Key Responsibilities:

• Serve as the full owner of our most visible and high-impact customer pipelines, making decisions that directly impact data quality, operational efficiency, revenue, and margins
• Understand customer requirements and design data taxonomy best suited to improving model performance based on customer needs
• Build out pipeline infrastructure to ensure quality and efficiency
• Train, coach, and manage dynamic and global teams
• Build analytics to make data-driven decisions
• Partner with diverse stakeholders (Engineering + Product + Ops + Go-to-Market) to work on problems that will drive advancements for the largest LLMs in the world
• Give regular progress updates to Scale's executive team

Requirements:

• A strong technical background is required for this position. For example, a degree in Machine Learning Engineering, Computer Science, or Software Engineering
• 3+ years of experience leading a team/projects, managing operational processes, or 3+ years of experience as a SWE
• Strong problem-solving capabilities in technical environments
• Ability to come up with creative solutions to complex, ambiguous, operational, and technical problems
• Entrepreneurial experience and mindset

Compensation and Benefits: The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. You'll also receive benefits including, but not limited to: Comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous PTO.

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Key responsibilities include:

• Serving as the full owner of our most visible and high impact customer pipelines, making decisions that directly impact data quality, operational efficiency, revenue, and margins
• Understanding customer requirements and designing data taxonomy best suited to improving model performance based on customer needs
• Building out pipeline infrastructure to ensure quality and efficiency
• Training, coaching, and managing dynamic and global teams
• Building analytics to make data-driven decisions
• Partnering with diverse stakeholders (Engineering + Product + Ops + Go-to-Market) to work on problems that will drive advancements for the largest LLMs in the world

You will have:

• A strong technical background, with a degree in Machine Learning Engineering, Computer Science, or Software Engineering
• 3+ years of experience leading a team/projects, managing operational processes, or 3+ years of experience as a SWE
• Strong problem-solving capabilities in technical environments, with the ability to come up with creative solutions to complex, ambiguous, operational, and technical problems
• Entrepreneurial experience and mindset, with a passion for building things from scratch and identifying issues and executing quickly

Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role.

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Key Responsibilities:

• Shape the direction of our applied AI areas and intelligence features in our products.
• Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services.
• Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains.
• Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration.
• Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction.
• Build scalable, reusable backend systems to support GenAI products across the company.

What We’re Looking For:

• 2-8 years of machine learning engineering experience in high-velocity, high-growth companies.
• Strong track record of working with language modeling technologies.
• Proficiency in Python, TensorFlow/PyTorch, and scalable ML architectures.
• Ability to drive end-to-end model development, from research and prototyping to deployment and monitoring.
• Strong analytical and problem-solving skills, with a passion for improving AI-driven user experiences.
• Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment.

Why Join Us?

At Databricks, we are building state-of-the-art AI solutions that redefine how users interact with data and our products. You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range for this role is $190,000-$285,000 USD.

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San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $347K – $490K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-to-Market teams to inform high-stakes decisions through rigorous data science and economic modeling.

As part of our expanding Data Science function, we’re building a best-in-class forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more.

We are developing a scalable forecasting platform to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy — supporting key investment decisions and unlocking OpenAI’s full potential.

About the Role

We’re looking for a hands-on technical leader to build and lead a small but mighty team of applied data scientists and ML engineers to develop forecasting capabilities and platforms from the ground up.

Your team will be responsible for building and scaling robust, interpretable, and production-ready forecasting systems. These models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, compute consumption, and profitability.

You will work closely with Strategic Finance leaders to integrate these scalable forecasting capabilities into their operational rhythms and collaborate with the Growth team to forecast key KPIs.

This is a highly cross-functional role that requires technical excellence, strong product intuition, and business acumen. You’ll collaborate with various partners to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI.

This role is based in San Francisco, CA. We follow a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Build and manage a world-class team of applied data scientists and ML engineers to develop forecasting platforms at scale.
• Design and own the roadmap for the forecasting platform in partnership with cross-functional stakeholders.
• Collaborate closely with Strategic Finance teams to ensure forecasts are well integrated into planning processes, and executive decision-making.
• Work closely with cross-functional partners to help them adopt scientific, automated forecasting solutions.
• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.
• Research and evaluate emerging tools and techniques in the forecasting space.
• Translate technical outputs into business-aligned recommendations and decision frameworks.

You might thrive in this role if you have:

• Extensive experience building production-ready models for time series applications.
• Proven track record of building adjustable and explainable forecast models for multiple planning cycles.
• 10+ years of applied data science & engineering experience, with deep hands-on expertise in forecasting and predictive modeling.
• Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.
• Excellent communication and storytelling skills — able to simplify complexity and influence executive stakeholders.
• Team leadership experience with a track record of building high-performing, engaged teams.
• An advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).
• A self-directed, intellectually curious mindset and comfort leading ambiguous projects from 0→1.
• The ability to thrive in a dynamic environment — flexible, resourceful, and willing to do what it

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