Who We Are

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future.

About the Team

Every vehicle that drives itself on a public road starts with a decision someone made about a single annotation. At Nuro, we take that seriously — and we're looking for someone who does too.

ML Operations is where data becomes decisions. We sit at the intersection of Autonomy Engineering and the globally distributed annotation teams who label the world our vehicles need to understand. We are builders, diagnosticians, and systems thinkers who care deeply about the end product - because we know that what we fix here shows up on the road. 

This team is collaborative and technically rigorous. We work closely with engineering and we hold each other to a high standard, but we also invest in getting things right together. Here is what the work actually looks like day-to-day: 

• Investigate annotation faults by querying databases directly to identify root causes, moving beyond reliance on ticket queues for initial troubleshooting.
• Sitting with Autonomy Engineering leadership, translating a model accuracy regression into a specific labeling workflow problem and walking in with the fix already scoped.
• Auditing a live pipeline, spotting a systematic edge-case misclassification that has been silently propagating, and building the evidence-based case to restructure how that object type gets labeled.
• Manage the processes for quality management/processes for the offshore annotation team
• Being the bridge: the person ML Operations leans on for quality diagnostics, and the person Engineering trusts to understand how a labeling decision upstream shapes what the model learns downstream.

If that sounds like the kind of problem-solving that lights you up, we would love to talk.

About the Role

This is a rare opportunity to own something that genuinely matters. You will co-own a portfolio of active labeling pipelines alongside senior leadership with real authority to set the quality standard, measure against it, and close the gaps. Not to report on pipelines. To make them better.

The work connects directly to Nuro's mission. A single systematic flaw in annotation can propagate silently through training and surface as a safety regression on the road. You are the person who finds it before that happens and who builds the systems that prevent it from happening again. That kind of upstream impact is hard to find in most roles. Here, it's the whole job.

You'll be well-supported: partnering closely with senior engineering and operations leadership, with the access and visibility to do your best work. What we ask in return is curiosity, rigor, and genuine care about getting it right.

About the Work

You will own the quality diagnostic layer across our labeling pipelines: defining the standard, building the instrumentation, and closing the gaps that matter most to model safety and performance.

Own the Quality Standard

• Define what 'good' looks like for each data type across active labeling pipelines and instrument pipelines to measure against it continuously, not just at delivery milestones.
• Build inter-annotator agreement frameworks, taxonomy governance, and sampling methodologies that hold up at offshore production scale.
• Design scalable processes to reduce systematic errors and support evolving ML training requirements.

Diagnose and Close Gaps

• Audit live workflows, query production databases, and trace accuracy failures to their structural root cause then return with an evidence-based plan that fixes the mechanism, not just the symptom.
• Apply statistical process control thinking to distinguish a labeling error from a labeling system error, and drive the changes that address it.

Bridge Engineering and Machine Learning Operations

• Connect ML labeling quality metrics directly to model performance and safety outcomes in close partnership with Autonomy Engineering leadership.
• Build executive-ready reporting that frames quality gaps as safety and business signals — not operational updates.
• Drive alignment across engineering, product, and global ops with clear analysis and well-reasoned recommendations.

About You

We are not looking for someone who has done everything. We are looking for someone who cares about the right things: data quality as a craft, systems thinking as a default, and mission as a motivator. If you are energized by finding what is broken and building what fixes it and you want that work to mean something this role was written for you.

What You Bring

• 5+ years embedded with ML, data operations, or software engineering teams close to the work, not managing from a distance.
• SQL fluency: you can investigate a labeling anomaly yourself, form a hypothesis, and test it without waiting on a data engineer.
• Deep experience with ML data pipelines and labeling ecosystems annotation workflows, quality sampling, taxonomy design, and inter-annotator agreement.
• A systems-level mindset: you identify where quality breaks down structurally and design the mechanism that fixes it, not just the process that patches it.
• Clear, confident communication: you can translate a nuanced data quality finding into a precise safety or business risk that senior leadership can act on.
• Experience managing large-scale offshore or globally distributed annotation teams.

Bonus Points

• Background in autonomous vehicles, robotics, computer vision, or ML model training.
• Prior experience in ML engineering, data engineering, or technical consulting.
• A demonstrated track record of improving training data quality at scale, with metrics to show for it.
• Bachelor's degree in a technical or business discipline, or equivalent practical experience.

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $120,270 and $180,410 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP

As a Senior DevOps Engineer, you will be a key contributor to our infrastructure strategy, focusing on automation, stability, and performance across both cloud and on-premise environments. You will drive best practices in CI/CD, configuration management, and monitoring, with a specific focus on optimizing the deployment and operation of large language models (LLMs) and related technologies.

Responsibilities

• Design, deploy, and manage highly available, scalable infrastructure using Kubernetes and Docker across public cloud (e.g., AWS, GCP, Azure) and on-premise data centers.
• Develop and maintain robust Configuration Management solutions (e.g., Ansible, Terraform) for consistent environment provisioning and management.
• Implement and manage CI/CD pipelines to facilitate rapid, reliable, and automated software releases.
• Administer and troubleshoot operating systems, encompassing both Linux and Windows environments.
• Implement and optimize observability practices using monitoring tools like Datadog for logging, tracing, and alerting.
• Spearhead the operational deployment, scaling, and maintenance of LLM infrastructure, leveraging tools like LiteLLM, OpenRouter, or similar LLM orchestration/gateway technologies.
• Automate repetitive tasks and system operations using scripting languages, primarily Bash and Python.
• Collaborate closely with development, MLOps, and security teams to ensure infrastructure supports product requirements and compliance standards.
• Participate in an on-call rotation to ensure service reliability and responsiveness to incidents.

Qualifications

Required Skills

• 5+ years of professional experience in a DevOps, SRE, or infrastructure engineering role.
• Deep expertise in containerization and orchestration, specifically Kubernetes (design, deployment, and troubleshooting) and Docker.
• Strong proficiency in managing infrastructure in both Cloud (e.g., AWS, GCP, Azure) and On-Premise environments.
• Expert-level administration skills in Linux and strong working knowledge of Windows Server environments.
• Proven experience with Infrastructure as Code (IaC) and Configuration Management tools (e.g., Terraform, Ansible).
• High proficiency in scripting and automation using Python and Bash.
• Extensive experience with monitoring and observability platforms, especially Datadog (or comparable tools like Prometheus/Grafana, New Relic).
• Hands-on experience deploying and managing technologies related to Large Language Models (LLMs), such as utilizing LiteLLM, OpenRouter, or setting up and managing LLM serving endpoints.

Nice-to-Have Skills

• Experience with specific Kubernetes distributions (e.g., K3s, Rancher, OpenShift).
• Familiarity with network configuration, firewalls, and security best practices for hybrid environments.
• Experience in MLOps workflows and related tools (e.g., MLflow, Kubeflow).
• Certifications such as CKA, CKAD, or relevant cloud provider certifications.

Please note that this job description is intended to provide a general overview of the position and does not include an exhaustive list of responsibilities and qualifications

At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company’s business strategy. For this position we are targeting a base pay between $133,400 - $200,000. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience.

Archer is proud to be an Equal Opportunity employer committed to diversity and inclusivity in the workplace. All aspects of employment are decided on the basis of merit, qualifications, and business needs. We do not discriminate based upon race, color, religion, sex, sexual orientation, age, national origin, disability status, protected veteran status, gender identity or any other characteristic protected by federal, state or local laws. Successful candidates must be able to demonstrate U.S. citizenship, permanent residency, or status as a protected individual to satisfy ITAR, contractual, and/or regulatory requirements.

Archer is committed to working with and providing reasonable accommodations to job applicants with physical or mental disabilities, and those with sincerely held religious beliefs. Applicants who may require reasonable accommodation for any part of the application or hiring process should provide their name and contact information to Archer’s People Team at people@archer.com. Reasonable accommodations will be determined on a case-by-case basis.

Archer is committed to working with and providing reasonable accommodations to job applicants with physical or mental disabilities, and those with sincerely held religious beliefs. Applicants who may require reasonable accommodation for any part of the application or hiring process should provide their name and contact information to Archer’s People Team at people@archer.com. Reasonable accommodations will be determined on a case-by-case basis.

Role Overview

We are seeking a Sr. Technical Product Manager to lead the strategy and execution of data product, AI/ML system, AI-powered tooling, and automation initiatives across the go-to-market and operational teams embedded within Natera’s core business units (i.e., Oncology, Women’s Health, Organ Health), including Sales, Marketing, and Medical stakeholders. 

This role focuses on building, scaling, and leveraging platforms and products that power decision intelligence and activity orchestration across these domains. You will work in close collaboration with ‘S&M + GM’ leaders while building through centralized Data & AI organization platforms, standards, and governance. You will own the full product lifecycle from discovery through production, ensuring solutions are adopted, trusted, and deliver measurable business impact. In doing so, you will work hands-on as an empowered builder of AI and software solutions to improve workflow efficiency, productivity, and quality for the teams you support.

This is a technical product role requiring fluency in data systems, modern data platforms, ML, and AI implementation patterns (and the ability and drive to build with them directly), combined with strong experience and stakeholder intuition across Sales, Marketing, Medical, and related functions.

This role sits at the intersection of business impact and technical depth, with deep visibility into commercial performance and executive decision-making. You will have direct ownership of high-impact initiatives fueling overall organizational success.

What You’ll Do

Strategy & Roadmap

• Define and own the Data & AI product strategy and roadmap for the S&M + GM pod by deeply partnering with business leaders to proactively identify high-impact opportunities, shape problem definitions, and drive aligned priorities
• Translate ambiguous business problems (e.g., churn risk, campaign performance, clinical profile segmentation, next-best-action orchestration) into clear product direction and measurable outcomes
• Own the line between ‘what the business needs’ and ‘what gets built’ end-to-end

Discovery, Experimentation, & Requirements

• Develop deep context on the domain so you can proactively propose solutions rather than field requests
• Be hands-on with data: query datasets, review schemas, and validate assumptions through analysis
• Lead end-to-end product discovery with interviews, workflow mapping, data assessments, ROI modeling, etc.
• Define clear product requirements (PRDs, user stories, acceptance criteria) and success metrics
• Design and run experiments to validate product performance and measure causal impact
• Establish leading indicators and KPIs for proactive product and process health assessments

Delivery, Data, & ML Lifecycle

• Partner with data and AI/ML engineering resources to deliver scalable products and capabilities
• Guide development of robust data pipelines and unified data models (360° views across key entities)
• Own the end-to-end ML lifecycle: feature definition, evaluation, deployment, monitoring, drift detection, and retraining
• Ensure training–serving consistency, model versioning, and clear deployment decision gates
• Establish strong observability across data pipelines and models (data quality, latency, reliability, cost)

AI Productization & Hands-On Building

• Define and implement AI product patterns, including agentic workflows and tool/function-calling integrations
• Build process automations and internal tools that improve workflow efficiency, productivity, and output quality for the teams you support
• Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency)
• Implement prompt strategies, guardrails, and continuous evaluation pipelines

Governance, Compliance, & Data Quality

• Ensure products meet regulatory and compliance requirements
• Champion data quality, lineage, and reliability through data contracts and observability standards
• Maintain strong documentation practices (e.g., model cards, dataset documentation, audit trails)
• Partner with governance teams (Security, Legal, Compliance, AI Governance) to operationalize AI responsibly

Adoption, Change Management, & Impact

• Launch products with supporting enablement activities to ensure solutions are embedded in workflows with confidence
• Partner with stakeholders to integrate products into proactive, day-to-day decision-making
• Monitor product usage, performance, and business outcomes to iterate based on data and feedback
• Quantify and communicate impact (e.g., revenue lift, cost reduction, cycle time improvements, forecasting accuracy)
• Influence across Sales, Marketing, Medical, and GM stakeholders through delivery, not authority

Qualifications

Required

• 7+ years of relevant experience (e.g., product management, applied AI/ML engineering, technical founder, forward-deployed engineering), including 3+ years building data products, AI/ML systems, or related platforms
• Demonstrated domain fluency in Sales, Marketing, Field Medical, and/or related commercial/GTM functions 
• Strong understanding of sales funnel, marketing funnel, medical affairs funnel, and related funnels to relate product ideas to the overall opportunity landscape
• Familiarity with key domain-relevant tooling (e.g., Salesforce) and associated data 
• Strong technical capability (e.g., comfortable scripting, performing EDA, with AI-aided application development)
• Hands-on experience shipping LLM-powered features or automations (e.g., prompting, RAG, tool/function calling, agentic orchestration, evals)
• ML/MLOps literacy (evaluation metrics, experimentation, feature stores, CI/CD, monitoring, drift)
• Familiarity with modern data stacks (e.g., Snowflake, dbt, AWS/SageMaker, Airflow/Dagster)
• Strong stakeholder leadership and communication skills, with the ability to work across technical and business teams
• Experience working in regulated environments (e.g., healthcare, life sciences, fintech)

Preferred

• Experience in healthcare, diagnostics, life sciences, and/or digital health
• Familiarity with healthcare data domains and systems
• Experience with vendor evaluation, platform selection, or external AI tooling ecosystems
• Advanced degree in a technical and/or business discipline

What Success Looks Like

• Production-grade data and AI products are deployed and embedded in day-to-day decision-making workflows
• Process automations and internal tools you build are reducing manual effort and improving output quality across the teams you support
• Measurable improvements seen in key business metrics (e.g., increased sales efficiency, better campaign performance)
• Reliable, high-quality data foundations connect patient, clinical, financial, and commercial insights end-to-end
• AI systems are continuously monitored, evaluated, and improved with measurable business impact
• Stakeholders trust and adopt implemented solutions across both corporate and business-unit teams
• Internal teams have transparent access to the function’s roadmap, trust in delivery against it, and visibility into the evaluation logic that prioritizes initiatives based on their impact to organizational goals

Role Overview

We are seeking a Sr. Technical Product Manager to lead the strategy and execution of data product, AI/ML system, AI-powered tooling, and automation initiatives across Patient Experience and Commercial Operations functions. This role is focused on improving how patients navigate testing and billing while enabling operational teams to efficiently progress cases from order to completion. You will play a crucial part in supporting initiatives to reduce friction, improve patient trust, and increase operational efficiency, ultimately driving better conversion, collections, and experience.

This role focuses on building, scaling, and leveraging platforms and products that power decision intelligence and activity orchestration across these domains. You will work in close collaboration with domain leaders while building through centralized Data & AI organization platforms, standards, and governance. You will own the full product lifecycle from discovery through production, ensuring solutions are adopted, trusted, and deliver measurable business impact. In doing so, you will work hands-on as an empowered builder of AI and software solutions to improve workflow efficiency, productivity, and quality for the teams you support.

This is a technical product role requiring fluency in data systems, modern data platforms, ML, and AI implementation patterns (and the ability and drive to build with them directly), combined with strong experience and stakeholder intuition across the associated functions. The role has direct ownership of high-impact initiatives that deeply influence organizational success.

What You’ll Do

Strategy & Roadmap

• Define and own the Data & AI product strategy and roadmap for the Patient Experience + Commercial Operations pod by deeply partnering with business leaders to proactively identify high-impact opportunities, shape problem definitions, and drive aligned priorities
• Translate ambiguous business problems (e.g., stuck cases, fragmented patient communication, support inefficiencies, billing friction) into clear product direction and measurable outcomes
• Own the line between 'what the business needs' and 'what gets built' end-to-end

Discovery, Experimentation, & Requirements

• Develop deep context on the domain so you can proactively propose solutions rather than field requests
• Be hands-on with data: query datasets, review schemas, and validate assumptions through analysis
• Lead end-to-end product discovery with interviews, workflow mapping, data assessments, ROI modeling, etc.
• Define clear product requirements (PRDs, user stories, acceptance criteria) and success metrics
• Design and run experiments to validate product performance and measure causal impact
• Establish leading indicators and KPIs for proactive product and process health assessments

Delivery, Data, & ML Lifecycle

• Partner with data and AI/ML engineering resources to deliver scalable products and capabilities
• Guide development of robust data pipelines and unified data models (360° views across key entities)
• Own the end-to-end ML lifecycle: feature definition, evaluation, deployment, monitoring, drift detection, and retraining
• Ensure training–serving consistency, model versioning, and clear deployment decision gates
• Establish strong observability across data pipelines and models (data quality, latency, reliability, cost)

AI Productization & Hands-On Building

• Define and implement AI product patterns, including agentic workflows and tool/function-calling integrations
• Build process automations and internal tools that improve workflow efficiency, productivity, and output quality for the teams you support
• Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency)
• Implement prompt strategies, guardrails, and continuous evaluation pipelines

Governance, Compliance, & Data Quality

• Ensure products meet regulatory and compliance requirements
• Champion data quality, lineage, and reliability through data contracts and observability standards
• Maintain strong documentation practices (e.g., model cards, dataset documentation, audit trails)
• Partner with governance teams (Security, Legal, Compliance, AI Governance) to operationalize AI responsibly

Adoption, Change Management, & Impact

• Launch products with supporting enablement activities to ensure solutions are embedded in workflows with confidence
• Partner with stakeholders to integrate products into proactive, day-to-day decision-making
• Monitor product usage, performance, and business outcomes to iterate based on data and feedback
• Quantify and communicate impact (e.g., revenue lift, cost reduction, cycle time improvements, case completion turnaround)
• Influence Patient Experience and Commercial Operations stakeholders through delivery, not authority

Qualifications

Required

• 7+ years of relevant experience (e.g., product management, applied AI/ML engineering, technical founder, forward-deployed engineering), including 3+ years building data products, AI/ML systems, or related platforms
• Demonstrated domain fluency in Patient Experience and/or Commercial Operations
• Strong understanding of patient journeys, support channels (e.g., chat, voice, call centers), and operational workflows that drive conversion and test completion
• Familiarity with key domain-relevant tooling (e.g., patient portals, support platforms) and associated data
• Strong technical capability (e.g., comfortable scripting, performing EDA, with AI-aided application development)
• Hands-on experience shipping LLM-powered features or automations (e.g., prompting, RAG, tool/function calling, agentic orchestration, evals)
• ML/MLOps literacy (evaluation metrics, experimentation, feature stores, CI/CD, monitoring, drift)
• Familiarity with modern data stacks (e.g., Snowflake, dbt, AWS/SageMaker, Airflow/Dagster)
• Strong stakeholder leadership and communication skills, with the ability to work across technical and business teams
• Experience working in regulated environments (e.g., healthcare, life sciences, fintech)

Preferred

• Experience in healthcare, diagnostics, life sciences, and/or digital health
• Familiarity with healthcare data domains and systems, including patient, provider, payer, claims, billing, and clinical trial data
• Experience with vendor evaluation, platform selection, or external AI tooling ecosystems
• Advanced degree in a technical and/or business discipline

What Success Looks Like

• Production-grade data and AI products are deployed and embedded in day-to-day decision-making workflows
• Process automations and internal tools you build are reducing manual effort and improving output quality across the teams you support
• Measurable improvements seen in key business metrics (e.g., case completion turnaround time, patient satisfaction, cost-to-serve)
• Reliable, high-quality data foundations connect patient, provider, clinical, operational, and billing/financial insights end-to-end
• AI systems are continuously monitored, evaluated, and improved with measurable business impact
• Stakeholders trust and adopt implemented solutions across both corporate and business-unit teams
• Internal teams have transparent access to the function's roadmap, trust in delivery against it, and visibility into the evaluation logic that prioritizes initiatives based on their impact to organizational goals

 Who We Are

2K is headquartered in Novato, California and is a wholly owned label of Take-Two Interactive Software, Inc. (NASDAQ: TTWO). Founded in 2005, 2K Games is a global video game company, publishing titles developed by some of the most influential game development studios in the world. Our studios responsible for developing 2K’s portfolio of world-class games across multiple platforms, include Visual Concepts, Firaxis, Hangar 13, CatDaddy, Cloud Chamber, 31st Union, HB Studios, and 2K SportsLab. Our portfolio of titles is expanding due to our global strategic plan, building and acquiring exciting studios whose content continues to inspire all of us! 2K publishes titles in today’s most popular gaming genres, including sports, shooters, action, role-playing, strategy, casual, and family entertainment.

Our team of engineers, marketers, artists, writers, data scientists, producers, thinkers and doers, are the professional publishing stewards of 2K’s portfolio currently includes several AAA, sports and entertainment brands, including global powerhouse NBA®️ 2K,  renowned BioShock®️, Borderlands®️, Mafia, Sid Meier’s Civilization®️ and XCOM®️ brands; popular WWE®️ 2K and WWE®️ SuperCard franchises, TopSpin 2K25, as well as the critically and commercially acclaimed PGA TOUR®️ 2K.

At 2K, we pride ourselves on creating an inclusive work environment, which means encouraging our teams to Come as You Are and do your best work! We encourage ALL applicants to explore our global positions, even if they don’t meet every requirement for the role.  If you're interested in the job and think you have what it takes to work at 2K, we encourage you to apply!

 What You Will Do

 2K Games is building a world-class data ecosystem to power real-time analytics, machine learning, and game intelligence across all our franchises. We are looking for a Senior Product Manager, Data Science to drive the vision, strategy, and execution of our data science strategies and products, partnering with game studios and analytics teams to enable enhanced player experiences.

This role requires a blend of strategic development, product management expertise, and technical understanding of data science and machine learning (ML) methodologies. The ideal candidate will seamlessly integrate data-driven strategies with player-centric goals, ensuring AI and analytics improve game design, live operations, and business intelligence.

Strategic Vision & Product Leadership

• Develop and implement the product strategy for 2K’s central data science platform, partnering with Data Scientists across our gaming business units to drive predictive analytics, player personalization, and real-time decision-making;
• Collaborate with the Director of Product, 2K Data to align the data engineering roadmap with customer objectives and player-centric outcomes;
• Define and prioritize a high-impact data science roadmap to lead a team of central data scientists in driving a variety of use cases such as player retention modeling, dynamic content generation, and real-time fraud detection. 

Data Science Platform Development

• Lead the development of reusable and extensible machine learning models, feature engineering pipelines, and ML-driven insights using tools like Databricks, MLflow, and SageMaker;
• Ensure data science initiatives integrate seamlessly with the real-time data infrastructure, supporting streaming analytics, AI-powered LiveOps, and dynamic matchmaking systems;
• Establish and maintain self-service tools that enable game teams to experiment with and deploy AI models independently;
• Support the build and enhancement of the central ML feature store;
• Monitor and optimize model performance in production environments, using MLOps standard methodologies;
• Implement governance frameworks to ensure AI models adhere responsibly to data privacy, security, and compliance standards (e.g., CCPA, GDPR). 

Cross-Functional Collaboration

• Work closely with data scientists across game studios and business units to understand their data science needs and to provide data-driven solutions at the pace they need them;
• Act as a strategic liaison between data scientists and product teams, ensuring model outputs are actionable, reliable, and aligned with business goals;
• Promote a culture of experimentation and rapid iteration, supporting game studios with A/B testing frameworks, predictive analytics, and personalization engines.
• Find opportunities to enhance game design and player engagement through advanced analytics and machine learning.

 What You Need

• 7+ years of experience in product management with a focus on data science, analytics, or ML-driven products
• 5+ years of experience in gaming, technology, or entertainment industries;
• Strong understanding of data science methodologies, such as predictive modeling, natural language processing, and computer vision;
• Experience with data science tools and platforms, such as Python, SQL, Databricks,TensorFlow, and R;
• Customer-focused mentality with focus on the Why and What without getting lost in the What and the How;
• Consistent track record of delivering data science products and features from ideation to production, with measurable business impact;
• Ability to translate complex data insights into clear product strategies and business opportunities;
• Knowledge of real-time analytics platforms, streaming data processing, and feature store management;
• Familiarity with data governance frameworks, model risk management, and MLOps standard processes preferred;
• Advanced degree or equivalent experience in Data Science, Machine Learning, Computer Science, or a related field preferred.

The pay range for this position in California at the start of employment is expected to be between $109,800 and $162,480 per Year. However, base pay offered is based on market location, and may vary further depending on individualized factors for job candidates, such as job-related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or equity awards and eligibility to participate in our 401(K) plan and Employee Stock Purchase Program. Regular, full-time employees are also eligible for a range of benefits at the Company, including: medical, dental, vision, and basic life insurance coverage; 14 paid holidays per calendar year; paid vacation time per calendar year (ranging from 15 to 25 days) or eligibility to participate in the Company’s discretionary time off program; up to 10 paid sick days per calendar year; paid parental and compassionate leave; wellbeing programs for mental health and other wellness support; family planning support through Maven; commuter benefits; and reimbursements for fitness-related expenses.

As an equal opportunity employer, we are committed to ensuring that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform their essential job functions, and to receive other benefits and privileges of employment. Please contact us if you need reasonable accommodation.

Please note that 2K Games and its studios never uses instant messaging apps or personal email accounts to contact prospective employees or conduct interviews and when emailing, only use 2K.com accounts. 

#LI-Onsite

SLSQ127R420

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

As the GTM Digital Native Programs Leader, you will operate as the de facto COO and scale pipeline and consumption  for th high growth Databricks’ Digital Native vertical. You will partner in a "two-in-a-box" model with the Industry GTM Leader to translate  strategy into durable, repeatable commercial execution across a rapidly scaling global field organization. You will work closely with sales and field engineering leaders to ideate, build, execute and scale global programs. 

You will play a critical role in shaping Databricks’ growth trajectory. You will be part of a lean GTM leadership that powers sellers to deliver measurable business outcomes through scale & leverage. You will be the lead orchestrator for your industry vertical, at the center of a mandate to inspire executives, drive industry outcomes, and unleash Databricks’ partner ecosystem in the high-growth SaaS space. 

The Impact You Will Have

Strategy & Operating Model

• Operating Model Ownership: Own and implement end-to-end op-model (across Sales, FE, Product, Engineering, Developer Relations, Partners) in a consistent global approach.
• Product-GTM Strategy: Inform the annual Digital Native strategy by synthesizing product telemetry and engineering roadmaps with field signals to identify Big Bets in the digital natives space.
• Data-Driven Product Feedback: Synthesize learnings from internal (field, product, engineering, developer relations) & external (customer, partner, developers) stakeholders into durable best practices 

Market Presence & Influence

• Internal Executive Influence: Wield two-way influence on Databricks’ Field (Sales, FE, Business Dev), Product & Engineering leadership: i.e., reflect industry signals in product roadmaps, as well as translate product activation goals into verticalized industry programs
• Market Presence: Own and engage with technical & business customer and partner executives, build market presence to uncover signals on technical friction, business value and Databricks differentiators.

• Industry Evangelism: Serve as a visible evangelist & thought leader (e.g., open source advocacy, scaling AI adoption), representing Databricks’ at key customer, partner, industry and developer forums

Execution & Accountability

• Full-Funnel Program Design: Select & design the industry’s programs across products (and verticalize product programs) across the full funnel: demand generation → pipe creation & progression → consumption
• Asset Creation: Create high-quality and scalable program assets that will be used by the full field (Sales, FE, BD, Marketing, Ecosystem) at scale

• Field Activation & Community: Drive continuous field adoption of program assets and outcomes by cross-pollinating best practices across product and field teams in high-impact community cadences.
• Ecosystem Integration: Collaborate with the partnership, ISV, and Data Marketplace teams to drive adoption of a seamless, interconnected data-sharing ecosystem.

• Outcome Measurement: Deliver commercial outcomes by establishing measurement and monitoring systems that track product telemetry, industry & partner program performance.

• Executive Accountability: Drive cross-functional leadership accountability for program adoption and outcomes through continuous monitoring and executive influence.

What We Look For

Strategic & Operational Mindset

• COO Mindset: Proven ability to act as the operational backbone of a business vertical, focusing on pipeline health, organizational efficiency, and execution.
• Strategic Execution: A track record of defining and translating complex industry strategies into durable, "field-ready" commercial motions for large organizations.
• Programmatic Vision: Experience developing global, scalable GTM frameworks - moving beyond "one-off" deals to create a scalable engine for growth.

Technical & Domain Expertise

• Technical Chops: Technical knowledge on Databricks critical for this market. Comfort with discussing  AI & LLM, Lakehouse, Lakebase, governance 

• Industry POV: A strong point of view on macro trends in high-growth SaaS and key persona triggers, helping to shape our industry advantage against peers.

• Open Source Knowledge: A strong understanding of the open-source ecosystem and how it integrates with proprietary enterprise platforms.
• AI Builder Experience: Proven track record of hands-on experience building AI systems (MLOps, GenAI apps, or large-scale predictive models).

Execution & Analytical Skills

• Full-Funnel Expertise: Deep understanding of sales play mechanics, including lead generation, pipeline progression, and consumption drivers.

• Scale Orientation: A "multiplier" mindset with a passion for activating large-scale field organizations and cross-pollinating best practices.
• Entrepreneurial Spirit: A "doer" mindset with the ability to operate in a fast-paced, ambiguous environment and drive results across cross-functional teams.

Leadership & Scale

• Influence Without Hierarchy: Ability to influence & drive outcomes across senior executives & global teams via influence, clarity & presence versus direct reporting lines.
• Cross-Functional Orchestration: Skill in collaborating with Product, Engineering, and BD leadership to align product roadmap shifts with commercial requirements specific to high-growth SaaS customers.

Role

We are hiring a Principal Software Development Engineer to be a founding member of our AI Security Team, offering the chance to build a high-reliability, low-latency AI security solution designed to scale for hundreds of millions of users. In this role, reporting to the Director, Software Development Engineering, you will be responsible for designing and implementing core infrastructure components and distributed systems while collaborating closely with various stakeholders throughout the development lifecycle. This is a hybrid role, reporting in to the San Jose, CA office 3 days a week.  

What you’ll do (Role Expectations)

• Architect, develop, and optimize a low-latency, high-throughput AI Security plane utilizing Rust, specifically leveraging its async/await model for highly efficient I/O and service-oriented architecture.
• Build resilient, distributed, and scalable systems, emphasizing concurrency, fault tolerance, and robust messaging protocols.
• Implement and maintain gRPC services and APIs to ensure seamless integration of the AI Security plane with control and orchestration infrastructure.
• Systematically enhance performance across the entire stack, including LLM models, by employing profiling tools for both kernel-space and user-space components.
• Contribute actively to the team through technical documentation, system design reviews, code reviews, and by mentoring junior engineering staff.Lead complex, multi-functional projects and initiatives, defining the technical roadmap and driving execution across teams.

Who You Are (Success Profile)

• Drive the mission with an owner's mindset, seamlessly bridging high-level strategy and detailed, hands-on execution.
• Actively seek challenges and find complex solutions that deliver maximum impact.
• Deeply empathize with internal and external stakeholders, anchoring every decision in addressing their real-world problems.
• Maintain strong technical curiosity, continually seeking more secure, scalable, and effective methods to accelerate transformation.
• Connect your work to the company's broader mission, designing solutions to support high-growth, global organizational needs.

What We’re Looking for (Minimum Qualifications)

• 12+ years of software engineering experience
• Deep experience in systems programming using Rust, with a focus on asynchronous frameworks such as Tokio or async-std.
• Proven ability to design and implement horizontally scalable, highly available, and observable distributed systems.
• Strong command of Linux internals, including kernel-user space interaction, networking, sockets, and namespaces.
• Skilled in performance instrumentation, containerized environments, Git workflows, and CI/CD pipelines.

What Will Make You Stand Out (Preferred Qualifications)

• Expert in systems languages such as C/C++ or Rust, with a focus on performance optimization.
• Deep understanding of Linux networking stacks, Kubernetes networking, service meshes, and LLM model optimization.
• Practical experience with gRPC, Protobufs, and L4-L7 networking protocols, including TCP/IP, TLS, QUIC, HTTP, and WebSockets (WSS).
• Direct experience in validating AI-driven products, including understanding of model performance metrics and MLOps principles.

#LI-BH1
#LI-Hybrid

The team 

Nebius is building a world-class commercial organization to match the scale of our infrastructure platform. Our GTM leadership team operates with high trust, direct access to Product and Engineering, and the mandate to define how Nebius competes and wins in the enterprise. You will join a senior leadership bench that values clarity, accountability, and speed — with fewer layers and more impact than the hyperscalers. 

The role 

Nebius is seeking an Enterprise Sales Leader to build and scale our industry go-to-market across priority verticals including Financial Services, Healthcare & Life Sciences, Manufacturing, Media & Entertainment, Automotive, and Energy. 

This leader will: 

• Build a verticalized enterprise sales motion 

• Lead a team of Enterprise AEs aligned to industry segments 

• Drive repeatable, multi-year AI infrastructure deals 

• Operationalize a partner-led GTM model anchored in NVIDIA and strategic SIs 

Success in this role requires combining vertical expertise, ecosystem orchestration, and enterprise deal execution to establish Nebius as the AI infrastructure platform of choice. 

Success will be measured not only by direct revenue, but by partner-influenced pipeline, multi-year capacity commitments, and lighthouse customer acquisition. 

You are welcome to work remotely from the US. 

Partner & NVIDIA Motion 

This role will lead a partner-centric enterprise GTM model: 

• Own joint go-to-market with NVIDIA field teams across priority verticals 

• Build and scale co-sell motions with SIs and channel partners (Accenture, Deloitte, Slalom, CDW, WWT, etc.) 

• Enable ISVs to embed Nebius into enterprise workflows and solutions 

• Drive partner-sourced and partner-influenced pipeline as a core KPI 

• Establish joint value propositions combining infrastructure + software + services 

Your responsibilities will include: 

• Define and execute vertical-specific GTM strategies aligned to NVIDIA’s top enterprise markets. 

• Hire, develop, and lead a team of Enterprise Account Executives focused on industry specialization — setting performance standards, coaching deal execution, and driving vertical expertise. 

• Own revenue targets and ARR growth across assigned verticals. 

• Develop named account strategies within priority enterprise segments. 

• Personally engage in complex, multi-million-dollar GPU infrastructure deals — connecting infrastructure decisions to vertical-specific business outcomes. 

• Build executive-level relationships across CIO, CTO, Chief Data, and Line-of-Business leaders. 

• Collaborate closely with NVIDIA field teams to drive joint account planning and pipeline development. 

• Establish forecasting rigor, pipeline discipline, and enterprise sales standards across the vertical portfolio. 

• Partner cross-functionally with CSA, Product, Marketing, Capacity Planning, and Partnerships to align resources and deliver differentiated value. 

• Drive land-and-expand motions across AI training, inference, and production AI workloads. 

• Evangelize Nebius’s NVIDIA-based GPU platform (H100, H200, B200, GB200 NVL72, and beyond) within vertical-specific use cases. 

• Build vertical-specific solution plays (RAG, training, inference, MLOps) with partners 

• Drive partner-led access to enterprise buying centers (CIO, CDAO, CISO, ML teams) 

• Scale 1:few and 1:many partner GTM programs (not just direct selling) 

• Establish lighthouse customers per vertical 

We expect you to have: 

• 10+ years of enterprise infrastructure or cloud sales experience, with a proven track record in one or more large enterprise verticals. 

• 5+ years of leadership experience managing enterprise sales teams with vertical or industry specialization. 

• Proven success closing $5M–$50M+ infrastructure or platform deals within regulated or complex industry segments. 

• Strong understanding of vertical-specific AI use cases, regulatory environments, and enterprise buying cycles. 

• Experience co-selling with strategic ecosystem partners (e.g., NVIDIA, OEMs, SIs) and navigating partner-driven motions. 

• Builder mindset — you’ve created vertical-focused GTM playbooks, coverage models, and scalable sales motions where none existed. 

• Demonstrated ability to build and scale vertical-focused sales teams within high-growth companies. 

• High energy, ownership mentality, and consistent overachievement against targets. 

• Experience co-selling with NVIDIA and/or large SIs in enterprise environments 

• Proven ability to drive partner-influenced enterprise revenue at scale 

• Deep understanding of enterprise procurement + partner-driven buying cycles 

It will be an added bonus if you have: 

• Direct experience working with NVIDIA field teams or ecosystem partners on enterprise sales motions. 

• Experience selling AI/ML infrastructure into regulated or highly complex industries (e.g., FSI, Healthcare, Energy). 

• Background in hyperscaler, neo-cloud, or GPU-first environments. 

• Established executive network within one or more priority enterprise verticals. 

• Experience structuring multi-year infrastructure and capacity commitments. 

• Prior leadership at a hyperscaler or neo-cloud (e.g., AWS, Azure, GCP, CoreWeave, Lambda). 

Key Employee Benefits 

• Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 

• 401(k) Plan: Up to 4% company match with immediate vesting. 

• Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. 

• Remote Work Reimbursement: Up to $85/month for mobile and internet. 

• Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. 

Compensation 

We offer competitive salaries in the 458K - 550K range + equity

Don’t just work in tech. Shape it. If you’re driven to define how the world builds with AI, we want to hear from you. 

About the Role

We're hiring an AI Engineer to work on the AI core of Mill Commercial — the computer vision and agentic systems that turn a stream of food waste into operational intelligence for commercial kitchens. Mill Commercial integrates a camera and onboard compute directly into our high-capacity food recycler; models running on the edge identify, classify, and quantify food scraps at the point of generation, and our vision pipeline turns that signal into procurement and operational guidance for large food service operators.

You'll join a small AI team, building the data and training pipeline that produces our edge CV models, designing the cloud-side evaluation that tells us whether those models are good enough to ship, and helping build the agentic, LLM-driven product features that turn raw waste data into customer-facing insights and recommendations. This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation model, prompting a VLM, and wiring an agent into a product feature.

What You'll Do

• Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling (including foundation-model-assisted labeling), training, evaluation, and retraining cycles.
• Train and evaluate segmentation, classification, and mass-estimation models for the Mill Commercial camera pipeline — from prompting foundation models to fine-tuning ConvNets and VLMs.
• Build the cloud-side evaluation harness that tells us how our shipped edge models are actually performing in the field — automated, reproducible, and aligned to product accuracy targets across food types, kitchen environments, and deployment configurations.
• Own MLOps: reproducible training, experiment tracking, model versioning, and automated evaluation against product-defined accuracy targets.
• Export and validate models for deployment to edge devices, working closely with the edge team on optimization, quantization, and integration.
• Help design and build the LLM- and agent-powered product features that consume waste characterization data and turn it into customer-facing recommendations — purchasing suggestions, anomaly explanations, operational nudges. Define how agents call tools, ground in customer data, and stay reliable in production.
• Analyze failure cases systematically — unfamiliar food classes, novel kitchen environments, challenging lighting and clutter conditions — and drive the data and modeling decisions that close accuracy gaps.

What We're Looking For

• Strong fundamentals in computer vision and deep learning — segmentation, detection, classification, tracking. You understand the architectures well enough to make informed choices.
• Fluency with modern ML approaches — VLMs, LLMs, foundation models, and agentic systems — alongside classical deep learning. You know when to fine-tune a ConvNet, when to prompt a VLM, and when to wire up an agent, and you understand the practical realities of putting any of them into a product.
• Experience building ML training pipelines and data annotation systems at scale.
• Experience evaluating ML models rigorously — designing metrics, building the eval harness, and using results to drive product decisions rather than just publish a number.
• Proficiency with cloud ML infrastructure (AWS or equivalent) — you've managed training jobs, data pipelines, and experiment workflows in production.
• Familiarity with cloud-to-edge model deployment.
• Clear, direct communication — you can explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow.
• Genuine interest in applying AI to food waste reduction and sustainability. This is a mission-driven product and we want people who care about the mission.

Software skills: Python, PyTorch, OpenCV. Strong familiarity with MLOps on AWS infrastructure. Experience with LLM and agent frameworks. Google Cloud / Gemini experience is a plus.

Nice to Have

• Experience with video understanding (temporal consistency, tracking, video segmentation)
• Experience with foundation models for data annotation
• Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, or equivalents)
• Experience shipping LLM- or agent-powered features in a consumer or B2B product
• Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems

The estimated base salary range for this position is $240 to $280k, 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. At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.

We are looking for a Senior Data Engineer to join our Data & ML Platform team, responsible for development, maintenance and support for Oura’s Datalake and adjacent systems, collaborating closely with data scientists, analysts and other departments utilizing the Datalake.

This is a hybrid role based in San Francisco, CA, working onsite 1-2 days per week.

We have offices in San Francisco, San Diego and Los Angeles for those who prefer hybrid or office settings. Oura employees in other major cities (like Boston and New York) occasionally gather informally at local co-working locations.

What you will do: 

• Build, operate, and improve Oura’s Datalake consisting of systems to move, process, and store large amounts of data (Terabyte–Petabyte scale) leveraging tools such as AWS Kinesis, S3, Spark / Glue, Athena, dbt, Iceberg, Snowflake, Docker, workflow engines, and more.
• Design and implement data models and pipelines with a strong focus on data quality, reliability, and governance.
• Ensure that our data platform and processes meet privacy, compliance, and security requirements.
• Build components that support the handling of datasets, training, testing, and release of new on-device and cloud-based ML models.
• Independently collaborate with different stakeholders including Data Scientists, Testing, Firmware, and Hardware to define and implement improvements and new functionality and support internal Datalake consumers in their day-to-day work.
• Write code, mostly in Python and SQL  with occasional additions Typescript, Java or some other language.

We would love to have you on our team if you have:

• 5+ years of experience developing and operating production systems.
• Experience running, monitoring, and debugging production systems at scale on a public cloud. We rely on AWS but experience with other cloud platforms counts too.
• Good architectural understanding of event-driven architectures, workflow engines, database, and data warehouse systems.
• Ability to design robust data models and pipelines, with attention to data accuracy and integrity.
• Experience in building infrastructure and components following best practices such as CI/CD and infrastructure as code.
• Demonstrated skills in writing well-structured, testable, and high-performance code.
• Familiarity with some of the following: Workflow engines, Stream processing, MLops, Spark, Athena, SQL, dbt, Databricks.
• You are self-motivated, proactive, and able to lead and manage your own work effectively.
• A pragmatic and goal oriented approach to development.

What Makes You Stand Out

• Real-life experience with Databricks.
• Familiarity and experience with the Data Mesh approach
• Broad knowledge of software fundamentals, databases, warehouses, and system design with experience on various programming languages

Benefits 

At Oura, we care about you and your well-being. Everyone here at Oura has a ring of their own and we are continually looking to improve employee health.

What we offer:

• Competitive salary and equity packages
• Health, dental, vision insurance, and mental health resources
• An Oura Ring of your own plus employee discounts for friends & family
• 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
• Paid sick leave and parental leave

Oura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates' pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future.

• Region 1 $172,550 - $203,000
• Region 2 $159,000 - $187,000
• Region 3 $148,000 - $174,000

A recruiter can determine your zones/tiers based on your US location.

Applicants must be authorized to work for any employer in the U.S. without sponsorship. We are unable to provide sponsorship for work visas of any kind at the time of hire, or at any point during employment. This includes, but is not limited to: F1-OPT, F1-CPT, H-1B, TN, J-1, etc.

Location: Live in or able to relocate to San Francisco / San Jose area

Your Role:

Tenable is not only building great products and carving out a completely new category, but we are also building a world-class engineering culture committed to hiring the very best.

We are looking for a Senior Software Engineer to join a founding initiative within our engineering organization. In this role, you will help design and implement the Agentic AI Platform, the orchestration layer and nervous system that enables autonomous agents to reason, plan, and execute complex security workflows across our Exposure Management ecosystem.

This is a hands-on engineering role with broad impact. You will not just be "using" LLMs; you will be building the distributed systems, state machines, and tooling that transform generative models into reliable, enterprise-grade agents. 

While you will work from home the majority of your time, there may be an expectation to gather for in-person meetings on occasion. 

Your Opportunity:

• Build the Agentic Backbone: Architect and implement the backend services that power multi-agent workflows. You will build systems that allow AI agents to decompose complex user requests, manage state, and execute tasks across distributed microservices.
• Orchestration & Workflow Automation: Design scalable workflow engines and "human-in-the-loop" systems. You will enable agents to perform long-running investigations and remediation tasks, balancing autonomy with control.
• Bridge AI & Enterprise Infrastructure: Create the integration layer between modern Python-based AI frameworks and Tenable’s robust JVM-based microservices architecture.
• Reliability & Guardrails: Implement verification layers, citations, and security guardrails to ensure agents operate deterministically and safely. You will treat "prompts" as code and model outputs as untrusted input that requires validation.
• Tooling & RAG: Build advanced Retrieval-Augmented Generation (RAG) pipelines and "Tool Use" capabilities, allowing agents to query databases, call internal APIs, and synthesize data from disparate sources.
• Lead Technical Direction: Collaborate with researchers and product leads to define the roadmap for AI orchestration. Champion best practices for MLOps, agent evaluation, and system observability.

What You’ll Need:

• B.S. or M.S. in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 5+ years of software engineering experience (7+ for Staff level), with a strong background in building backend systems, APIs, and platforms.
• Agentic AI Expertise: Hands-on experience building AI agents and autonomous workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Pydantic AI.
• Location: Live in or able to relocate to San Francisco / San Jose area
• Polyglot Proficiency: Strong programming skills in Python (essential for AI/ML ecosystems) with experience in, or a willingness to work with, JVM-based languages (Java, Kotlin, or Scala) for high-scale platform components.
• Distributed Systems: Experience designing event-driven architectures, managing concurrency, and building fault-tolerant services (e.g., using Kafka, gRPC, REST).
• Data Fluency: Proficiency with Relational Databases (PostgreSQL) and experience with Vector Databases (e.g., Pinecone, Weaviate, Milvus) or search engines (Elasticsearch).
• Builder Mindset: A self-starter who thrives in ambiguity and is passionate about taking AI from "prototype" to "production-grade."

And Ideally:

• LLM Ops & Evaluation: Experience designing frameworks to evaluate agent performance (accuracy, latency, cost) and implementing CI/CD for AI workflows.
• Cloud Native: Experience deploying and scaling services in AWS using Docker and Kubernetes.
• Graph & Search: Familiarity with Knowledge Graphs or advanced search algorithms to improve agent reasoning.
• Security Context: Understanding of cybersecurity concepts (vulnerability management, cloud security) is a plus, but not required if you are a strong systems engineer.

#LI-LP1

We are seeking a high-impact Staff Data Scientist to play a leading role on our data needs to bridge the gap between advanced research and production-grade ML systems. You will be a key technical leader and partner to our Research Scientists, driving the evolution of our AI algorithms through rigorous data science methodologies, strategic data curation, and cross-functional leadership.

Job Responsibilities:

• Vision & Cross-Functional Leadership: Engage with cross-functional teams (Product, Research, Engineering, and Process Engineering) and help guide the overarching vision for data analysis, curation, and annotation to improve our AI models. Drive the definition of algorithm and data requirements necessary to deliver on end-point objectives.
• Data Campaigns and Annotation Strategy: Introduce best practices and efficient processes to enable multiple, large-scale annotation campaigns for continuous AI product improvements. Spearhead the strategic design of data campaigns needed to advance AI algorithms.
• Advanced Data Curation & Management: Develop comprehensive data curation strategies for improvements to AI models. Partner with MLOps to architect data management systems for algorithm training and performance validation, and establish data dashboards and monitoring reports.
• Performance Evaluation and Statistical Analysis: Establish frameworks and develop tools to rigorously evaluate the strengths and weaknesses of model iterations across patient populations. Design and oversee data analyses using statistical techniques to illuminate the performance of our AI algorithms.

Required Qualifications:  

• Education: Masters or PhD Degree in Engineering, Computer Science, Statistics, Data Science or related degree.
• Work Experience: 7+ years (or 5+ with PhD) of proven track record in Data Science, Machine Learning, Computer Vision, or Image Analysis in industry. Have a wealth of experience from working on complex, real-world AI problems in medical imaging or computer vision requiring careful consideration of data needs.
• Software: Proficient in Python, including statistical and machine learning packages (e.g. scipy, sklearn, statsmodels, seaborn) with a commitment to writing clean, reproducible and scalable code. Familiarity with image and 3D processing tools like VTK and ITK, and visualization tools like 3DSlicer and MITK, a plus.
• Statistics: Understanding of standard statistical techniques and ability to apply appropriate statistical methods to a range of data structures; experience with medical imaging data a plus.
• Data Management: Experience working with data lakes, SQL/No SQL Databases, AWS Cloud Storage, Tableau dashboards.
• AI & Medical Imaging: Expertise in deep learning and machine learning models in production. Strong experience working with medical imaging data (CT and vascular imaging) is highly preferred.
• (Preferred) Proficiency in Agentic AI Tools: Experience integrating generative AI and agentic tools into daily workflows to act as a force multiplier—accelerating coding, prototyping, and complex data analysis—while demonstrating the critical judgment required to rigorously evaluate AI outputs for accuracy.
• Communication: Excellent written and verbal communication. Detail orientated but able to describe complex topics in a manner that is digestible to a broad audience. Ability to distill complex analyses into interpretable and actionable findings.
• Commitment: Committed to the success of the team and successful completion of project - 75% confidence / 100% committed.
• Influence without Authority: Able to build trust and obtain cross-functional alignment across departments.

A reasonable estimate of the base salary compensation range is $220,000 to $280,000, bonus, and equity. #LI-IB1 #LI-Hybrid

About the team/role

We’re hiring an ML Engineer (P2) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering.

Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.

This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.

This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.

We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.

What you’ll do

• Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.
• Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.
• Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.
• Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.
• Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.
• Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.

What you bring

• A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience
• 4+ years building software professionally, with at least 2 of those building ML systems that run in production
• Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software
• Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation
• You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on
• Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling
• Experience with NLP tasks in production: classification, extraction, entity resolution, summarization
• Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal
• You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model
• Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon
• You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.

Nice to have

• Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)
• Background in document processing, OCR, or information extraction
• Experience with PySpark or large-scale data processing
• Ruby experience (Checkr’s platform runs on Rails)
• Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)
• Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools

What you’ll get

• A fast-paced, low-bureaucracy environment where shipping matters more than process
• Direct exposure to executive and C-level decision-making
• A seat on the Data & ML team shaping Checkr’s AI strategy
• Learning and development allowance
• Competitive cash and equity compensation and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend

ABOUT THE TEAM

Anduril Maritime delivers platforms, systems, and integrated effects in the maritime domain. Our autonomous vehicles (sub-surface and surface) are the cornerstone of these capabilities, and we continually strive to push the boundaries of the possible in terms of endurance, autonomy and mission capability. The Maritime team develops and maintains core products and payloads, and adapts and applies those products to serve a wide variety of defense, IC and commercial customers in US and international markets.

ABOUT THE JOB

As a Senior Software Engineer, DevOps on the Maritime Digital Shipbuilding team, you will build and operate the infrastructure that keeps our digital production systems running at full speed. You’ll develop and manage CI/CD pipelines, automate infrastructure with code, and deploy applications and machine learning models across cloud and edge environments with security, traceability, and reliability in mind.

You’ll work closely with software, data, and operations engineers to turn designs into working systems—streamlining development, improving performance, and keeping production stable as we scale. You’ll also collaborate with digital, manufacturing, and corporate technology teams across Anduril in a high-tech, fast-paced culture of innovation focused on solving real problems and delivering results.

If you’re driven to build systems that last, thrive on deep technical challenges, and want to see your work directly shape how we design, build, and sustain complex platforms, you’ll be helping build the future of digital shipbuilding and the next generation of maritime vehicles.

WHAT YOU'LL DO

• Build and Manage CI/CD Pipelines: Develop and maintain CI/CD pipelines using tools like GitHub Actions and Jfrog Artifactory to ensure seamless integration and deployment of machine learning models and applications.
• Infrastructure as Code (IaC): Utilize Terraform and Ansible to automate infrastructure provisioning and management on cloud platforms such as Azure, AWS, or Google Cloud Platform (GCP).
• Containerization and Orchestration: Implement containerization solutions with Docker and manage container orchestration using Kubernetes to ensure reliable deployment and scaling of applications.
• Model Management and Deployment: Set up and maintain model registries and feature stores (e.g., MLflow, Kubeflow), and manage deployment pipelines for both batch and real-time inference.
• Monitoring and Logging: Establish comprehensive monitoring and logging solutions using tools like ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana to ensure the smooth operation of deployment environments.
• Collaborate with Cross-Functional Teams: Work closely with development, data science, and operations teams to foster collaboration and ensure the efficient and effective deployment of machine learning models.
• Optimize Performance: Utilize parallel computing frameworks such as CUDA and OpenCL to accelerate high-performance computing tasks, ensuring timely processing of large datasets and complex simulations.

REQUIRED QUALIFICATIONS

• Advanced proficiency in programming languages (Python for scripting and integration).
• Experience with CI/CD tools like GitHub Actions, Jfrog Artifactory, and Git.
• Proficiency with IaC tools (Terraform, Ansible).
• Experience with cloud platforms (Azure, AWS, GCP).
• Proficiency in containerization (Docker) and container orchestration (Kubernetes).
• Knowledge of model registries and feature stores (e.g., MLflow, Kubeflow).
• Experience with logging and monitoring tools (ELK Stack, Prometheus, Grafana).
• Understanding of parallel computing frameworks (CUDA, OpenCL).
• Strong collaboration skills and proficiency with collaborative tools (JIRA, Confluence).
• Eligible to obtain and maintain an active U.S. Secret security clearance.

PREFERRED QUALIFICATIONS

• Previous experience in a manufacturing or industrial setting.
• Familiarity with observability concepts and tools.
• Knowledge of security best practices for DevOps and MLOps.

ABOUT THE TEAM

Anduril Maritime delivers platforms, systems, and integrated effects in the maritime domain. Our autonomous vehicles (sub-surface and surface) are the cornerstone of these capabilities, and we continually strive to push the boundaries of the possible in terms of endurance, autonomy and mission capability. The Maritime team develops and maintains core products and payloads, and adapts and applies those products to serve a wide variety of defense, IC and commercial customers in US and international markets.

ABOUT THE JOB 

We are seeking a Senior Machine Learning/MLOps Engineer to join the Applied Intelligence team within Maritime Digital Production. You will lead the design, deployment, and sustainment of the AI-enabled backbone that connects data, tools, and people across our digital shipbuilding environment. This role focuses on operationalizing advanced models and architecting the industrial AI stack—selecting, integrating, and standardizing technologies that bring intelligence into production systems only where they deliver real value.

You’ll define and implement core components of the platform, including unified data and feature stores, vector databases, orchestration layers, and model-serving frameworks, while integrating off-the-shelf models for computer vision, OCR/IDP, speech, and retrieval-augmented generation. You’ll work across software, data, manufacturing, and corporate technology teams to translate real factory scenarios—inspection, root-cause analysis, receiving workflows, scheduling—into applied AI capabilities with clear human-in-the-loop controls, auditability, safety gates, and integrations with PLM, MES, CMMS, ERP, and the unified data plane.

This role demands technical range, judgment, and an instinct for when AI is the right tool versus when a simpler logic or workflow solution wins. You’ll collaborate across Anduril in a high-tech, fast-paced culture of innovation focused on delivering systems that work in the real world. If you want to shape how AI becomes a practical, reliable tool for production and logistics at scale, you’ll be helping build the future of digital shipbuilding and the next generation of maritime vehicles.

WHAT YOU’LL DO 

• Architect and own the AI/ML platform stack—from data ingestion, labeling, and feature engineering to model training, deployment, monitoring, and lifecycle management.
• Select, prioritize, and standardize industrial AI components including feature stores, vector databases + RAG, OCR/IDP and CV/STT providers, orchestration layers, and observability systems.
• Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems.
• Translate factory scenarios (receiving, inspection, RCCA, scheduling) into applied AI workflows with defined human-in-the-loop gates, audit trails, and integration contracts with PLM, MES, CMMS, ERP, and the unified data plane.
• Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from production and logistics systems.
• Drive make/buy strategy by researching internal and vendor AI capabilities and recommending investments aligned to enterprise roadmaps, Anduril IP principles, and production constraints.
• Define and maintain model governance processes for validation, safety reviews, traceability, and rollback.
• Partner with Maritime platform teams and CorpTech to align architectures with enterprise data standards, ontologies, and compliance policies.
• Lead reliability engineering for deployed models—managing drift detection, retraining triggers, alerting, and operational SLOs.
• Mentor junior engineers and data scientists; establish best practices for MLOps, observability, data management, and secure handling of sensitive production data.

REQUIRED QUALIFICATIONS 

• Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams.
• 8+ years of experience in software or ML engineering with end-to-end delivery of production-grade AI/ML systems.
• Deep experience with MLOps: data acquisition, labeling, curation, pipeline management, model versioning, continuous integration, and model monitoring.
• Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow).
• Experience building and deploying containerized ML services using Docker and Kubernetes.
• Proficiency in data engineering, time-series data modeling, and working with semantic/ontology-driven data systems.
• Experience implementing observability for model performance, inference accuracy, and data drift.
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration.
• Excellent communication and documentation abilities; able to bridge research, platform, and production domains.
• Eligible to obtain and maintain an active U.S. Secret security clearance.

PREFERRED QUALIFICATIONS 

• Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments.
• Background with digital twins, predictive maintenance, OCR/IDP, CV, or STT model integrations.
• Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal.
• Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton).
• Experience in regulated environments (NNPI/ITAR) and secure model/data governance.
• Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale.

ABOUT THE TEAM

Anduril Maritime delivers platforms, systems, and integrated effects in the maritime domain. Our autonomous vehicles (sub-surface and surface) are the cornerstone of these capabilities, and we continually strive to push the boundaries of the possible in terms of endurance, autonomy and mission capability. The Maritime team develops and maintains core products and payloads, and adapts and applies those products to serve a wide variety of defense, IC and commercial customers in US and international markets.

ABOUT THE JOB

As a Senior Site Reliability Engineer on the Maritime Digital Shipbuilding team, you will build and operate the infrastructure that keeps our digital production systems running at full speed. You’ll develop and manage CI/CD pipelines, automate infrastructure with code, and deploy applications and machine learning models across cloud and edge environments with security, traceability, and reliability in mind.

You’ll work closely with software, data, and operations engineers to turn designs into working systems—streamlining development, improving performance, and keeping production stable as we scale. You’ll also collaborate with digital, manufacturing, and corporate technology teams across Anduril in a high-tech, fast-paced culture of innovation focused on solving real problems and delivering results.

If you’re driven to build systems that last, thrive on deep technical challenges, and want to see your work directly shape how we design, build, and sustain complex platforms, you’ll be helping build the future of digital shipbuilding and the next generation of maritime vehicles.

WHAT YOU'LL DO

• Build and Manage CI/CD Pipelines: Develop and maintain CI/CD pipelines using tools like GitHub Actions and Jfrog Artifactory to ensure seamless integration and deployment of machine learning models and applications.
• Infrastructure as Code (IaC): Utilize Terraform and Ansible to automate infrastructure provisioning and management on cloud platforms such as Azure, AWS, or Google Cloud Platform (GCP).
• Containerization and Orchestration: Implement containerization solutions with Docker and manage container orchestration using Kubernetes to ensure reliable deployment and scaling of applications.
• Model Management and Deployment: Set up and maintain model registries and feature stores (e.g., MLflow, Kubeflow), and manage deployment pipelines for both batch and real-time inference.
• Monitoring and Logging: Establish comprehensive monitoring and logging solutions using tools like ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana to ensure the smooth operation of deployment environments.
• Collaborate with Cross-Functional Teams: Work closely with development, data science, and operations teams to foster collaboration and ensure the efficient and effective deployment of machine learning models.
• Optimize Performance: Utilize parallel computing frameworks such as CUDA and OpenCL to accelerate high-performance computing tasks, ensuring timely processing of large datasets and complex simulations.

REQUIRED QUALIFICATIONS

• Advanced proficiency in programming languages (Python for scripting and integration).
• Experience with CI/CD tools like GitHub Actions, Jfrog Artifactory, and Git.
• Proficiency with IaC tools (Terraform, Ansible).
• Experience with cloud platforms (Azure, AWS, GCP).
• Proficiency in containerization (Docker) and container orchestration (Kubernetes).
• Knowledge of model registries and feature stores (e.g., MLflow, Kubeflow).
• Experience with logging and monitoring tools (ELK Stack, Prometheus, Grafana).
• Understanding of parallel computing frameworks (CUDA, OpenCL).
• Strong collaboration skills and proficiency with collaborative tools (JIRA, Confluence).
• Eligible to obtain and maintain an active U.S. Secret security clearance.

PREFERRED QUALIFICATIONS

• Previous experience in a manufacturing or industrial setting.
• Familiarity with observability concepts and tools.
• Knowledge of security best practices for DevOps and MLOps.

Why You'll Love This Role

You will work closely with the founding team and a group of highly-skilled engineers to shape the future of AI-enabled Security/Observability platforms. You will play a central role in bringing integrating cutting-edge AI/ML technologies to the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.

On top of it all you will have fun. Cribl strives to be a great place to work for everyone.

As An Active Member Of Our Team, You Will...

• Design, train, and evaluate machine learning models across a range of research and applied AI initiatives
• Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements
• Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems
• Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation
• Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation
• Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team
• Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them
• This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones

If You've Got It - We Want It

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 5+ years of industry or research experience (Master's or PhD a plus)
• Deep hands-on experience training and evaluating ML models, including language models
• Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights & Biases, Kubeflow, or similar)
• Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques
• Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize
• Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders

Salary Range ($230,000 - $275,000)

The salary for this role is dependent on geographic location. The salary offered within the range described will be based on the individual candidate’s job-related knowledge, skills, and experience.  In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), equity, and eligibility for a discretionary company-wide bonus.

#LI-JB1
#LI-Remote

We are looking for a highly motivated and versatile Senior Platform Engineer to join our platform engineering team. This unique role is central to accelerating our product development lifecycle and operational efficiency. You will be instrumental in bridging the gap between development, infrastructure, and our burgeoning AI/ML initiatives, focusing on automation, self-service infrastructure, and workflow optimization. This position is ideal for an experienced engineer who thrives on creating leverage for others and tackling complex, interdisciplinary challenges.

Key Responsibilities:

CI/CD and Automation

• Design, implement, and maintain scalable and reliable CI/CD pipelines (e.g., using GitLab CI, GitHub Actions, Jenkins, Spinnaker) to ensure rapid and safe deployments across multiple environments.
• Drive the adoption of best practices for build management, testing automation, and deployment strategies (Canary, Blue/Green, etc.).
• Minimize deployment friction and cycle time for all engineering teams.

Self-Service Infrastructure & Provisioning

• Develop and champion self-service tooling and internal platforms (leveraging tools like Terraform, Ansible, Kubernetes, or equivalent cloud provider APIs) that empower development teams to provision and manage their own infrastructure resources securely and efficiently.
• Establish guardrails and policy-as-code to ensure compliance, cost efficiency, and security across all provisioned infrastructure.

AI Infrastructure Enhancement & Support

• Collaborate closely with AI/ML Engineers to improve the underlying platform used for model training, experimentation, and serving.
• Focus on the operationalization of MLOps pipelines, including data lineage tracking, feature store integration, and production model deployment automation.
• Assist in optimizing resource allocation (e.g., GPU usage, specialized hardware) for AI workloads.

Workflow Automation & Internal Tooling

• Architect and build automated workflows and bots, primarily within communication tools like Slack or Zoom, to streamline common engineering tasks (e.g., on-call handoffs, incident response, environment status checks, approval processes).
• Develop internal APIs and services that connect disparate engineering systems to enhance communication and cross-functional transparency.

Required Qualifications

• 5+ years of professional experience in Platform Engineering, DevOps, Site Reliability Engineering (SRE), or a related discipline.
• Deep expertise in cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (Terraform strongly preferred).
• Expertise in containerization and orchestration technologies (Docker and Kubernetes).
• Proficiency in scripting and general-purpose programming languages (e.g., Python, Go, Bash).
• Extensive experience designing and managing CI/CD systems at scale.
• Proven track record of building internal developer platforms or self-service tools.

Preferred Qualifications

• Experience with AI/ML infrastructure, MLOps tooling, or data pipeline technologies (e.g., Airflow, Kubeflow, Vertex AI, SageMaker).
• Prior experience building conversational interfaces, bots, or workflow automation tools for platforms like Slack or Teams.
• Familiarity with distributed tracing, logging, and monitoring systems (e.g., Prometheus, Grafana, ELK stack, Datadog).
• Experience with security best practices in a cloud environment (IAM, network security, secrets management).

At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company’s business strategy. For this position we are targeting a base pay between $144,000 - $175,000. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience.

Archer is proud to be an Equal Opportunity employer committed to diversity and inclusivity in the workplace. All aspects of employment are decided on the basis of merit, qualifications, and business needs. We do not discriminate based upon race, color, religion, sex, sexual orientation, age, national origin, disability status, protected veteran status, gender identity or any other characteristic protected by federal, state or local laws.

Archer is committed to working with and providing reasonable accommodations to job applicants with physical or mental disabilities, and those with sincerely held religious beliefs. Applicants who may require reasonable accommodation for any part of the application or hiring process should provide their name and contact information to Archer’s People Team at people@archer.com. Reasonable accommodations will be determined on a case-by-case basis.

Shopmonkey's vision is to help every shop thrive by equipping them with the tools they need to run and grow their business. Our cloud based all-in-one shop management software takes owners and technicians from quote to cashing out a satisfied customer. Our software has a modern and intuitive UI and our backend is powered by the latest technologies so our clients can focus on the things they do best. As a Senior ML Engineer at Shopmonkey, you will be a part of a globally distributed engineering team working closely with your product and design counterparts. You will have the chance to work on the frontier of ML modeling, data pipelines, MLOps, and agentic AI systems to meet real-world auto shop needs. Shopmonkey has the structured data, workflows, and operational maturity to deliver ML powered systems that are not only intelligent but trusted and useful. You’ll move fast to bring ML systems from discovery all the way through production, helping to shape the future of the automotive care experience. For any Bay area based candidates, this would be hybrid with 2-3 days/week office at our Morgan Hill, CA office for collaboration. 

What you will do:

• Design, build, and ship production-ready ML models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems.
• Conduct end-to-end development of ML systems: data gathering, experimentation, feature engineering, model training, evaluation, deployment, and monitoring.
• Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback.
• Help design and implement shared feature stores so that reusable features can serve multiple models consistently in both batch and real-time contexts.
• Work within a modern MLOps environment to ensure scalable and reliable deployment of models.
• Contribute to training infrastructure, model versioning, and CI/CD pipelines for ML workflows.
• Work closely with data scientists and data engineers to develop data driven solutions that are high impact for businesses.
• Translate complex ML workflows into digestible updates for cross-functional stakeholders.
• Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment.
• Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization.
• Contribute to analytics and predictive features such as no-show prediction and sentiment dashboards.
• Help build and ship AI agents that help automate key auto-shop business processes.

We are looking for people who have:

• Minimum of 5+ years of industry experience in applied machine learning; advanced degrees (Master’s or PhD) may offset years of experience.
• Proven experience shipping models into production (not just proof-of-concepts or notebooks).
• Proficiency in Python; experience with ML frameworks like PyTorch or Tensorflow.
• Strong foundations in classical ML/DL. Including some of the following: regression, classification, clustering, ranking, feature engineering, model evaluation, and experimentation.
• Bachelor’s degree in a STEM field, or equivalent practical experience.
• Strong collaboration and communication skills—comfortable working with PMs, designers, engineers and other cross functional team members.

Nice to have:

Candidates should have some experience in one or more of the following areas.

• Understanding of MLOps principles: model versioning, orchestration, evaluation, monitoring, model serving, and CI/CD for ML. 
• Understanding of MLOps, and experience with modern tooling like MLFlow, DVC, Airflow, etc.
• Experience with LLMs and NLP frameworks (e.g., TensorFlow, Hugging Face, LangChain).
• Experience with/interest in LLM workflows and agentic workflows
• Cloud infrastructure experience, (e.g. GCP, AWS).
• Familiarity with vector databases (e.g. Pinecone, pgvector) and embedding-based retrieval or similarity search.
• Strong SQL skills for working with large-scale data.
• Experience designing or contributing to feature stores (e.g. Feast, VertexAI Feature Store, Tecton) for shared, reusable feature pipelines.

Bonus Points:

• Prior experience working at a high growth startup.
• Experience building consumer-facing agents in vertical SaaS, in the automotive industry (business or consumers).
• Background in data processing or real-time analytics.
• Experience with Snowflake or other large-scale data warehouse solutions.

In the United States, the range is typically a salary of $165,000 to $200,000 + bonus + equity + benefits. The range provided is Shopmonkey’s reasonable estimate of the base compensation for this role. The actual amount will be based on job-related and non-discriminatory factors such as location, experience, training, skills, and abilities. Consult with your Recruiter during the initial call to determine a more targeted range based on these job-related factors. In addition to this base compensation company stock options and benefits as outlined below are included.

Who We Are 

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale—empowering a safer, richer, and more connected future.

About the Role

Nuro is seeking a Software Engineer with expertise in large-scale infrastructure, workload orchestration, and data processing to join our ML Infrastructure team. In this role, you will focus on building and evolving the core platform that provides researchers and engineers with seamless access to compute and data resources. You will be responsible for executing the technical strategy for automated resource provisioning, high-performance workload scheduling, and efficient feature management to accelerate the Nuro Driver™ development lifecycle.

About the Work

You will build the foundation that powers Nuro’s model development from experimentation to production. Key responsibilities include:

• Resource Provisioning & IaC: Scaling automated infrastructure-as-code (IaC) pipelines to manage thousands of GPU/CPU nodes across diverse environments.
• Intelligent Scheduling: Designing and optimizing workload orchestration to maximize hardware utilization, minimize job wait times, and handle massive-scale distributed training.
• Data & ETL: Designing robust pipelines for the extraction and transformation of petabyte-scale sensor and telemetry data into ML-ready formats.
• Feature Management: Implementing robust feature caching and storage solutions to reduce redundant computations and ensure low-latency access to pre-computed features.
• Platform Abstraction: Contributing to a unified ML platform that abstracts complex cloud infrastructure for end-users.

About You

• Experience: 5+ years of professional experience in ML Infrastructure, Backend Platform Engineering, or Distributed Systems.
• Resource Provisioning: Deep familiarity with modern Infrastructure-as-Code and provisioning tools such as Terraform, Pulumi, or Crossplane.
• Workload Scheduling: Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, KubeRay, Ray, Slurm, or Volcano).
• Distributed Data Processing: Proficiency in at least one distributed processing framework, such as Apache Spark or Apache Beam, for large-scale data extraction and transformation.
• Feature Management: Experience implementing or maintaining feature stores and caching layers (e.g., Feast, Hopsworks, or Redis-based custom caching).
• Systems Design: A strong understanding of distributed systems, networking, and storage bottlenecks in the context of high-performance computing.

Bonus Points

• Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities).
• Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading.
• Knowledge of cost-optimization strategies for large-scale GPU clusters in public clouds (AWS, GCP, or Azure).

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,930 and $291,150 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #li-dnp

Who We Are 

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale—empowering a safer, richer, and more connected future.

About the Role

Nuro is seeking a Software Engineer with expertise in large-scale infrastructure, workload orchestration, and data processing to join our ML Infrastructure team. In this role, you will focus on building and evolving the core platform that provides researchers and engineers with seamless access to compute and data resources. You will be responsible for executing the technical strategy for automated resource provisioning, high-performance workload scheduling, and efficient feature management to accelerate the Nuro Driver™ development lifecycle.

About the Work

You will build the foundation that powers Nuro’s model development from experimentation to production. Key responsibilities include:

• Resource Provisioning & IaC: Scaling automated infrastructure-as-code (IaC) pipelines to manage thousands of GPU/CPU nodes across diverse environments.
• Intelligent Scheduling: Designing and optimizing workload orchestration to maximize hardware utilization, minimize job wait times, and handle massive-scale distributed training.
• Data & ETL: Designing robust pipelines for the extraction and transformation of petabyte-scale sensor and telemetry data into ML-ready formats.
• Feature Management: Implementing robust feature caching and storage solutions to reduce redundant computations and ensure low-latency access to pre-computed features.
• Platform Abstraction: Contributing to a unified ML platform that abstracts complex cloud infrastructure for end-users.

About You

• Experience: 3+ years of professional experience in ML Infrastructure, Backend Platform Engineering, or Distributed Systems.
• Resource Provisioning: Deep familiarity with modern Infrastructure-as-Code and provisioning tools such as Terraform, Pulumi, or Crossplane.
• Workload Scheduling: Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, KubeRay, Ray, Slurm, or Volcano).
• Distributed Data Processing: Proficiency in at least one distributed processing framework, such as Apache Spark or Apache Beam, for large-scale data extraction and transformation.
• Feature Management: Experience implementing or maintaining feature stores and caching layers (e.g., Feast, Hopsworks, or Redis-based custom caching).
• Systems Design: A strong understanding of distributed systems, networking, and storage bottlenecks in the context of high-performance computing.

Bonus Points

• Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities).
• Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading.
• Knowledge of cost-optimization strategies for large-scale GPU clusters in public clouds (AWS, GCP, or Azure).

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $160,360 and $240,540 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.

About the team

Applied Intuition is building the toolchain for autonomy across air, land, sea, space and spectrum. The Axion product family is a modular, all-domain autonomy toolchain: simulation (Axion Sim), hardware/software integration (Axion Integrate), MLOps for ATR models (Axion MLOps), mission control UIs (Axion Mission Control), etc. 

The Axion team is mission-critical: enabling development, test, integration and deployment of collaborative autonomous systems in contested environments. You’ll be working at the intersection of high-fidelity simulation, autonomy software, hardware platforms, and human-machine interfaces.

About the role

As an Engineering Manager on the Axion team, you will lead a cross-functional engineering group (software, simulation, integration, UI/UX) to deliver high-impact autonomy toolchain capabilities. You will drive engineering execution, mentor and grow a team of engineers, define technical roadmaps, hold the bar on quality, and collaborate closely with product, operations, hardware/integration teams, and defense customers.

Your work will influence the architecture, scalability, performance, and field-readiness of Axion products.

At Applied Intuition, you will:

• Lead, coach, and grow a team of engineers (individual contributors, leads) working on one or more Axion modules (e.g., simulation engine, mission-control UI, hardware integration pipeline)
• Define and own the technical roadmap for your area: translate product vision into deliverables, ensure alignment with overall Axion strategy
• Drive software engineering best practices: architecture design, code reviews, CI/CD, test automation, performance/scalability, quality metrics
• Manage project execution: planning sprints/releases, monitoring progress, identifying risks/blockers, ensuring delivery of milestones on time and to quality
• Work with product management and UX/design to refine requirements, make trade-offs, and ensure that the engineering roadmap maps to user/customer value and field readiness
• Collaborate with hardware/integration and operations teams to ensure seamless delivery of autonomy toolchain across simulation, hardware-in-the-loop (HIL), live platforms
• Ensure the team is set up for high-impact: recruiting top talent, setting clear goals, fostering a culture of ownership, iteration, and innovation
• Communicate status, risk, and outcomes to senior leadership; escalate as needed; provide engineering input for business decisions
• Stay abreast of emerging technologies: simulation, autonomy, AI, real-time systems, human-machine interfaces, and help the team apply them

We’re looking for someone who has:

• Bachelor’s degree in Computer Science, Engineering, or related field (advanced degree preferred)
• Proven experience (e.g., 3 – 5+ years) in software engineering roles with increasing responsibility, and at least 1 – 2 years in a leadership role (team lead, manager) delivering complex systems
• Strong technical background: architecture, design, implementation and delivery of large-scale, high-performance software systems (ideally simulation, real-time, autonomy, robotics, or hardware integration)
• Experience managing full software development lifecycle: requirements, design, implementation, testing, deployment, maintenance
• Experience in mentoring and growing engineering teams: recruiting, performance management, career development, building high-performing teams
• Excellent collaboration and communication skills: able to work cross-functionally with product, UX/design, hardware/integration, operations, senior leadership
• Comfort in a fast-paced, highly-ambiguous environment; ability to make thoughtful trade-offs between speed, quality, and scalability
• US citizenship. You must already have or be eligible to receive an active US Top Secret security clearance upon onboarding to this role

Nice to have:

• Experience in autonomy, robotics, simulation or unmanned systems (air/land/sea) and tool-chain development
• Familiarity with multi-domain systems (e.g., air/land/sea/spectrum) and heterogeneous fleets/platforms
• Familiarity with real-time systems, sensor fusion, hardware/software integration, HIL/SIL testing, cloud infrastructure, large datasets
• Demonstrated ability to lead architecture/design of modular, scalable software systems with high performance constraints

Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.

Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position.

Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $204,000 - $343,000 USD annually.

CSQ227R175

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. government Top Secret clearance is required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Resident Solutions Architect (RSA) on our Professional Services team, you will work with customers on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• Handle a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues
• Collaborate with the Databricks Technical, Project Manager, Architect and Customer teams to ensure the technical components of the engagement are delivered to meet customer's needs
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues

What we look for:

• US Top Secret Clearance Required TS/SCI Preferred this position
• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines
• Documentation and white-boarding skills
• Experience working with clients and managing conflicts
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects
• Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience
• Ability to travel up to 30% when needed

CSQ227R175

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. government Top Secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Resident Solutions Architect (RSA) on our Professional Services team, you will work with customers on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• Handle a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues
• Collaborate with the Databricks Technical, Project Manager, Architect and Customer teams to ensure the technical components of the engagement are delivered to meet customer's needs
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues

What we look for:

• US Top Secret Clearance Required TS/SCI Preferred this position
• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines
• Documentation and white-boarding skills
• Experience working with clients and managing conflicts
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects
• Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience
• Ability to travel up to 30% when needed

Responsibilities

We are seeking a product-minded Staff Software Engineer to join our San Francisco team focused on building the next generation of developer products. This role is at the forefront of innovation, specifically creating tools and platforms for developers who are building LLM and Agentic applications. You will be instrumental in shaping the future of how developers interact with and build on top of Workato's ecosystem in the age of AI. You will collaborate closely with product, engineering, and developer relations teams to deliver robust, scalable, and intuitive services. This role requires a high technical bar, plus an emphasis on developer ergonomics, so we can deliver a delightful developer experience.

• Design, build, and maintain robust, scalable, and high-quality developer-facing products and APIs for LLM and Agentic application development.

• Collaborate closely with product managers, designers, developer relations and other engineers to translate developer needs and product requirements into elegant technical solutions.

• Write clean, maintainable, and well-tested code, and contribute to a high standard of engineering excellence within the team.

• Implement and improve validation, monitoring, and observability for ML services within the sandbox environment.

• Drive the full development lifecycle: from ideation and architecture to deployment and continuous improvement.

• Continuously evaluate and optimize existing implementations for performance, scalability, and developer usability.

• Identify and resolve bottlenecks, bugs, and technical challenges in a fast-paced, iterative environment.

• Conduct code reviews and mentor team members to uphold high engineering standards.

• Stay at the cutting edge of AI, LLMs, and developer tooling, bringing new ideas and technologies to the team.

• This role is on-site in our San Francisco Office.

Requirements

Qualifications / Experience / Technical Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent industry experience.

• 8+ years of experience of professional software development experience, with a proven track record of shipping high-quality products.

• You are a product-minded engineer who thinks deeply about the end-user. You have strong opinions about what makes a great developer experience.

• You have experience building developer-facing products, APIs, SDKs, or CLIs.

• You are proficient in one or more modern programming languages such as Go, Python, Ruby, or JavaScript and hands-on experience with LLM models.

• Proven track record of deploying and maintaining ML services in production environments is a strong plus.

• Solid understanding of API design, developer tooling, software architecture, data structures, and algorithms.

• LLMops/MLops experience is a strong plus, especially in developer-facing products.

• Experience building or supporting developer platforms, APIs, or sandbox environments is highly desirable.

Soft Skills / Personal Characteristics

• Excellent written and verbal communication skills in English.

• Collaborative team player with a passion for empowering developers and improving developer experience.

• Adaptable, proactive, and creative—comfortable working in a dynamic, startup-like environment.

• Strong problem-solving skills and a continuous improvement mindset.

Join us in San Francisco and help shape the future of developer innovation with AI-powered sandbox experiences!

(REQ ID: 2710)

Responsibilities

We are seeking a product-minded Senior Software Engineer to join our San Francisco team focused on building the next generation of developer products. This role is at the forefront of innovation, specifically creating tools and platforms for developers who are building LLM and Agentic applications. You will be instrumental in shaping the future of how developers interact with and build on top of Workato's ecosystem in the age of AI. You will collaborate closely with product, engineering, and developer relations teams to deliver robust, scalable, and intuitive services. This role requires a high technical bar, plus an emphasis on developer ergonomics, so we can deliver a delightful developer experience.

 In this role, you will also be responsible to:

• Design, build, and maintain robust, scalable, and high-quality developer-facing products and APIs for LLM and Agentic application development.

• Collaborate closely with product managers, designers, developer relations and other engineers to translate developer needs and product requirements into elegant technical solutions.

• Write clean, maintainable, and well-tested code, and contribute to a high standard of engineering excellence within the team.

• Implement and improve validation, monitoring, and observability for ML services within the sandbox environment.

• Drive the full development lifecycle: from ideation and architecture to deployment and continuous improvement.

• Continuously evaluate and optimize existing implementations for performance, scalability, and developer usability.

• Identify and resolve bottlenecks, bugs, and technical challenges in a fast-paced, iterative environment.

• Conduct code reviews and mentor team members to uphold high engineering standards.

• Stay at the cutting edge of AI, LLMs, and developer tooling, bringing new ideas and technologies to the team.

Requirements

Qualifications / Experience / Technical Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent industry experience.

• 5+ years of experience of professional software development experience, with a proven track record of shipping high-quality products.

• You are a product-minded engineer who thinks deeply about the end-user. You have strong opinions about what makes a great developer experience.

• You have experience building developer-facing products, APIs, SDKs, or CLIs.

• You are proficient in one or more modern programming languages such as Go, Python, Ruby, or JavaScript and hands-on experience with LLM models.

• Proven track record of deploying and maintaining ML services in production environments is a strong plus.

• Solid understanding of API design, developer tooling, software architecture, data structures, and algorithms.

• LLMops/MLops experience is a strong plus, especially in developer-facing products.

• Experience building or supporting developer platforms, APIs, or sandbox environments is highly desirable.

Soft Skills / Personal Characteristics

• Excellent written and verbal communication skills in English.

• Collaborative team player with a passion for empowering developers and improving developer experience.

• Adaptable, proactive, and creative—comfortable working in a dynamic, startup-like environment.

• Strong problem-solving skills and a continuous improvement mindset.

(REQ ID: 2428)

Responsibilities

We are seeking a product-minded Software Engineer to join our San Francisco team focused on building the next generation of developer products. This role is at the forefront of innovation, specifically creating tools and platforms for developers who are building LLM and Agentic applications. You will be instrumental in shaping the future of how developers interact with and build on top of Workato's ecosystem in the age of AI. You will collaborate closely with product, engineering, and developer relations teams to deliver robust, scalable, and intuitive services. This role requires a high technical bar, plus an emphasis on developer ergonomics, so we can deliver a delightful developer experience. You will also be responsible to:

• Design, build, and maintain robust, scalable, and high-quality developer-facing products and APIs for LLM and Agentic application development.

• Collaborate closely with product managers, designers, developer relations and other engineers to translate developer needs and product requirements into elegant technical solutions.

• Write clean, maintainable, and well-tested code, and contribute to a high standard of engineering excellence within the team.

• Implement and improve validation, monitoring, and observability for ML services within the sandbox environment.

• Drive the full development lifecycle: from ideation and architecture to deployment and continuous improvement.

• Continuously evaluate and optimize existing implementations for performance, scalability, and developer usability.

• Identify and resolve bottlenecks, bugs, and technical challenges in a fast-paced, iterative environment.

• Conduct code reviews and mentor team members to uphold high engineering standards.

• Stay at the cutting edge of AI, LLMs, and developer tooling, bringing new ideas and technologies to the team.

Requirements

Qualifications / Experience / Technical Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent industry experience.

• 3+ years of experience of professional software development experience, with a proven track record of shipping high-quality products.

• You are a product-minded engineer who thinks deeply about the end-user. You have strong opinions about what makes a great developer experience.

• You have experience building developer-facing products, APIs, SDKs, or CLIs.

• You are proficient in one or more modern programming languages such as Go, Python, Ruby, or JavaScript and hands-on experience with LLM models.

• Proven track record of deploying and maintaining ML services in production environments is a strong plus.

• Solid understanding of API design, developer tooling, software architecture, data structures, and algorithms.

• LLMops/MLops experience is a strong plus, especially in developer-facing products.

• Experience building or supporting developer platforms, APIs, or sandbox environments is highly desirable.

Soft Skills / Personal Characteristics

• Excellent written and verbal communication skills in English.

• Collaborative team player with a passion for empowering developers and improving developer experience.

• Adaptable, proactive, and creative—comfortable working in a dynamic, startup-like environment.

• Strong problem-solving skills and a continuous improvement mindset.

(REQ ID: 2242)

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.

Compensation

232k-260k base + variable + equity

Founded in Silicon Valley in 2009 by Marc Andreessen and Ben Horowitz, Andreessen Horowitz (aka a16z) is a venture capital firm that backs bold entrepreneurs building the future through technology. We are stage agnostic. We invest in seed to venture to growth-stage technology companies, across AI, bio + healthcare, consumer, crypto, enterprise, fintech, games, and companies building toward American dynamism. a16z has $100B+ under management across multiple funds.

We’ve established a team that is defined by respect for the entrepreneur and the company-building process; we know what it’s like to be in the founder’s shoes. We’ve invested in companies like Anduril, Airbnb, Coinbase, Cursor, Databricks, Deel, Figma, GitHub, Roblox, SpaceX, and Stripe. Our team is at the forefront of new technology, helping founders and their companies impact and change the world.

The Role

You will join a team of highly autonomous, senior ICs developing both software capabilities for the firm’s investing and operating functions, and external-facing products for the firm’s partners, portfolio, and investors. This role specifically involves leading the technical strategy, architecture, and implementation of machine learning systems that power the firm’s internal and external-facing applications. You will be responsible for the entire model lifecycle, from feature engineering and training to large-scale, low-latency serving and robust MLOps infrastructure.

The Applied Software Group operates in the mode of an internal startup. We own an exceptionally tight feedback loop and take on requests from all corners of the firm. This is a high-visibility role that requires a clear architectural vision, strong software engineering fundamentals, exacting communication skills, and the ability to engage and educate our firm on the technology we are developing and shipping.

This role requires an in-office presence 2-3 days a week in our San Francisco, CA office.

To join our team, you should be excited to:

• Architect, build, and maintain scalable and reliable ML production pipelines (data, training, and inference)
• Collaborate with the Applied Software Group (ASG) team to integrate ML models into user-facing products serving the investing partnership and our operating teams
• Set and evolve MLOps standards for the team, including producibility, automated testing, CI/CD, observability, and model governance across the firm.
• Research and lead the implementation of model serving strategies (e.g., real-time APIs, batch processing) to meet strict latency and throughput requirements
• Drive the technical roadmap for core ML infrastructure, influencing platform choices and building cloud-native systems primarily on GCP.
• Operate with high autonomy, owning outcomes end-to-end, proactively identifying gaps, and raising the technical bar for the team.
• Set a high standard for code quality and system design, producing clear, principled, and extensible solutions that scale with the firm.
• Bring positive energy in every interaction with the team and foster inclusive collaboration
• Learn the inner workings of VC and work closely with the investing partnership

Minimum Qualifications

• 2+ years of professional experience as a Machine Learning Engineer or similar role.
• Strong mathematical intuition and problem-solving skills
• Expertise in Python and its statistical modeling ecosystem (e.g. NumPy, scikit-learn, etc.)
• Solid experience with major ML frameworks like PyTorch, TensorFlow, or JAX
• Proven experience deploying and managing ML models in a production environment using tools like Kubeflow, MLflow, Airflow, or TFX
• Strong understanding of containerization technologies (e.g., Docker, Kubernetes/GKE) as applied to ML serving
• Familiarity with cloud computing environments (preferably GCP), including data storage and compute services
• Low ego, high empathy, and the capacity to collaborate effectively with diverse teams

The anticipated salary range for this role is between $341,000 - $422,000, actual starting pay may vary based on a range of factors which can include experience, skills, and scope.

This role is eligible to participate in the a16z carry program and various discretionary bonus programs as well as benefit and perquisite plans including health, dental, vision, disability, life insurance, 401K plan, vacation, and sick leave.

About the Team: 

Relativity Space is on a mission to better connect humanity to space and the universe beyond our planet. With decades of experience scaling world-class technology organizations like Google, CEO Eric Schmidt is guiding Relativity into its next phase: advancing Terran R toward launch and building the foundation for long-term impact. 

The Terrestrial Software team is building the foundation for an automated rocket factory and integrated launch platform. Their mission is to automate and streamline workflows across the entire lifecycle of Terran R, from raw material intake to launch operations and eventually manufacturing on Mars. Today, that means partnering directly with teams across design, materials, manufacturing, and test and launch to design, implement, and deploy end user enterprise-wide applications, industrial automation, data analytics infrastructure, and next-generation AI to solve real problems and accelerate progress. Long term, the team is laying the groundwork for a modular, scalable software platform that can power highly autonomous operations on Earth and beyond. This is a team for builders and thinkers who thrive on cross-functional impact and want to shape the digital backbone of our future in space.

About the Role:

We are looking for a Senior SRE / DevOps Engineer to join our growing team at our headquarters. In this role, you will be part of an SRE team supporting our company’s efforts in expanding our automation and development platform across our manufacturing teams. Your mission will be to design and implement our platform to enable seamless software deployments, maintenance, security and scalability, to champion and instill best practices and tools throughout the company and working closely with development and teams to support best practices and unlock scaled automation.

• Support infrastructural growth across multiple sites (Cloud and On-Premises)
• Ensure continued up-time on our services, processes, and infrastructure throughout the company and across all our sites
• Develop roadmaps for automated software deployments across the company
• Champion automation to reduce toil and increase development velocity
• Help define and instrument Service-Level Objectives
• Leverage Configuration Management to build and maintain consistency across services
• Apply infrastructure-as-code methodologies across configuration, orchestration, and elsewhere

About You:

• 3+ years of Similar Site Reliability Engineering, Automation or DevOps role
• Kubernetes ecosystem experience/familiarity
• Proficient in cloud services and architectures (GCP preferred)
• Knowledge of deployment and infrastructure-as-code frameworks (e.g., Helm, Terraform)

Nice to haves but not required:  

• Advanced proficiency with Kubernetes
• Experience with deployments across cloud and on-premises environments
• Familiarity with GitLab
• Heavy experience with production monitoring, observability best practices and tools, log aggregation/analysis (e.g., Prometheus, Grafana, Loki)
• Experience with jFrog Artifactory
• Knowledge of Configuration Management tools (e.g., Ansible, Puppet, others)
• Experience with MLOps or Machine Learning Frameworks

We’re looking for a Staff Product Manager for Developer Platforms who doesn’t just ship features—but defines what should exist in the first place. This role owns the end-to-end experience for developers building on Workato: from first API call to production-scale agent systems.

This is not a roadmap execution role. You will define the platform strategy, make hard tradeoffs, and build a product that developers choose in a competitive market.

Responsibilities

We are seeking a strategic Staff Product Manager to lead our developer-facing products for LLM and Agentic applications. In this pivotal role, you will be the bridge between our developers, our business goals, and our engineering teams, driving the vision and execution of next-generation AI-powered automation and integration tools on the Workato platform.

You will be responsible for the entire product lifecycle, from identifying market opportunities and defining the product vision to working with engineering to deliver solutions that empower developers. This role requires someone who has successfully built and shipped developer products before, with a deep understanding of developer ergonomics and an instinct for what separates good developer tools from truly exceptional ones.

This is a critical role for someone with a proven track record in developer experience, a strong understanding of the AI landscape, and the ability to translate complex technical capabilities into tangible business value. You should know what great developer tools look and feel like because you've built them yourself. You will also be responsible to:

• Define and champion the product vision, strategy, and roadmap for Workato's AI and LLM-focused developer products.

• Partner closely with engineering, design, marketing, and sales to deliver exceptional AI products and drive go-to-market success.

• Build deep insights into developer workflows, needs, and pain points through direct customer engagement, user research, and community feedback.

• Transform developer insights and business requirements into actionable PRDs, user stories, and technical specifications.

• Own the product backlog and feature prioritization, making data-driven decisions that balance immediate developer needs with long-term platform vision.

• Lead competitive intelligence and market analysis to identify emerging trends and ensure Workato stays ahead in AI developer tooling.

• Champion the product vision both internally and externally, engaging with developer communities, conferences, and key technical stakeholders.

• Establish and monitor key product metrics including developer satisfaction, API adoption, and platform engagement to drive continuous improvement.

• Drive developer adoption metrics and optimize onboarding experiences to reduce time-to-first-value.

Requirements

Qualifications / Experience / Technical Skills

• 7+ years in product management, including owning a developer-facing product or platform end-to-end—from defining the vision to shipping and iterating
  
  
• Strong product instincts for developer platforms, with a clear point of view on what differentiates winning products (DX, capabilities, ecosystem, distribution)
  
  
• Experience driving adoption with external developers, including improving key stages of the funnel: discover → try → activate → scale
  
  
• Hands-on experience building or managing APIs, SDKs, CLIs, or extensibility frameworks, with an understanding of how these impact real developer workflows
  
  
• Demonstrated developer empathy, grounded in observing and improving real workflows (e.g., working with APIs, CI/CD, logs, IDEs)
  
  
• Comfortable going deep with engineering on system design and tradeoffs, including constraints, scalability, and abstraction decisions
  
  
• Familiarity with modern AI/LLM or agent-based development workflows (e.g., orchestration, prompt iteration, evaluation, observability)
• Awareness of how developer workflows are evolving (AI copilots, APIs-first development, faster iteration cycles), with a perspective on where things are headed

Nice to Have

• Hands-on experience building or using LLM-powered applications and familiarity with AI frameworks like LangChain, LlamaIndex, or similar.

• Experience with integration, automation, or MLOps platforms.

• Active participation in developer communities through writing, speaking, or open-source contributions.

Soft Skills / Personal Characteristics

• You are a strategic thinker who can connect technology with business impact and craft a compelling product narrative.

• You have excellent leadership and communication skills, with the ability to influence and align cross-functional teams.

• You are comfortable with ambiguity and rapid iteration, thriving in fast-paced, innovative environments.

(REQ ID: 2249)

About the role

In the role of senior machine learning engineer, you will report to the director of data science and machine learning in the Center for Data and Insights, working from Los Angeles or San Francisco area in California.

In this Machine Learning Engineer role focused on Algorithm Development and End-to-End Production at our VOD streaming company, you will lead the creation and deployment of scalable ML models to detect account sharing fraud, drawing from user behavior data like geolocation, concurrent sessions, and device profiles. This position is essential for collaborating with data scientists, product analysts, and data engineers,  translating research prototypes into production systems that enhance detection accuracy while minimizing user disruption, aligning with proven strategies from streaming giants to boost subscriber growth.

We work a hybrid schedule, in-office three days a week; Tuesday, Wednesday, Thursday. 

Core Areas of Responsibility

• Design and implement machine learning algorithms, including anomaly detection models, to identify unauthorized account sharing in real-time.
• Develop end-to-end ML pipelines for data preprocessing, model training, evaluation, and deployment using cloud platforms.
• Optimize models for performance, scalability, and efficiency to handle high-volume streaming data.
• Integrate ML solutions with existing systems via APIs and establish monitoring for model drift and retraining.
• Collaborate on A/B testing and iteration to refine algorithms based on feedback and evolving evasion tactics.

About You

• Experience: You bring 5+ years of hands-on experience in ML engineering, with a track record in fraud or anomaly detection systems, ideally in online, consumer facing products and services.
• Technical Skills: Expert in Python and frameworks like TensorFlow, PyTorch, or XGBoost, with proficiency in MLOps tools such as MLflow, Docker, and cloud services like AWS SageMaker, Databricks MLFlow, etc. Experience with cloud computing infra and tooling such as AWS/GCP.
• Cross-Functional Collaborations: Experienced in working with data scientists, engineers, and product teams to deploy models that support business goals like revenue optimization.
• Communication Skills: Strong ability to document technical processes and explain algorithm decisions to diverse stakeholders for seamless integration.
• Education Background: Possess a master's degree in computer science, machine learning, or a related quantitative field, with certifications in cloud ML being a plus.
• Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Educational Background: Graduate degree (MS or PhD) in Computer Science, Statistics, or a related field.

About the Team

Our team is composed of passionate Machine Learning Engineers and Data Scientists who have already made a significant impact across our product offerings, content strategy, and user engagement metrics. As we expand our scope, our team is poised to become the cornerstone of innovation and growth across various business verticals within the company. We are dedicated to leveraging advanced machine learning techniques to continue transforming how users interact with and enjoy our offering of video contents and all other services.

Why you will love working at Crunchyroll

In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:

#LifeAtCrunchyroll #LI-Hybrid

About the role

In the role of Senior Machine Learning Engineer for Recommendation and Personalization, you will report to the Director of Data Science and Machine Learning in the Center for Data and Insights, working from Los Angeles or San Francisco area in California. You will lead the research, development, and test of advanced models, and work together with product and engineering partners to deliver tailored experiences for our fans across the anime ecosystem, including anime video recommendations, digital manga suggestions, merchandise personalization, anime-themed gaming, music, and more. This position will collaborate closely with scientists, engineers, product owners, to prototype innovative algorithms, evaluate their impact, and integrate them into production systems that drive user engagement, retention, discovery, and satisfaction.

We work a hybrid schedule, in-office three days a week; Tuesday, Wednesday, Thursday. This position can be based in our Los Angeles or San Francisco offices.

Core Areas of Responsibility

• Research, design, and implement machine learning algorithms for recommendation systems, including collaborative filtering, content-based models, and deep learning, and generative recommendation approaches to personalize content discoveries.
• Co-develop end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment using scalable cloud platforms with engineers.
• Optimize models for accuracy, latency, and scalability to handle massive user interaction data from streaming and multi-platform, multi-domain experiences across the fandom.
• Integrate personalization solutions with other services, and implement monitoring for model performance, bias detection, and automated retraining.
• Collaborate on A/B testing, experimentation, and iterative improvements to refine recommendations based on user feedback and evolving content trends.

About You

We get excited about candidates, like you, because you have:

Experience: You bring 8+ years of hands-on experience in applied machine learning, with a proven track record in building recommendation systems or personalization engines, ideally in media, entertainment, or e-commerce platforms.

Technical Skills: Expert in Python and frameworks like TensorFlow, PyTorch, or Scikit-learn, with proficiency in MLOps tools such as MLflow, Docker, and cloud services like AWS SageMaker, Databricks, or similar. Experience with big data tools (e.g., Spark) and cloud infrastructure (e.g., AWS/GCP) for handling large-scale datasets.

Cross-Functional Collaborations: Experienced in partnering with data scientists and analysts, engineers, and product teams to deploy models that align with business objectives like increasing user retention and content consumption.

Communication Skills: Strong ability to document research findings, explain algorithmic choices, and present results to diverse stakeholders for effective adoption.

Educational Background: Graduate degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related quantitative field, with publications or contributions in recommendation systems being a plus.

About the Team

Our team is composed of passionate Machine Learning Engineers and Data Scientists who have already made a significant impact across our product offerings, content strategy, and user engagement metrics. As we expand our scope, our team is poised to become the cornerstone of innovation and growth across various business verticals within the company. We are dedicated to leveraging advanced machine learning techniques to continue transforming how users interact with and enjoy our offering of video contents and all other services.

Why you will love working at Crunchyroll

In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:

#LifeAtCrunchyroll #LI-Hybrid

The Opportunity:

We are seeking a Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions.

The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems.

The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.

Key responsibilities include:

Develop Predictive Models for Drug Discovery

• Design and implement machine learning models to predict compound activity, selectivity, and developability.

• Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.

• Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.

• Evaluate model performance and apply appropriate validation strategies.

• Work with data engineers and ML engineers to integrate models into discovery pipelines.

Analyze Complex Scientific Data

• Perform exploratory data analysis on chemical, biological, and phenotypic datasets.

• Integrate heterogeneous datasets including:

• Chemical structure and screening data.

• High-content imaging data.

• Structural biology and molecular simulation outputs.

• Identify patterns and relationships that inform scientific hypotheses.

Collaborate with Research Scientists

• Partner with medicinal chemists to support compound design and lead optimization.

• Work with biologists to interpret experimental results and identify new target opportunities.

• Translate scientific questions into computational modeling strategies.

Required Skills, Experience and Education:

• PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.

• 4–8 years experience applying machine learning or advanced analytics to scientific datasets.

• Python and scientific computing libraries (NumPy, Pandas, SciPy).

• Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).

• Model development, validation, and evaluation methods.

• Data visualization and exploratory analysis.

• Experience working with noisy and incomplete experimental datasets.

Preferred Skills:

• Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).

• Multi-omics data analysis.

• Cloud computing environments.

• MLOps or scalable model deployment. 

  #LI-Hybrid  #LI-LN1

The Opportunity:

As an Executive Director and Head of Research Computational Biology within our Biology Function, the individual will oversee the scientific and strategic direction of systems biology research and preclinical computational biology initiatives. The individual will lead a team of computational biologists and bioinformaticians focused on acquisition, organization, and analysis of large multi-omics data sets, using both public and proprietary data sources, that will help inform treatment strategies and guide patient stratification for a growing pipeline of RAS(ON) and targeted oncology therapeutics. We are looking for an experienced leader with strong interpersonal skills and an ability to lead a strong and diverse computational biology team. Key aspects of the role include:

• Strategic vision and leadership: Develop and implement a long-term strategy for research systems biology and bioinformatic activities to support a growing pipeline of RAS(ON) and novel targeted inhibitors. Lead and supervise a team of experienced computational biologists and bioinformaticians to generate, organize, and interpret large data sets to inform therapeutic and combination strategies for patients with cancer.

• Cross-functional collaboration across research, translational, development and medical affairs groups: Work across functions to support advancement of programs from early discovery into clinical development using biological insights derived from computational approaches. Additionally, leverage clinical data from RVMD investigational agents to generate proprietary insights into RAS mutant tumor biology and RVMD RAS(ON) inhibitor mechanisms of action.

• Technical leadership and innovation: Lead a team analyzing large, high-dimensional datasets. Work with colleagues in Information Sciences and Information Systems to oversee data integration, analysis and visualization using cloud-based platforms (AWS, Azure, GCP). Direct the development and application of cutting-edge statistical analysis, AI/ML methods, generative AI models, and MLOps principles to elucidate findings and generate new insights related to biomarker discovery, drug target identification, and predictive modeling. Establish and implement modern workflow management systems (Snakemake, Nextflow) and reproducible research practices.

• External collaboration: Cultivate and manage academic and industry collaborations, including CRO partnerships. Represent the company at scientific conferences through presentations and thought leadership. Maintain an active publication record in high-impact, peer reviewed journals. Drive intellectual property strategy and patent filings related to computational discoveries.

Required Skills, Experience and Education:

• Education: A Ph.D. in systems biology, computational biology, bioinformatics, or a related biological or biomedical discipline.

• Experience: Minimum of 15+ years total experience with significant pharmaceutical/biotechnology industry and management experience.

• Scientific knowledge: Deep expertise in systems biology, computational biology, and bioinformatics, including proficiency with multi-omics data integration, modeling, and analysis. Experience with single-cell technologies, spatial transcriptomics, proteomics, and epigenomics. Oncology research experience preferred but not required.

• Technical: Advanced programming skills in R, Python, and at least one additional language (Perl, C/C++, Java). Proficiency with high-performance computing (HPC) and cloud platforms (AWS, Azure, or GCP). Knowledge of workflow management tools and version control systems. Understanding of MLOps principles, model deployment, and generative AI applications in drug discovery.

• Leadership and management: Demonstrated ability to lead, mentor, and inspire interdisciplinary teams while fostering a collaborative culture. Experience managing budgets and strategic planning at the department level. Proven track record of advancing research programs from discovery through development.

• Strategic thinking: Proven strategic thinker who can align research and drug development objectives and leverage innovative technologies effectively.

• Communication: Excellent communication and presentation skills for interacting with technical teams, senior leadership, and external partners. #LI-Hybrid  #LI-GL1

The Opportunity:

As Executive Director, AI/ML Drug Discovery Analytics, you will be a senior leader shaping the future of data-driven drug discovery at Revolution Medicines. You will define the Research’s AI/ML strategy, oversee implementation of advanced analytics across discovery and build a world-class team to unlock the full potential of our rich internal datasets and make these insights accessible to other scientist. Your leadership will directly influence early discovery innovation, pipeline acceleration, and strategic portfolio decisions.

Responsibilities include:

Technical Leadership and Innovation: You will have a deep understanding of Machine Learning Operations and AI system design.  Design and scale a robust ML infrastructure, integrating MLOps best practices to ensure seamless development, deployment, and monitoring of AI/ML models.

Drive innovation in applying deep learning, generative models, and diffusion models to drug discovery, including target prediction, hit identification, and lead optimization. Leverage RevMed’s unique datasets to generate novel hypotheses and enable data-driven decision-making across the research continuum.

Strategic vision and Leadership: Define the long-term vision for AI/ML-enabled discovery and align it with organizational priorities.  Build, mentor, and inspire a cross-disciplinary team of data scientists, computational chemists, engineers, and biologists.  Establish key performance metrics and ensure delivery of impactful solutions that accelerate portfolio progression.

External and Cross-functional Collaboration: Forge partnerships with leading academic groups, technology innovators, and compute providers to access cutting-edge platforms and methodologies. Collaborate across research, chemistry, biology, and IT to identify high-impact opportunities for AI/ML integration and ensure smooth deployment of scalable solutions.  Act as a thought leader internally and externally, representing RevMed at scientific forums and shaping the broader field of AI-driven drug discovery.  Work closely with the Chief Digital Officer and team to build and drive the research vision for advanced analytics within the enterprise framework.  Lead the cross functional analytics team to harness data science and AI to accelerate early drug discovery.  

Required Skills, Experience and Education:

• Ph.D. or Masters degree in Computer Science, Machine Learning, Physics, Math or a relevant scientific discipline with over 10+ years’ experience, exposure to drug discovery is a plus.

• Expertise in a wide variety of AI/ML-based computational techniques and developing adaptable ML workflows to solve challenging problems.

• Deep understanding of AI/ML techniques such as deep learning, reinforment learning and generative models.

• Expertise in frameworks such as PyTorch/TensorFlow, training, and fine-tuning models on GPUs.

• Track record of deploying AI/ML solutions at scale.

• Proven track record of leadership and cross-functional collaboration across ML-Scientist, Software Engineering, and MLOps.

• Previous experience leading large AI/ML projects that require engaging with collaborators across the board and with varying degrees of expertise.

• Expertise in machine learning infrastructure and MLOps, including cloud and on-prem compute environments.

• Demonstrated ability to build, scale, and lead high-performing cross-functional teams.

• Strong communication and leadership skills with the ability to bridge diverse scientific and technical disciplines.

• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills:

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

• Knowledge of oncology therapeutic area and drug discovery.

• Strong familiarity with biological and chemical datasets and compound libraries.

• Evidence of successful coaching, mentorship and development of both individuals and teams in order to build long-term organizational capability.

#LI-Hybrid  #LI-GL1

Meet our software engineers!

About the role

The Axion Data team is building the data engine to train perception models. We build the means to run models at the edge such as Automatic Target Recognition (ATR) models, then backhaul data to our cloud pipeline that ingests and manages this data to enable the continual improvement of these models.

As an engineer experienced in MLOps, you will be responsible for both our edge application which runs perception stacks and our cloud data engine which ingests video data to run the next iteration of the model before we deploy it back down to the edge. This includes optimizing our ability to run these models on both the edge and the cloud, building MLOps tooling to gain insight and visibility into the pipeline for our stakeholders.

At Applied Intuition, you will:

• Construct optimized data pipelines to run ML models
• Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles
• Integrate foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery
• Leverage software-in-the-loop and hardware-in-the-loop testing
• Interact with the DoD customer to understand their use cases, requirements, and triage needs during field events to deliver a superior customer experience

We're looking for someone who has:

• 5+ years of relevant work experience
• Familiarity with modern ML infrastructure, data-centric AI approaches and running large-scale jobs on GPUs
• Created or worked on microservices and/or databases for data-oriented software
• A hunger to learn and grow into a position of ownership and impact on a new product team
• U.S. citizenship (legally required) and eligibility to obtain a security clearance

Nice to have:

• Full-stack experience React, TypeScript Python, Golang or similar 
• Experience with Docker, Kubernetes, Opensearch and Postgres
• Direct experience with foundation models, including LLMs and VLMs, for data automation tasks
• Background in autonomous driving or robotics perception
• Experience with active learning, auto-labeling, or human-in-the-loop ML systems

Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.

Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position.

Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $125,000 - $222,000 USD annually.

By bringing together next-gen technology and the finest live data available, Genius Sports is enabling a new era of sports for fans worldwide, delivering experiences that are more immersive, interactive and personalized than ever before. Learn more at geniussports.com. 

The Role - Staff Engineer - Infrastructure Platform 

We are seeking a Staff Engineer to help lead critical initiatives of our core infrastructure platform. Genius Sports is currently in the process of aligning its various tech teams and acquisitions under one unified technical strategy. The infrastructure platform is the ground level of this change. The goal is to enable engineering teams to build, deploy, and operate Genius Sport’s massive product catalog in a consistent and cohesive manner. 

In this role, you’ll work with other InfraPlat leads to define and drive technical vision and implementation for a variety of projects. Some of these initiatives include multi-account/region Kubernetes clusters, MLOps, standardized deployments, and the company-wide authentication platform. In addition, you will engage with stakeholders from product engineering teams to scope requests, identify shared pain points within the org, and prioritize initiatives. 

What You’ll Bring

• 8+ years of experience building and operating infrastructure or devops platforms. 
• Experience architecting complex infrastructure with strict uptime and latency requirements across multiple regions.  
• Ability to navigate significant ambiguity and make sound technical decisions that hold up over time.
• Experience building applications and automations to eliminate toil for engineering teams. 
• Strong communication skills that enable you to drive platform adoption forwards for new teams.  
• Track record of making pragmatic tradeoff decisions across architecture, implementation, technical debt, and customer requests.
• Passion for mentorship and upleveling the team around you to maximize their full potential.

It’ll Be a Bonus if You Have 

• Experience managing hybrid edge infrastructure environments. 
• Experience with Rust in production. 
• History of open source contribution.
• Full stack software development experience with web applications or data pipelines.

The salary for this role is based on an annualized range of $230,000 - $260,000 USD. This role will also be eligible to take part in Genius Sports Group's benefits plan.

We enjoy an ‘office-first’ culture and maximize opportunities to collaborate, connect and learn together. Our hybrid working models differ depending on your role and location. Occasional travel may be required.

As well as a competitive salary and range of benefits, we’re committed to supporting employee wellbeing and helping you grow your skills, experience and career. Learn more about how rewarding life at Genius can be at Reward | Genius Sports.

One team, being brave, driving change 

We strive to create an inclusive working environment, where everyone feels a sense of belonging and the ability to make a difference. Learn more about our values and culture at Culture | Genius Sports.

Let us know when you apply if you need any assistance during the recruiting process due to a disability.

As we continue to push the boundaries of what is possible, we’re seeking an ML Platform / MLOps Engineer on the machine learning team to build and operate the infrastructure that powers our machine learning systems. You will work closely with machine learning scientists, protein design scientists, and engineers to enable reliable, scalable platforms for training, evaluating, and deploying large-scale generative biology models.

As an early member of the company, you’ll have significant ownership over the systems and tools that enable our research team to move quickly from experiments to production models.

What You'll Work On

• Infrastructure supporting large-scale generative models for proteins
• Systems that process massive biological datasets
• Experimentation platforms that enable rapid iteration by ML researchers
• Production services powered by machine learning models

Responsibilities

• Develop infrastructure that enables researchers to run large-scale ML training and inference workloads reliably and efficiently on GPU clusters
• Implement and maintain security best practices across our ML infrastructure, including access control, secrets management, and least-privilege policies
• Monitor and optimize infrastructure performance, reliability, and cost
• Evaluate different open source infrastructure solutions and cloud providers
• Build and maintain machine learning pipelines to support model inference workloads
• Implement CI/CD pipelines for machine learning models and services
• Develop tooling that helps researchers move quickly from experiments to production models

Qualifications

• BS in Computer Science or a related field
• 3+ years of experience building or operating production ML systems
• Experience with MLOps, ML infrastructure, or ML platform engineering
• Strong experience with cloud infrastructure (GCP preferred)
• Experience working with containerized workloads and orchestration systems (Kubernetes, Docker)
• Experience building data or ML pipelines
• Familiarity with CI/CD and infrastructure-as-code practices

Preferences (but not required)

• Experienced with the challenges of working with large scale ML models
• Experience with transitioning research ideas into production
• Familiarity with ML frameworks such as PyTorch, MLFlow
• Interested in the intersection between biology and AI

What We Offer

• High-growth opportunity with meaningful impact on the future of protein design
• Competitive compensation package with equity participation
• 401(k) with a strong employer match
• Comprehensive benefits including health/dental/vision insurance
• Generous PTO policy and commitment to work-life balance
• Professional development opportunities in a cutting-edge field at the intersection of AI and biology

Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law.

Work Authorization Requirement

Applicants must have ongoing work authorization in the United States that does not require employer sponsorship. Sponsorship will not be provided now or at any time in the future for this position.

Employment Eligibility Verification

Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire.

We are looking for a motivated Software Engineering Intern to join our Machine Learning(ML) Engineering and MLOps team in our San Francisco office. In this role, you will work alongside senior engineers and research scientists on a scoped project contributing to HeartFlow's transition toward a cloud-native, unified ML pipeline. You will get hands-on exposure to cardiac AI technologies, software architecture, and validation approaches used to run algorithms at scale. You will be assigned to a project that spans across computational geometry, microservices, and inference pipeline architectures.

Required Qualifications

• Actively pursuing a Bachelor's Degree (rising juniors and seniors preferred) in Computer Science, Data Science, Mathematics, or Engineering
• Coursework or project experience in image processing or computational geometry.
• Exposure to algorithmic/scientific development and/or deep learning applications.
• Proficiency in Python with strong foundations in class design and object-oriented programming.
• Strong foundations in data structures and algorithms.
• Excellent communication and collaboration skills.

How You Stand Out

• Proficiency in modern C++ (C++11 and up)
• Exposure to cloud environments (AWS, GCP, Azure).
• Familiarity with containerization technologies (e.g., Docker).
• Exposure to build/package management tools (e.g., Conan, poetry/uv) or infrastructure-as-code frameworks (e.g., AWS CDK)
• Exposure to the use of Gen AI for software development.
• Previous internship experience in software development

A reasonable hourly rate estimate for this position is $45 to $60.

 #LI-DNI

Vast is looking for a Staff Data Architect, reporting to the Senior Director of Information Systems, to support the development of the data infrastructure and AI-driven capabilities that will be required for the design and build of artificial-gravity human-rated space stations.

This will be a full-time, exempt position located in our Long Beach location.

Responsibilities:

• Design, architect, and implement scalable enterprise data infrastructure including data lakes, warehouses, and real-time streaming platforms
• Build and maintain robust data pipelines that feed internal AI/ML tools and enable advanced analytics across all departments
• Own end-to-end data architecture from ingestion through transformation to consumption, ensuring data quality, reliability, and security
• Integrate data from complex engineering systems (PLM/Teamcenter), manufacturing systems (MES/MOM), ERP (NetSuite), IoT/sensor networks and other enterprise systems
• Create innovative data solutions that enable AI/ML capabilities for engineering analysis, manufacturing optimization, and supply chain intelligence
• Establish data governance frameworks, standards, and best practices across the organization
• Implement MLOps infrastructure to support model training, deployment, and monitoring
• Build real-time data pipelines from shop floor systems, test equipment, and operational technology (OT) environments
• Collaborate with Engineering, Manufacturing, Supply Chain, and business teams to understand data requirements and deliver BI/AI-ready datasets
• Lead and mentor a high-performing data engineering team, establishing technical standards and fostering a culture of extreme ownership
• Evaluate and implement cutting-edge data technologies including cloud-native services, vector databases, and modern data stack tools
• Optimize data pipelines for performance, cost-efficiency, and scalability
• Implement monitoring, alerting, observability, and disaster recovery capabilities for mission-critical data systems
• Provide technical leadership and strategic guidance on data architecture to executive leadership

Minimum Qualifications:

• Bachelor's degree in Computer Science, Data Engineering, Computer Engineering, or equivalent work experience
• 8+ years of hands-on experience in data engineering, data architecture, or related fields
• 3+ years of experience in a technical leadership role leading data engineering teams or initiatives
• Proven track record of building enterprise-scale data infrastructure and production data pipelines
• Expert-level proficiency in SQL and Python
• Deep hands-on experience with cloud data platforms (AWS, Azure, or GCP) and associated data services
• Strong experience with modern data processing frameworks (Apache Spark, Kafka, Airflow, or equivalent)
• Demonstrated experience with data warehousing technologies (Snowflake, Databricks, BigQuery, Redshift, or similar)
• Experience supporting AI/ML initiatives with production-grade data pipelines and infrastructure

Preferred Skills & Experience:

• Experience in manufacturing, aerospace, defense, or hardware-intensive industries
• Background integrating data from PLM systems (Teamcenter, Windchill), ERP systems (NetSuite, SAP), and MES/manufacturing execution systems
• Hands-on experience with MLOps tools, feature stores, and machine learning data pipelines
• Knowledge of DevOps/DataOps practices including CI/CD, infrastructure as code (Terraform, CloudFormation), and containerization (Docker, Kubernetes)
• Experience with real-time streaming architectures and event-driven systems
• Familiarity with vector databases, knowledge graphs, and AI/LLM data architectures
• Understanding of dimensional modeling, data vault methodology, and modern data architecture patterns
• Contributions to open-source data engineering projects
• Experience with digital twin architectures or simulation data management
• Strong understanding of data security, compliance, and governance in regulated industries
• Excellent communication skills with the ability to translate complex technical concepts to non-technical stakeholders
• Proven ability to balance strategic vision with tactical execution in fast-paced startup environments
• Track record of extreme ownership—proactively identifying problems and driving solutions to completion

Additional Requirements:

• Ability to travel up to 10% of the time to other Vast facilities or vendor sites
• Willingness to work extended hours or weekends to support critical mission milestones and production launches
• Must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain required authorizations from the U.S. Department of State (export control requirement)

Pay Range:

• Staff Data Architect: $155,800- $221,160

CSQ227R88

Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R88

Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R88

Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R88

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R88

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R88

PLEASE NOTE:
Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification