Machine Learning Engineer

Posted 12 Days Ago
San Francisco, CA, USA
In-Office
150K-250K Annually
Entry level
Professional Services • Consulting
The Role
Design, train, evaluate, and deploy machine learning models that assess complex technical work across images, geometry, and interaction logs. Translate expert judgment into training signals, labels, datasets, and evaluation criteria. Manage the full learning loop, analyze model failures, collaborate with domain specialists, and deliver production models for live decision-making using Python and PyTorch.
Summary Generated by Built In

About the company

Our client is a fast-growing software company.

The role

Raydar is recruiting for this role on behalf of our client. Own the design and delivery of machine learning models that automatically evaluate complex technical work. Partner closely with domain specialists to turn expert judgment into structured training signals, and take models from research through to production use.

What you'll do

- Build and train models that reason over several types of technical data, including images, geometry and interaction logs.

- Translate how skilled practitioners approach problems into structured criteria a model can apply.

- Run the full learning loop: generate data, capture trajectories, analyze failures, curate datasets and evaluate continuously.

- Work side by side with subject matter experts to turn their judgment into structured labels.

- Deploy models into production pipelines that support live decisions.


Requirements

What we're looking for

- Experience building and training models from the ground up, not only applications on top of third-party models.

- Track record of shipping machine learning on multimodal data in a single pipeline.

- BS or MS in computer science, machine learning or a related quantitative field.

- Solid theoretical and practical command of neural network methods and current large-model approaches.

- Ability to turn expert judgment into well-defined machine learning problems.

- Comfort working with Python and PyTorch.

- Hands-on, autonomous working style suited to an early-stage environment, with openness to occasional extra hours when something urgent arises.

Bonus points

- Experience on the machine learning side of a CAD or generative design company.

- Machine learning experience at a chip, EDA or hardware company.

- Familiarity with reinforcement learning from human feedback, reward modeling or policy optimization.


Benefits

Compensation and benefits

- Base salary: USD 150,000 to 250,000 per year

- Equity

Location and work model

- San Francisco, CA, United States

- Hybrid, minimum 3 days per week in office

- Full-time

Skills Required

  • Experience building and training machine learning models from the ground up, beyond applications built on third-party models
  • Track record of shipping machine learning models on multimodal data in a single pipeline
  • Bachelor’s or master’s degree in computer science, machine learning, or a related quantitative field
  • Strong theoretical and practical knowledge of neural network methods and current large-model approaches
  • Ability to translate expert judgment into well-defined machine learning problems
  • Proficiency with Python and PyTorch
  • Autonomous working style suitable for an early-stage environment
  • Experience with machine learning for CAD or generative design
  • Machine learning experience at a chip, EDA, or hardware company
  • Familiarity with reinforcement learning from human feedback, reward modeling, or policy optimization
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The Company
28 Employees
Year Founded: 2021

What We Do

Raydar is a talent acquisition and business consulting firm that connects world-class and emerging talent with growing organizations. It supports companies through team development, strategic hiring, and customized growth solutions, helping clients recruit roles such as engineers, product managers, executives, legal counsel, and quantitative traders. Raydar focuses on understanding each organization’s needs, culture, and long-term goals to build high-impact teams.

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