers, and product managers in discovering data insights to help improve healthcare.

This role is expected to work in our San Francisco office 3 days a week (M, T, TH). 

About Us

• Here are some of the ways we bring value to doctors
• Here is an introduction to our tech stack 
• We use UNIX command-line interface and standard programming tools (vim/emacs, git, etc.) and have over 350 private repositories in Github containing our applications, forks of gems, our own internal gems, and open-source projects
• We have worked as a distributed team for a long time; we're currently about 65% distributed
• Find out more information on the Doximity engineering blog
• Our company core values
• Our recruiting process
• Our product development cycle
• Our on-boarding & mentorship process

Here's How You Will Make an Impact

• Leverage Doximity's extensive datasets to identify and classify behavioral patterns of medical professionals on our platform.
• Play a key role in creating both product and client-facing analytics.
• Inform data team strategy by working with the product leaders and managers. Actively participate in execution and some planning of organizational data team strategy. 
• Collaborate with a team of product managers, analysts, and other developers to define and lead data projects from data ingestion to analysis to recommendations.

About you

• At least 2 years of professional experience as a data analyst or a data scientist.
• Knowledge of statistical concepts, especially exploratory data analysis techniques, and probability theory. 
• Excellent SQL skills to create and evaluate complex statements involving numerous tables and data relationships.
• Excellent visualization and storytelling skills to explain your results and solutions to the stakeholders, clearly and compellingly.
• Proficient in using Python data analysis libraries such as Pandas and Numpy. Also has a basic understanding of the object-oriented programming concepts as it relates to Python.
• Prior exposure to distributed data processing concepts and execution (e.g., working with column stores, leveraging spark, etc.)
• Fast learner; curiosity about and passion for data.

Compensation

The US total compensation range for this full-time position is $110,000 - $150,000 (inclusive of salary + equity). Our ranges are determined by role and level. The range displayed on each job posting reflects the approximate total target compensation for the position across the US. Within the range, individual pay is determined by factors including relevant skills, experience, and education/training. Please note that the compensation listed does not include benefits.

Benefits

Doximity is proud to offer industry-leading benefits. Some of our offerings include: 

• Medical, dental, vision offerings for you and your family
• 401k with matching program
• Employee stock purchase plan
• Family planning support, Childcare FSA, and parental leave
• Life, AD&D, and Disability
• Generous time off, holidays and paid company trips
• Wellness benefits…plus many more!

More info on Doximity

For the past decade, it’s been our mission to help every physician be more productive so they can provide better care for their patients. We believe that when doctors are connected, the healthcare system works better and patients benefit. Doximity enables our verified clinician members to collaborate with colleagues, stay up-to-date with the latest medical news and research, manage their careers, and conduct virtual patient visits. Today, Doximity is the leading digital platform for U.S. medical professionals, with over 80% of physicians, 50% of all nurse practitioners and physician assistants, and 90% of graduating medical students as members. 

Joining Doximity means being part of an incredibly talented and humble team passionate about improving inefficiencies in our $4.3 trillion U.S. healthcare system. We are a team of doers who solve problems everyday by treating obstacles like an adventure, and we love creating technology that has a real, meaningful impact on people’s lives. Doxers are committed to working towards a more equitable world both within and beyond our office walls. This starts by fostering an inclusive and diverse work environment where differences are valued and all employees are encouraged to bring their full, authentic selves to work daily. To learn more about our team, culture, and users, check out our careers page, company blog, and engineering blog. We’re growing fast, and there’s plenty of opportunity for you to make an impact—join us! For more information, visit Doximity.com.

____________________________________________

EEOC Statement

Doximity is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.

Doximity is transforming the healthcare industry. Join our mission to help every physician be more productive and provide better care for their patients. As medicine's largest network in the United States, there's an elevated level of responsibility in everything we do. We don't take that responsibility lightly and are committed to building diverse teams with an inclusive culture that can make a direct impact on the healthcare system.

One of Doximity's core values is stretching ourselves. Even if you don't check off all the boxes below we encourage you to apply. Doximity is full of exceptional people who bring their own unique experiences to work everyday and make us all better for it!

As a Data Analyst, you'll work within cross-functional delivery teams alongside other analysts, engineers, and product managers in discovering data insights to help improve healthcare.

This role can be filled in our San Francisco headquarters OR remotely in the U.S.

About Us

• Here are some of the ways we bring value to doctors
• Here is an introduction to our tech stack 
• We use UNIX command-line interface and standard programming tools (vim/emacs, git, etc.) and have over 350 private repositories in Github containing our applications, forks of gems, our own internal gems, and open-source projects
• We have worked as a distributed team for a long time; we're currently about 65% distributed
• Find out more information on the Doximity engineering blog
• Our company core values
• Our recruiting process
• Our product development cycle
• Our on-boarding & mentorship process

Here's How You Will Make an Impact

• Leverage Doximity's extensive datasets to identify and classify behavioral patterns of medical professionals on our platform.
• Play a key role in creating both product and client-facing analytics.
• Inform data team strategy by working with the product leaders and managers. Actively participate in execution and some planning of organizational data team strategy. 
• Collaborate with a team of product managers, analysts, and other developers to define and lead data projects from data ingestion to analysis to recommendations.

About you

• At least 2 years of professional experience as a data analyst or a data scientist.
• Knowledge of statistical concepts, especially exploratory data analysis techniques, and probability theory. 
• Excellent SQL skills to create and evaluate complex statements involving numerous tables and data relationships.
• Excellent visualization and storytelling skills to explain your results and solutions to the stakeholders, clearly and compellingly.
• Proficient in using Python data analysis libraries such as Pandas and Numpy. Also has a basic understanding of the object-oriented programming concepts as it relates to Python.
• Prior exposure to distributed data processing concepts and execution (e.g., working with column stores, leveraging spark, etc.)
• Fast learner; curiosity about and passion for data.

Compensation

The US total compensation range for this full-time position is $110,000 - $150,000 (inclusive of salary + equity). Our ranges are determined by role and level. The range displayed on each job posting reflects the approximate total target compensation for the position across the US. Within the range, individual pay is determined by factors including relevant skills, experience, and education/training. Please note that the compensation listed does not include benefits.

Benefits

Doximity is proud to offer industry-leading benefits. Some of our offerings include: 

• Medical, dental, vision offerings for you and your family
• 401k with matching program
• Employee stock purchase plan
• Family planning support, Childcare FSA, and parental leave
• Life, AD&D, and Disability
• Generous time off, holidays and paid company trips
• Wellness benefits…plus many more!

More info on Doximity

For the past decade, it’s been our mission to help every physician be more productive so they can provide better care for their patients. We believe that when doctors are connected, the healthcare system works better and patients benefit. Doximity enables our verified clinician members to collaborate with colleagues, stay up-to-date with the latest medical news and research, manage their careers, and conduct virtual patient visits. Today, Doximity is the leading digital platform for U.S. medical professionals, with over 80% of physicians, 50% of all nurse practitioners and physician assistants, and 90% of graduating medical students as members. 

Joining Doximity means being part of an incredibly talented and humble team passionate about improving inefficiencies in our $4.3 trillion U.S. healthcare system. We are a team of doers who solve problems everyday by treating obstacles like an adventure, and we love creating technology that has a real, meaningful impact on people’s lives. Doxers are committed to working towards a more equitable world both within and beyond our office walls. This starts by fostering an inclusive and diverse work environment where differences are valued and all employees are encouraged to bring their full, authentic selves to work daily. To learn more about our team, culture, and users, check out our careers page, company blog, and engineering blog. We’re growing fast, and there’s plenty of opportunity for you to make an impact—join us! For more information, visit Doximity.com.

____________________________________________

EEOC Statement

Doximity is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.

Location: San Francisco, CA (Hybrid) 

What is Verse? 

Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world’s largest energy buyers, managing energy has never been more complex or more critical.

Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs.

The Role

Verse is seeking a Senior Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and Behind-The-Meter (BTM) battery storage. You will play a critical role in shaping our modeling and analytical data layer, leveraging machine learning techniques, supporting optimization modeling infrastructure, and developing scalable approaches to power Verse’s software offerings.

This position emphasizes strong technical depth in Python, machine learning, and analytical modeling, along with the ability to independently scope and execute complex projects end-to-end. Experience in electricity markets and energy systems is preferred.

Key Responsibilities

• Lead End-to-End Data Science Projects: Own and drive large projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions with minimal oversight.
• Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models, optimization-linked models) to support decision-making and enhance product capabilities.
• Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured analytical data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets, with an emphasis on dimensional modeling and reproducibility (e.g., dbt-style workflows).
• Software Development & Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems and model deployment pipelines in a cloud-based environment.
• Exploratory Data Analysis & Insight Generation: Apply statistical methods and data exploration techniques to uncover insights, validate assumptions, and inform modeling approaches.
• Machine Learning and MLOps: Contribute to Verse’s machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production.
• Cross-Functional Collaboration: Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows.
• Technical Leadership: Mentor junior team members, contribute to best practices, and help shape the technical direction of modeling and analytics across the team.

What We're Looking For (Minimum Qualifications)

• Master’s degree or higher in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field. A bachelor’s degree with significant relevant experience may be considered.
• 5+ years of professional experience in data science, machine learning, or a related field
• Proven track record of independently leading and delivering complex modeling or data science projects
• Experience deploying and maintaining models in production environments
• Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
• Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation
• Hands-on experience in complex transformations, dimensional modeling, and translating analytical requirements into well-structured, tested, and documented models

What Will Make You Standout (Preferred Qualifications)

• Experience in energy, climate tech, or related domains (not required)
• Familiarity with optimization methods or operations research
• Experience with real-time or streaming data systems
• Prior experience mentoring or leading technical teams
• PhD in a quantitative field

What Makes Verse a Great Place to Work? 

• Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first
• Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders
• Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
• Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$190,000 - $230,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks 

• Competitive compensation and equity grant at a high-growth start up 
• Comprehensive benefits package including medical, dental and vision insurance, and 401k 
• Flexible hours and unlimited PTO 
• Diverse and inclusive working environment 

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.

Overview

We are looking for interns to join Instacart’s Economics team. The ideal candidate for this role will bring a combination of experience in both economics and machine learning. We are in particular looking for current or recently graduated PhD students in economics or related fields like marketing, finance, or operations research. Candidates should bring some relevant research experience, typically in computationally intensive empirical topics, as well as some exposure to machine learning coursework and applications.

The Economics team at Instacart works on a range of interesting and challenging problems at the intersection of machine learning and economics, from aligning the incentives in our multi-sided marketplace to analyzing the impact of behavioral nudges on our customers’ and shoppers’ decisions. Some of the core areas of focus for our team include online advertising, uplift and long term value modeling, logistics, marketplace optimization (consumers, shoppers, retailers), inventory intelligence, and general causal inference. You can find more information in our blog post that introduces the team and the type of work we do.

About the Job

• You will help design and build end-to-end machine learning solutions.
• You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects.
• You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains.
• You will develop high impact solutions to support Instacart's ambitious growth plans.
• You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments.
• You will have the freedom to suggest and drive organization-wide initiatives.

About You

Minimum Qualifications

• Current or recently graduated PhD student in economics or a related field with focus on data-intense problems.
• A blend of economic theory, applied econometrics, and business acumen that let you jump into a fast-paced environment and contribute from day one.
• Expertise in causal inference with observational and experimental data.
• Expertise in Python or R and fluency in data manipulation (SQL, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools.
• Self-motivation and a strong sense of ownership

Overview

As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.

The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.

The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.

About the Job

• Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.
• Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.
• Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.
• Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.
• Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.
• Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.
• Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.

About You

Minimum Qualifications

• PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.
• 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.
• Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations.
• Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation. Ability to reason about selection bias, position bias, and propensity-based correction methods.
• Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX). Fluency in data manipulation tools (SQL, Spark, Pandas).
• Track record of formulating ambiguous problems into well-scoped ML research directions and delivering results through rigorous experimentation.
• Strong written and verbal communication skills. Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists.

Preferred Qualifications

• Experience in ads ranking or auction-based systems (pCTR, bid optimization, ROAS feedback loops, marketplace dynamics).
• Hands-on experience with autoregressive sequence models for user behavior prediction, generative retrieval, or transformer-based ranking architectures.
• Familiarity with learned representations such as Semantic IDs, product embeddings, or other approaches to reducing feature cardinality and cold-start challenges.
• Experience with transfer learning or domain adaptation techniques (e.g., LoRA, adapter-based fine-tuning) applied to recommendation or ranking models.
• Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR, or similar).
• Experience mentoring junior engineers or shaping technical direction for a modeling team.
• Familiarity with LLM-driven approaches to recommendation, including prompt-based personalization and AI-assisted model development (AutoML).

About Snorkel

We’re on a mission to democratize AI by building the definitive AI data development platform. The AI landscape has gone through incredible change between 2016, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

As an Applied AI Engineer (Pre-Sales), you will partner closely with Sales and our AI Solution Practice Leaders during pre-sales engagements. You will lead technical discovery and solution scoping to identify high-value use cases, and develop tailored demos and pilots that demonstrate how modern GenAI and machine learning techniques can drive measurable business outcomes. You will also translate proposed solutions into clearly scoped, technically sound proposals and Statement of Works (SOWs) that set realistic expectations and position engagements for success, while collaborating with delivery teams during initial implementation.

This role sits at the intersection of deep technical problem-solving and customer partnership. You will help customers unpack complex business challenges, quickly prototype solutions, and clearly communicate technical approaches to both engineering teams and senior business stakeholders. From early architecture discussions to demo development, you will play a key role in helping organizations adopt AI effectively while surfacing field insights that inform our product and research roadmap.

Main Responsibilities

• Partner closely with Sales and our AI Solution Practice Leaders to shape technical strategy across active prospects, win technical evaluations, and ensure a seamless transition to post-sales delivery through structured handoff documentation and clear expectation alignment.
• Lead structured technical discovery engagements with prospects to understand business objectives, success criteria, data landscape, architectural constraints, security considerations, and organizational readiness.
• Translate discovery findings into well-defined GenAI solution architectures that demonstrate technical feasibility and business impact using internal frameworks, and complementary third-party technologies.
• Design, build and deliver bespoke demos, including evaluation pipelines, dataset strategy, retrieval-augmented generation systems, fine-tuning workflows, prompt engineering strategies, and agentic architectures tailored to specific customer use cases and senior business stakeholders.
• Author and contribute to custom proposals, including Statements of Work and RFP/RFI responses, ensuring scope clarity, architectural soundness, realistic effort estimates, and alignment with delivery capabilities.
• Systematically capture reusable patterns from bespoke demos and evolve them into reference architectures, demo assets, benchmarks, and internal solution playbooks.
• Annual travel up to 25%.

Preferred Qualifications

• B.S. in Computer Science, Engineering, Math/Statistics, or equivalent experience.
• 8+ years in customer-facing technical roles (pre-sales, solutions engineering, or applied AI), including discovery, scoping, demos, proof-of-value engagements, and RFP responses.
• Hands-on Python experience and familiarity with the modern Gen AI stack -  LLM ecosystems, RAG, vector databases, data processing, synthetic dataset curation, evaluation workflows, LLM orchestration and agent authoring tools.
• Expertise across the predictive ML stack, including classical ML (e.g., scikit-learn) and data processing frameworks (e.g., pandas, Spark).
• Ability to operate in fast-paced environments and rapidly build prototypes (ML solutions, RAG systems, prompt-based workflows, fine-tuned models, agentic systems) that demonstrate business value.
• Able to translate ambiguous business problems into testable technical approaches and measurable success criteria.
• Strong presentation and storytelling skills with the ability to engage both technical and executive audiences with credibility.
• Experience estimating scope and producing technical content for proposals and Statements of Work.

Compensation range is $190K - $300K OTE. All offers also include equity in the form of employee stock options. Our compensation ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Locations

New York; San Francisco Bay Area; Austin; Dallas; Seattle; Atlanta; Nashville; Boston; Remote - US

#LI-CG1

Want to help us help others? We’re hiring! 

GoFundMe is the world’s most powerful community for good, dedicated to helping people help each other. By uniting individuals and nonprofits in one place, GoFundMe makes it easy and safe for people to ask for help and support causes—for themselves and each other. Together, our community has raised more than $40 billion since 2010.

We’re looking for a Data Science Manager to architect and lead the next generation of marketing data science at GoFundMe. This role will build and scale the foundations of applied data science and AI that empower our Marketing, Growth, and Finance teams to make high-confidence, ROI-positive investment decisions. You’ll serve as a player-coach for a talented group of experienced data scientists, driving innovation while ensuring excellence in delivery.

Candidates considered for this role will be located in the San Francisco, Bay Area. There will be an in-office requirement of 3x a week.

The Job

• Build a strong AI and data science foundation: Develop scalable pipelines, reusable modeling frameworks, and robust experimentation platforms to support marketing and growth decision-making.
• Lead end-to-end data science & AI projects: From requirements gathering through feature engineering, modeling, validation, deployment, and monitoring.
• Establish best practices: Champion standards in model governance, reproducibility, data quality, and system reliability to ensure sustainable and trustworthy AI adoption.
• Drive marketing science innovation: Apply advanced methods—causal inference, uplift modeling, multi-touch attribution, and media mix modeling—to unlock insights and optimize spend.
• Advance forecasting & ROI modeling: Deliver budget allocation frameworks and predictive models that guide long-term roadmap planning and marketing efficiency.
• Partner cross-functionally: Work closely with Marketing, Growth, Product, Engineering, and Finance leaders to align analytics initiatives with revenue impact.
• Invest in people: Mentor, coach, and elevate a team of high-performing data scientists; foster a culture of technical rigor, curiosity, and applied innovation.
• Push the frontier of applied AI in marketing: Evaluate emerging generative and predictive AI approaches for audience segmentation, creative optimization, personalization, and campaign efficiency.

You

Experience & Education

• 8+ years of experience in data science roles with direct impact on marketing, growth, or revenue optimization.
• Master’s or Ph.D. in a quantitative field (Statistics, Mathematics, Economics, Computer Science, Physics, Operations Research or related), or equivalent applied experience.

Technical Skills

• Advanced proficiency in Python (NumPy, pandas, scikit-learn) and SQL (window functions, optimization). (Please note, advance python and SQL proficiency will be tested during the interview process for this position.) 
• Deep experience with experimentation frameworks: A/B testing, causal inference, uplift modeling, and attribution models.
• Proven success in forecasting, optimization, and budget allocation models for marketing and growth functions.
• Hands-on with data platforms (Snowflake, Databricks) and BI tools (Looker, Tableau, or equivalent).
• Strong data storytelling and executive presentation abilities.

Leadership & Collaboration

• Exceptional communication skills with the ability to influence executive stakeholders and translate data into actionable business recommendations.
• Experience developing senior data scientists and elevating team practices.
• Demonstrated ability to define a strategic vision for applied data science in marketing, balancing rapid experimentation with long-term infrastructure investments.

Preferred 

• Familiarity with experimentation and web/mobile analytics platforms (Optimizely, GrowthBook, Google Analytics, Amplitude).
• Experience integrating with marketing APIs (Google, Meta, programmatic platforms) for campaign optimization.
• Prior exposure to generative AI or LLMs in marketing use cases (e.g., personalization, targeting, creative analysis).
• Knowledge of multi-arm and contextual bandit algorithms for adaptive experimentation and continuous marketing optimization.
• Familiarity with ML ops practices: version control, model monitoring, scalable ETL frameworks.

Why you’ll love it here

• Make an Impact: Be part of a mission-driven organization making a positive difference in millions of lives every year.
• Innovative Environment: Work with a diverse, passionate, and talented team in a fast-paced, forward-thinking atmosphere.
• Collaborative Team: Join a fun and collaborative team that works hard and celebrates success together.
• Competitive Benefits: Enjoy competitive pay and comprehensive healthcare benefits.
• Holistic Support: Enjoy financial assistance for things like hybrid work, family planning, along with generous parental leave, flexible time-off policies, and mental health and wellness resources to support your overall well-being.
• Growth Opportunities: Participate in learning, development, and recognition programs to help you thrive and grow.
• Commitment to DEI: Contribute to diversity, equity, and inclusion through ongoing initiatives and employee resource groups.
• Community Engagement: Make a difference through our volunteering program.

We live by our core values: impatient to be great, find a way, earn trust every day, fueled by purpose. Be a part of something bigger with us!

GoFundMe is proud to be an equal opportunity employer that actively pursues candidates of diverse backgrounds and experiences.  We do not discriminate on the basis of race, color, religion, ethnicity, nationality or national origin, sex, sexual orientation, gender, gender identity or expression, pregnancy status, marital status, age, medical condition, mental or physical disability, or military or veteran status.

The total annual salary for this full-time position is $202,000 - $303,000 + equity + benefits. The company also offers equity and other benefits to employees, including healthcare, dental, vision, life insurance and 401(k) saving program. In addition to this wage, there are geolocation differentials that will increase pay depending on the work location. Additionally pay may vary depending on other factors including skills, experience, education, or training. Your recruiter can share more about the specific total compensation package based on your location during the hiring process. 

If you require a reasonable accommodation to complete a job application or a job interview or to otherwise participate in the hiring process, please contact us at accommodationrequests@gofundme.com. 

Global Data Privacy Notice for Job Candidates and Applicants:

Depending on your location, the General Data Protection Regulation (GDPR) or certain US privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available here. By submitting your application, you are agreeing to our use and processing of your data as required. 

Learn more about GoFundMe:

We’re proud to partner with GoFundMe.org, an independent public charity, to extend the reach and impact of our generous community, while helping drive critical social change. You can learn more about GoFundMe.org’s activities and impact in their FY ‘25 annual report.

Our annual “Year in Help” report reflects our community’s impact in advancing our mission of helping people help each other.

For recent company news and announcements, visit our Newsroom.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We use Metaview as part of our hiring process for jobs in NYC and certain features may qualify it as an automated employment decision tool (AEDT), as defined by New York City Local Law 144. As part of the hiring process, we provide Metaview with job requirements and candidate submitted resumes and application materials to assist in evaluating job-related qualifications. While this tool is used to improve efficiency and support our recruiting personnel, all final hiring decisions are made by GoFundMe employees.

We began using Metaview on March 10, 2026. The Metaview tool has been reviewed by an independent auditor. Results of the audit may be viewed here. The tool evaluates your professional experience, technical skills, education, and other qualifications using information collected directly from your submitted resume and application materials; all such data is retained in accordance with GoFundMe’s Personnel Privacy Notice and applicable legal requirements. If you would like to request an alternative selection process or a reasonable accommodation, please contact accommodationrequests@gofundme.com.

Want to help us help others? We’re hiring! 

GoFundMe is the world’s most powerful community for good, dedicated to helping people help each other. By uniting individuals and nonprofits in one place, GoFundMe makes it easy and safe for people to ask for help and support causes—for themselves and each other. Together, our community has raised more than $40 billion since 2010.

We’re looking for a Staff Data Scientist, Pricing to serve as the senior individual contributor driving the science, strategy, experimentation and AI deployment behind pricing and yield optimization at GoFundMe. This role sits at the intersection of economics, behavioral science, experimentation, and machine learning, with direct responsibility for optimizing donation conversion, donation amounts, and donor experience across the product.

Candidates considered for this role will be located in the San Francisco Bay Area. There will be an in-office requirement of 3x a week.

The Job

• Own donation pricing and amount optimization end-to-end: Define the analytical strategy, modeling frameworks, and success metrics for pricing recommendations across product surfaces, balancing conversion, donation amounts, and long-term donor trust.
• Model human behavior using economics and AI: Apply economic theory, behavioral science, and machine learning to understand donor decision-making, estimate elasticity, and predict responses to changes in product design and choice architecture.
• Leverage non-transactional behavioral signals: Model sparse and indirect signals (e.g., navigation, hesitation, context, device, timing) to detect shifts in intent and interaction patterns beyond observed transactions.
• Build adaptive and reinforcement-aware systems: Design models that learn over time using experimentation signals, feedback loops, and reinforcement concepts (e.g., contextual bandits or sequential decision-making) where appropriate.
• Lead experimentation and causal learning: Partner with Product and Engineering to design robust experimentation and measurement frameworks, ensuring pricing and donation models are causal-aware, interpretable, and safe to deploy at scale.
• Incorporate external data and context: Augment behavioral models with external datasets (macroeconomic indicators, seasonality, regional or temporal signals) to better understand and anticipate donor behavior.
• Translate insights into action: Convert complex economic and behavioral analyses into deployable models, clear product recommendations, and measurable business impact.
• Influence through storytelling and leadership: Communicate insights effectively to senior leaders, humanize donor behavior through narrative, and serve as a trusted thought partner on pricing and donation strategy.
• Raise the technical bar: Set best practices for modeling rigor, validation, monitoring, and iteration; mentor other data scientists and elevate pricing science across the organization.

You

Experience & Education

• Either a Ph.D. in Economics, Applied Economics, or a closely related quantitative field, demonstrating the ability to push the boundaries of applied research and translate theory into practical modeling approaches OR 8+ years of industry experience in data science, applied economics, pricing, marketplace optimization, or monetization at a high-tech digital company, with a proven track record of owning and scaling pricing or decisioning systems.
• Deep experience applying economic reasoning, causal inference, and behavioral modeling to real-world decision-making problems.
• Demonstrated ability to own ambiguous, high-impact problems and deliver measurable business outcomes.

Core Skills

• Strong foundation in econometrics, causal inference, and behavioral modeling.
• Deep understanding of price elasticity, choice modeling, and decision science.
• Experience modeling noisy, sparse, or non-transactional behavioral data.
• Hands-on experience designing and interpreting experiments and causal signals.
• Familiarity with reinforcement learning, bandits, or adaptive optimization concepts (applied or research-driven).

Technical Skills

• Advanced proficiency in Python (pandas, NumPy, scikit-learn, PyMC/Stan or equivalent) and SQL, with the ability to build, validate, and iterate on complex analytical and modeling workflows.
• Demonstrated ability to leverage modern AI tools and coding agents (e.g., LLM-based assistants, autonomous or semi-autonomous coding agents, model-driven feature generation, synthetic data generation) to accelerate research, prototyping, and productionization of models.
• Experience designing or applying LLM-based or AI-assisted solutions to complex decisioning problems (e.g., feature extraction from unstructured data, rapid experimentation, simulation, or model orchestration), beyond basic prompt usage.

Leadership & Collaboration

• Exceptional ability to tell clear, compelling stories from complex data.
• Comfortable influencing product direction and executive decision-making.
• Demonstrated ability to lead without authority and elevate team practices.

Preferred but not required

• Experience in consumer pricing, marketplaces, or digital payments/donations.
• Experience partnering with engineering to productionize models and AI-driven systems, including monitoring, evaluation, and iteration in live environments.
• Familiarity with modern data platforms (Snowflake, Databricks) and experimentation infrastructure; experience with model versioning and validation is a plus.
• Strong data visualization, documentation, and presentation skills, with an emphasis on clarity and executive-ready communication.

Why you’ll love it here

• Make an Impact: Be part of a mission-driven organization making a positive difference in millions of lives every year.
• Innovative Environment: Work with a diverse, passionate, and talented team in a fast-paced, forward-thinking atmosphere.
• Collaborative Team: Join a fun and collaborative team that works hard and celebrates success together.
• Competitive Benefits: Enjoy competitive pay and comprehensive healthcare benefits.
• Holistic Support: Enjoy financial assistance for things like hybrid work, family planning, along with generous parental leave, flexible time-off policies, and mental health and wellness resources to support your overall well-being.
• Growth Opportunities: Participate in learning, development, and recognition programs to help you thrive and grow.
• Commitment to DEI: Contribute to diversity, equity, and inclusion through ongoing initiatives and employee resource groups.
• Community Engagement: Make a difference through our volunteering program.

We live by our core values: impatient to be great, find a way, earn trust every day, fueled by purpose. Be a part of something bigger with us!

GoFundMe is proud to be an equal opportunity employer that actively pursues candidates of diverse backgrounds and experiences.  We do not discriminate on the basis of race, color, religion, ethnicity, nationality or national origin, sex, sexual orientation, gender, gender identity or expression, pregnancy status, marital status, age, medical condition, mental or physical disability, or military or veteran status.

The annual U.S. salary range for this full-time position is $179,500 - $269,500. The company also offers equity and other benefits to employees, including healthcare, dental, vision, life insurance and 401(k) saving program. In addition to this wage, there are geolocation differentials that will increase pay depending on the work location. Additionally pay may vary depending on other factors including skills, experience, education, or training. Your recruiter can share more about the specific total compensation package based on your location during the hiring process.

If you require a reasonable accommodation to complete a job application or a job interview or to otherwise participate in the hiring process, please contact us at accommodationrequests@gofundme.com. 

Global Data Privacy Notice for Job Candidates and Applicants:

Depending on your location, the General Data Protection Regulation (GDPR) or certain US privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available here. By submitting your application, you are agreeing to our use and processing of your data as required. 

Learn more about GoFundMe:

We’re proud to partner with GoFundMe.org, an independent public charity, to extend the reach and impact of our generous community, while helping drive critical social change. You can learn more about GoFundMe.org’s activities and impact in their FY ‘25 annual report.

Our annual “Year in Help” report reflects our community’s impact in advancing our mission of helping people help each other.

For recent company news and announcements, visit our Newsroom.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We use Metaview as part of our hiring process for jobs in NYC and certain features may qualify it as an automated employment decision tool (AEDT), as defined by New York City Local Law 144. As part of the hiring process, we provide Metaview with job requirements and candidate submitted resumes and application materials to assist in evaluating job-related qualifications. While this tool is used to improve efficiency and support our recruiting personnel, all final hiring decisions are made by GoFundMe employees.

We began using Metaview on March 10, 2026. The Metaview tool has been reviewed by an independent auditor. Results of the audit may be viewed here. The tool evaluates your professional experience, technical skills, education, and other qualifications using information collected directly from your submitted resume and application materials; all such data is retained in accordance with GoFundMe’s Personnel Privacy Notice and applicable legal requirements. If you would like to request an alternative selection process or a reasonable accommodation, please contact accommodationrequests@gofundme.com.

Navan is seeking a Senior Applied Economist to join the Data Science & Machine Learning team. This is a foundational, "first-of-its-kind" role at Navan, designed for a technical leader who can bridge the gaps between hands-on machine learning, rigorous economic theory, and driving business outcomes.

In this role, you will be the primary architect of our internal economic "brain." You will move beyond point-estimate forecasting to build sophisticated models that account for market nuances, uncertainty, and causal drivers. You will partner closely with Finance, Treasury, and FP&A to steer the company’s financial trajectory, while providing the strategic frameworks that Sales and Pricing teams use to maximize customer adoption and revenue.

What You’ll Do:

• Next-Generation Forecasting: Uplevel our existing forecasting pipelines (currently built on Prophet). You will integrate econometric rigor to improve accuracy and, crucially, provide a range of likely outcomes (probabilistic forecasting) that Finance and Treasury can rely on for risk management.
• Causal Inference & Strategy: Design and execute experimental and quasi-experimental frameworks to identify the "levers" of the business. You will answer critical questions regarding price elasticity, product feature attribution, and the ROI of sales incentives.
• Strategic Blueprinting: Partner with Sales and Account Management to create data-driven frameworks for pricing and customer retention. You will translate complex causal models into actionable blueprints for go-to-market teams.
• Production-Level Data Science: Work hands-on within our ML infrastructure. You will write production-quality Python code to deploy models into our AWS and Snowflake-based ecosystem, ensuring your insights are automated and scalable.
• Internal Advisory: Act as the subject matter expert on economic literature and methodology, translating technical findings into strategic recommendations for executive leadership.

What We’re Looking For:

• Education: An advanced degree (PhD preferred, Masters required) in Economics, Statistics, or a related quantitative field with a heavy emphasis on econometrics or causal inference.
• Experience: 4+ years of post-academic experience in an applied research, finance, or data science role, ideally within a high-growth tech environment or fintech.
  • Technical Proficiency:
    • Deep expertise in Python and its data science ecosystem (pandas, statsmodels, scikit-learn, etc.).
    • Advanced SQL skills, with experience querying large-scale data warehouses like Snowflake.
    • Experience working in production environments and a strong understanding of the ML lifecycle is nice to have.
• Econometric Mastery: Proven ability to apply advanced methods (e.g., Synthetic Control, IV, Diff-in-Diff, Structural Modeling) to messy, real-world datasets.
• Self-Starter Mentality: Experience functioning in "underdefined" spaces. As our first economist, you must be comfortable setting the roadmap.
• Communication: The ability to explain not just the "what," but the "why" and the "what if." You can communicate uncertainty and risk to a CFO just as clearly as you can discuss model architecture with an ML Engineer.
• Preferred Qualifications:
  • Prior experience in Fintech, Payments, or Travel industries.
  • Experience building and scaling "first-of-their-kind" functions within a data organization.

While candidates in the listed location(s) are encouraged for this role, candidates in other US locations will be considered. #LI-REMOTE

As a Solutions Architect on the Digital Natives team, you will work with the leading data engineering, data science & ML teams in the market to push the boundaries of what big data architectures are capable of. 

Reporting to the Field Engineering Manager, you will collaborate with strategic customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

We are looking for high technical aptitude individuals with a deep sense of ownership and a desire to help customers ship solutions at production scale. Ideal candidates are deeply curious, capable of operating with confidence in ambiguous situations, and are extremely adaptable. 

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Drive technical discovery and solution design, focusing on winning competitive deals and accelerating time-to-value in strategic accounts.
• Continuously research & learn new technologies and their implementations on Databricks
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our customers
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.
• Work closely with account executives to create and execute account penetration strategies, focusing on winning technical decision-makers and building new customer champions.
• Build trusted advisor relationships with senior and executive stakeholders by articulating the business value of Databricks in clear, outcomes-driven terms.
  
  

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems
• Comfortable programming in, and debugging, Python and SQL
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
• Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Strong executive presence with the ability to influence C/VP-level stakeholders and align technical solutions to strategic business priorities.
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification
  
  

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply. We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

P-344

At Databricks, our customers challenge us with solving some of the world's toughest data problems. Our customers rely on our Solutions Architect team to understand the technologies that power our platform, the broader cloud and technology ecosystems, and how they can all be brought together to solve these problems.

Solutions Architects at Databricks lead the adoption of the Databricks Unified Analytics Platform. As a member of our team, you will sharpen your skills on open-source projects such as Apache Spark™, MLflow, and Delta Lake to advocate the Lakehouse paradigm on public cloud providers. You will gain expertise in cloud platforms, data engineering, data analytics, data science, and machine learning. In this customer-facing role, you will identify use cases for Databricks, develop architectures and solutions using our platform, and guide customers through implementations to deliver strategic business value. You will partner with the sales team to build relationships with our customers and establish yourself as a trusted advisor.

Databricks' culture thrives on collaboration and teamwork, and you will contribute your knowledge to our teams by participating in our subject matter expert groups. In this role, you will report to the Solutions Architect Leader supporting the specific business segment.

If you are a technologist interested in Data and AI, and enjoy prototyping solutions for customers, this role is for you.

The impact you will have:

• Provide technical leadership for customers to evaluate and adopt Data and AI solutions from Databricks
• Consult on big data architecture, implement proof of concepts for strategic customer projects, data science and machine learning projects, and validate integrations with cloud services and other 3rd party applications
• Build and present reference architectures, technical guides, and demo applications for customers
• Provide escalated support for critical customer operational issues
• Become an expert in, and evangelize Databricks driven open-source projects (Apache Spark™, Delta Lake, MLflow, Koalas) across developer communities through meetups, conferences, and webinars
• Use your strengths to help your fellow SA's, and drive cross-functional relationships across the company
• Travel to customers (up to 30%, of which 80%+ local to your region)

What we look for:

Solution Architects are customer-facing. You'll need to be comfortable communicating ideas to various audiences through presentations, whiteboarding, architecture discussions, and platform demonstrations.

• 3+ years in a customer-facing pre-sales, technical architecture, or consulting role
• Experience designing and architecting distributed data systems
• Comfortable programming in and debugging at least one of Python, Scala, Java, SQL, or R
• Have built solutions with public cloud providers, such as AWS, Azure, or GCP
• Experience in at least one of the following:
  • Data Engineering technologies (e.g., Spark, Hadoop, Kafka)
  • Data Warehousing (e.g., SQL, OLTP/OLAP/DSS)
  • Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, HPO)
• [Preferred] Degree in a quantitative discipline (e.g., Computer Science, Applied Mathematics, Operations Research, etc.)
• Nice to have: Databricks Certification

P-345

At Databricks, our customers challenge us with solving some of the world's toughest data problems. Our customers rely on our Solutions Architect team to understand the technologies that power our platform, the broader cloud and technology ecosystems, and how they can all be brought together to solve these problems.

Solutions Architects at Databricks lead the adoption of the Databricks Unified Analytics Platform. As a member of our team, you will sharpen your skills on open-source projects such as Apache Spark™, MLflow, and Delta Lake to advocate the Lakehouse paradigm on public cloud providers. You will gain expertise in cloud platforms, data engineering, data analytics, data science, and machine learning. In this customer-facing role, you will identify use cases for Databricks, develop architectures and solutions using our platform, and guide customers through implementations to deliver strategic business value. You will build relationships with our customers and establish yourself as a trusted advisor.

Databricks' culture thrives on collaboration and teamwork, and you will contribute your knowledge to our teams by participating in our subject matter expert groups. In this role, you will report to the Solutions Architect Leader supporting the specific business segment.

If you are a technologist interested in Data and AI, and enjoy prototyping solutions for customers, this role is for you.

The impact you will have:

• Provide technical leadership for customers to evaluate and adopt Data and AI solutions from Databricks
• Consult on big data architecture, implement proof of concepts for strategic customer projects, data science and machine learning projects, and validate integrations with cloud services and other 3rd party applications
• Build and present reference architectures, technical guides, and demo applications for customers
• Provide escalated support for critical customer operational issues
• Become an expert in, and evangelize Databricks driven open-source projects (Apache Spark™, Delta Lake, MLflow, Koalas) across developer communities through meetups, conferences, and webinars
• Use your strengths to help your fellow SA's, and drive cross-functional relationships across the company
• Travel to customers (up to 30%, of which 80%+ local to your region)

What we look for:

Solution Architects are customer-facing. You'll need to be comfortable communicating ideas to various audiences through presentations, whiteboarding, architecture discussions, and platform demonstrations.

• 5+ years in a customer-facing pre-sales, technical architecture, or consulting role
• Experience designing and architecting distributed data systems
• Comfortable programming in and debugging at least one of Python, Scala, Java, SQL, or R
• Have built solutions with public cloud providers, such as AWS, Azure, or GCP
• Experience in at least one of the following:
  • Data Engineering technologies (e.g., Spark, Hadoop, Kafka)
  • Data Warehousing (e.g., SQL, OLTP/OLAP/DSS)
  • Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, HPO)
• [Preferred] Degree in a quantitative discipline (e.g., Computer Science, Applied Mathematics, Operations Research, etc.)
• Nice to have: Databricks Certification

About NewLimit

NewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat age-related diseases by reprogramming the epigenome, a new therapeutic mechanism to restore regenerative potential in aged and diseased cells. We leverage functional genomics, pooled perturbation screening, and machine learning models to unravel the biology of epigenetic aging and disease using experiments of unprecedented scale.

Description

NewLimit is seeking a Computational Biologist to join our Predict team. In this role, you will develop, improve, and operate bioinformatics pipelines supporting large-scale single-cell perturbation screens. Success in this position requires a deep attention to methodological detail at every stage of the analysis, as well as careful measurement and evaluation of pipeline performance.

You will work closely with experimentalists and computational scientists to maintain core infrastructure, assess emerging analysis tools, and build robust software solutions that improve efficiency, scalability, and reliability.

As a member of our team, you will:

• Collaborate with Write, Read, and Predict teams to support the design, execution, and interpretation of experiments using single-cell and multi-omics data
• Contribute to the development and implementation of primary and secondary analysis software.
• Improve and run production pipelines for large-scale single-cell perturbation screens to ensure timely and reliable data delivery
• Contribute to the development of reproducible, well-documented computational workflows and internal best practices
• Develop microservice web applications and integrations that reduce wet lab manual effort and improve data flow between teams

Requirements

• Bachelor’s, Master's, or PhD in Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related technical field
• Experience analyzing and interpreting single-cell or multi-omics datasets (e.g., scRNA-seq, multiome, scATAC-seq), with hands-on exposure to perturbation screens preferred
• Familiarity with core single-cell bioinformatics workflows, including demultiplexing, alignment, and quality control
• Strong software engineering fundamentals and experience working with bioinformatics tools and pipelines
• Working knowledge of statistical and machine learning methods applied to biological data (e.g. regression analysis, supervised learning) sufficient to critically evaluate analytical choices and interpret results conceptually

Nice to haves

• Proficiency with Python-based data science tools (NumPy, SciPy, Pandas)
• Experience with cloud computing environments (AWS, GCP, or Azure)
• Strong Python programming skills and comfort working in Unix-based environments
• Experience with containerized workflows (Docker, Singularity)
• Familiarity with molecular biology workflows and experimental genomics

Benefits

• Health, dental, and vision insurance
• 401(k) with company match
• Industry-leading paid time off (PTO) -- 20 days/year for all employees + holidays
• Paid parental leave
• Meals and snacks on-site

Salary Range: $130,000 to $170,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. NewLimit offers a competitive benefits package, which includes a 401k, healthcare coverage, and 20 days of PTO. Learn more at: https://www.newlimit.com/careers   *This compensation and benefits information is based on Company's good faith estimate as of the date of publication and may be modified in the future.

Overview

The Search & Personalization ML team is Instacart’s engine for state-of-the-art multi-task, multi-objective ranking—unifying search, discovery, recommendation, ads, and merchandising into a single value-aware platform. Partnering with world-class engineers, scientists, and PMs, we build the ranking backbone that powers every pixel of the shopping journey, optimizing not just for clicks, but for incremental GTV, basket lift, and retention over the long run.

What We’re Building

• Foundational Ranking Backbone Models: Multi-task/multi-objective models (shared encoders + task heads) that jointly learn relevance, conversion, margin contribution, churn risk, and ad quality, enabling consistent decisions across search and recommendations.
• Value-Aware Optimization: Uplift and long-horizon value models that steer decisions toward incrementality and LTV, with calibrated constraints on quality, diversity, fairness, and spend pacing—plus guardrails for safe exploration.
• LLM-Enhanced Retrieval & Features: Using LLMs to enrich query and item semantics for long-tail recall, generate features for cold-starts, and feed the ranker with reasoning-rich context, while remaining the source of truth for final ordering.

Our commitment to AI innovation is reflected in our recent publications and research contributions to the field.

About the Job

• Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
• Design and build a search autosuggest system optimized for personalization and value-based relevance.
• Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
• Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
• Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
• Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
• Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
• Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.

About You
Minimum Qualifications

• 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production.
• Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR.
• Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch).
• Excellent analytical skills and strong cross-functional communication abilities.

Preferred Qualifications

• Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration.
• Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference.
• Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate.

At our labor marketplace tech company, we believe that even in the age of AI, the core of success comes down to good people doing honest work. We're seeking a curious and driven Product Operations Analyst to help us operationalize our intelligent product features, ensuring they deliver value and efficiency to our users. This is a fantastic opportunity to grow your career at the intersection of product, data, and user experience.

Who You Are:

• An early-career professional, from a recent graduate up to 2 years of experience, looking to build a career in product operations.
• A Passion for Product & Tech: You are fascinated by how technology products are built, launched, and improved. You're eager to learn and contribute to the product lifecycle.
• Curious about AI: You don't need to be an expert, but you're an enthusiast who is interested in learning about the latest AI technologies and tools and their potential to improve user experiences.
• Analytical & Data-Curious: You have a knack for looking at data to find patterns and answer questions. You're comfortable with spreadsheets (Excel/Google Sheets) and are excited to learn more about data analysis.
• A Problem-Solver: You enjoy tackling challenges and are motivated to find efficient solutions. You're organized, detail-oriented, and not afraid to ask questions to get to the root of a problem.
• Eager to Learn Technical Skills: You have an interest in learning data analysis tools. Any exposure to SQL or other data tools is a plus, but a strong desire to learn is most important.
• Collaborative & Hands-On: You are a team player who is ready to roll up your sleeves and help with a variety of tasks, from testing new features to analyzing user feedback.

What You'll Do:

• Support Product Rollouts & Adoption: Work closely with product, design, and operations teams to help execute outreach and rollout plans for new features.
• Contribute to Product Development: Act as a key tester for new product capabilities. You'll document your findings, share user feedback with the product team, and help identify areas for improvement.
• Analyze Feature Performance: Assist in monitoring the performance of new features by tracking key metrics, analyzing user behavior, and summarizing insights for the team.
• Champion the User Experience: Be a voice for our users. You'll help gather, organize, and analyze user feedback from various channels to inform the product roadmap.
• Streamline Team Operations: Support the team by identifying opportunities to make our processes more efficient, whether through better documentation, improved feedback loops, or simple automations.

Nice To Have:

• Familiarity with Automation Tools: Any experience playing with tools like Zapier or other no-code/low-code platforms is a bonus.
• Data Analysis Exposure: Basic experience with SQL, Python (Pandas), or data visualization tools like Tableau or Looker.
• Internship or Project Experience: Previous internships or relevant project work in a tech, startup, or marketplace environment.
• Customer-Facing Experience: Experience in a role where you directly interacted with users or customers (e.g., customer support, community management).

For CA based applicants:

• The base salary for this position is $80,000 - $100,000
• This position is eligible for equity in the form of stock options
• This position is eligible for Instawork benefits, including:
• A variety of medical, dental, and vision plans with coverage beginning on the date of hire
• Flexible paid time off
• At least 8 paid company holidays annually
• Phone stipend
• Commuter stipend
• Supplemental pay on qualified leaves
• Employee health savings accounts (HSA) contribution
• Flexible spending plans
• 401K plan
• Perkspot - discount program through Lumity

A variety of factors are considered when determining someone’s compensation including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed above.

About Snorkel

We’re on a mission to democratize AI by building the definitive AI data development platform. The AI landscape has gone through incredible change between 2016, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

As an Applied AI Engineer, you’ll research and utilize state-of-the-art Gen AI and machine learning (ML) techniques to successfully deliver solutions to our customers. You will work directly with our customers to understand their business and technical needs and design and deliver AI solutions to solve them - either by leveraging Snorkel Flow or developing custom approaches when needed. You will also help define Snorkel’s Applied AI tooling by translating repeatable real-world challenges into reusable solution recipes, workflows, best practices, and platform-level capabilities that become part of Snorkel Flow’s next generation of AI tooling. We move fast and are constantly prototyping and innovating new ways to deliver value to our customers. This position is ideal for someone who enjoys solving complex problems, bridging the gap between AI technology and business value, working directly with customers, keeping up-to date with AI research, and standardizing bespoke solutions into internal recipes and staying naturally curious about the infrastructure that underpin the Applied AI stack end-to-end.

Main Responsibilities

• Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This may involve leveraging Snorkel Flow or designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of the Snorkel platform.
• Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows.
• Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. A data- and evaluation-first mindset is essential for success in this role.
• Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow.
• Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup.
• Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices.
• Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI.
• Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams.
• Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI.
• Annual travel up to 25%.

Preferred Qualifications

• B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.
• 3+ years of customer-facing experience in the design and implementation of AI/ML solutions.
• Proficiency in Python, including strong grounding in software engineering fundamentals (e.g., modular design, testing, profiling, packaging) and experience with modern Python constructs and libraries for type validation and typed data modeling (e.g., pydantic), building type-safe systems (e.g., mypy), testing (e.g., pytest), packaging and environment configuration (e.g., poetry), API and service frameworks (e.g., FastAPI), serialization and structured data handling (e.g., msgspec), and orchestration tooling relevant to ML deployment (e.g., Ray, Airflow).
• Expertise across the Applied AI stack, spanning classical ML libraries (e.g., scikit-learn), deep learning frameworks (e.g., PyTorch), foundation-model ecosystems (e.g., Hugging Face Transformers), vector/embedding tooling (e.g., FAISS), data processing frameworks (e.g., pandas, Spark), retrieval/RAG tooling (e.g., Chroma, Weaviate), synthetic dataset curation, evaluation workflows, and LLM orchestration, workflow, agent authoring tools (e.g., LlamaIndex, LangGraph, CrewAI).
• Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement.
• Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.
• Ability to work in a fast-paced environment and balance priorities across multiple projects at once.

Compensation range for Tier 1 locations of San Francisco Bay Area $172K - $300K OTE. All offers also include equity in the form of employee stock options. Our compensation ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Locations

Redwood City, CA - Hybrid; San Francisco, CA - Hybrid - US; New York, NY - Hybrid

#LI-CG1

Pantera Capital is the first institutional investment firm focused exclusively on bitcoin, other digital currencies, and companies in the blockchain tech ecosystem. Pantera launched the first cryptocurrency fund in the United States in 2013. The firm subsequently launched the first exclusively-blockchain venture fund. In 2017, Pantera was the first firm to offer an early-stage token fund. Pantera manages $3.5bn across three strategies – passive, hedge, and venture – exclusively focused on bitcoin, other digital currencies, and companies in the blockchain tech ecosystem.

Pantera is hiring a Trader to help execute trades across spot, derivatives, and structured products while managing risk, P&L, and market relationships. This role will help optimize trading strategies, provide real-time insights, and ensure efficient execution and operation. This role requires presence at least three days a week in our San Francisco or New York City office.

Responsibilities:

• Execute trades in digital assets (spot, derivatives, and structured products) across centralized and decentralized venues in line with portfolio manager directives.
• Monitor orders, market conditions, and liquidity to ensure efficient trade execution.
• Provide real-time market color and trading insights to the investment team.
• Manage exposure, risk limits, and P&L related to assigned trading strategies.
• Maintain relationships with counterparties, exchanges, and liquidity providers.
• Support trade settlement, reconciliation, and operational processes to ensure accuracy.
• Track and analyze trading performance, slippage, and transaction costs to improve efficiency.
• Collaborate with researchers, portfolio managers, and operations teams to implement trading strategies.

Qualifications: 

• Minimum of 3–5 years of experience in a trading-related role.
• Familiarity with both traditional finance (equities, derivatives) and crypto-native products (perpetual swaps, staking, spot).
• Comfort with trading APIs and execution management systems.
• Understanding of DeFi protocols and smart contract risk.
• Experience with data analysis libraries (pandas) and SQL for trade/performance analytics.
• High attention to detail while executing at speed.
• Strong communication skills to give clear, concise market updates to PMs and risk committees.
• Entrepreneurial mindset — willingness to experiment with new trading tools and venues.

Compensation:
$150,000 - $200,000 USD*

*Compensation range is provided as an estimate and will be determined based on the candidate's experience and qualifications.