Machine Learning Engineer

Posted 7 Days Ago
New York, NY, USA
In-Office
220K-300K Annually
Entry level
Professional Services • Consulting
The Role
Designs and operates evaluation systems for evolving machine learning models. Responsibilities include defining metrics, building data and automated judging pipelines, analyzing model failures, simulating users and environments, running experiments, and documenting findings. The role requires production-quality Python, PyTorch, reinforcement learning evaluation or simulation experience, and strong machine learning research or open-source contributions.
Summary Generated by Built In

About the company

Our client is a technology company.

The role

Raydar is recruiting for this role on behalf of our client. Design, build and run evaluation systems that measure how machine learning models perform as they change over time. Work across defining metrics, building data pipelines and automated judging, and simulating test environments, then turn the findings into concrete model improvements.

What you'll do

- Define evaluation metrics and success criteria for model behavior that shifts over time, and keep them current as methods evolve.

- Build evaluation pipelines covering data handling, labeling, versioning, reruns and automated judging.

- Read model traces and results to find real failure points and propose fixes.

- Create simulated users and environments to test models at scale.

- Own evaluation work from the initial question through pipeline, experiments and written findings.


Requirements

What we're looking for

- Experience designing, building and running complex evaluations or simulators for reinforcement learning post-training, such as LLM-as-judge setups, RL environments or simulations.

- Peer-reviewed research at leading machine learning venues, or independently released open-source projects of similar caliber.

- Production-quality Python, including testing, maintenance and a regular release cadence.

- Working knowledge of PyTorch.

Bonus points

- Hands-on time creating artificial test agents or mock user populations for assessing or tuning language models.

- Advanced study in a numerate discipline such as computing or applied maths is a plus.

- Track record of contributing to open-source projects in the machine learning research community.

- Experience with personalization, memory or identity modeling for language models.

- Experience with data pipeline tooling and SQL.


Benefits

Compensation and benefits

- Salary: $220K - $300K

- Equity

- Health, dental and vision coverage

- Retirement plan

- Flexible time off

Location and work model

- New York, NY, United States

- On-site, 5 days in-office per week

- Full-time

Skills Required

  • Experience designing, building, and running complex evaluations or simulators for reinforcement learning post-training, including LLM-as-judge systems, RL environments, or simulations
  • Peer-reviewed research at leading machine learning venues or independently released open-source projects of similar caliber
  • Production-quality Python, including testing, maintenance, and regular release cadence
  • Working knowledge of PyTorch
  • Experience creating artificial test agents or mock user populations for assessing or tuning language models
  • Advanced study in a numerate discipline such as computing or applied mathematics
  • Track record of contributing to open-source projects in the machine learning research community
  • Experience with personalization, memory, or identity modeling for language models
  • Experience with data pipeline tooling and SQL
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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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