Research Engineer

Posted 2 Days Ago
Be an Early Applicant
San Francisco, CA, USA
In-Office
200K-350K Annually
Mid level
Professional Services • Consulting
The Role
Own the machine learning experimentation lifecycle, including dataset creation, training workflows, evaluation benchmarks, model optimization, and production deployment. Collaborate with infrastructure engineers to improve compute efficiency and training stability. Apply rigorous scientific methods to assess model quality and communicate findings through articles, papers, and open-source contributions.
Summary Generated by Built In

About the company

Our client is an AI technology company.

The role

Raydar is recruiting for this role on behalf of our client. Own the experimentation cycle for improving machine learning models, from preparing data and defining evaluation criteria through training and handing results to production. Collaborate across engineering and leadership, and convert successful experiments into repeatable internal methods.

What you'll do

- Create and run training workflows that blend several learning techniques, and assess how each affects model quality and cost.

- Assemble training datasets generated from real usage, including choosing the source models and filtering the output.

- Build testing setups, scoring components and benchmarks so that gains can be measured credibly.

- Work with infrastructure engineers to reduce the compute and memory cost of experiments and keep training stable.

- Move successful research into live deployments and publish what you learn through articles, papers and open-source code.


Requirements

What we're looking for

- At least 3 years of relevant experience.

- Practical background in model training or fine-tuning, gained in industry or an academic lab, with deep expertise in at least one technique.

- Understanding of how PyTorch or JAX run training jobs, and comfort debugging unfamiliar research code and confirming it behaves as designed.

- Sound scientific rigor: sensible baselines, fair comparisons and awareness of overfitting, contamination and flawed metrics.

- Clear, specific communication about your own contributions to past work and the evidence supporting your conclusions.


Benefits

Compensation and benefits

- Base salary: USD 200,000 to 350,000 per year

- Health insurance and standard benefits

Location and work model

- San Francisco, CA, United States

- On-site

- Full-time

Skills Required

  • At least 3 years of relevant experience
  • Practical experience with model training or fine-tuning in industry or an academic lab
  • Deep expertise in at least one model training or fine-tuning technique
  • Understanding of training jobs using PyTorch or JAX
  • Ability to debug unfamiliar research code and verify that it behaves as designed
  • Scientific rigor, including sensible baselines, fair comparisons, and awareness of overfitting, contamination, and flawed metrics
  • Clear communication about personal contributions and supporting evidence
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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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