Research Engineer

ThirdLayer, Inc.; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

ThirdLayer, Inc. is seeking a Research Engineer to build end-to-end training pipelines and infrastructure that support continuous post-training runs. You will bridge research and production, turning experiments into scalable, reliable systems that run with production data.

You will work with researchers to co-design methods and systems and partner with product engineers to ship impactful improvements across our RL and data platforms.

Qualifications

• Strong software engineering fundamentals with ML depth.
• Experience with distributed training or large-scale experiments.
• Proficiency in Python and ML frameworks (PyTorch, JAX).
• Ability to turn prototyping into production-ready systems.
• Experience with RL environments or data quality systems is a plus.

Responsibilities

• Build and own pipelines for post-training runs from data ingestion to deployment.
• Turn research prototypes into reliable systems that run on production data.
• Create infrastructure to generate, scale, and version training environments and evals.
• Develop tooling to extract signals from traces and curate data for learning loops.
• Build instrumentation to inspect, debug, and understand training runs.
• Collaborate with researchers and product engineers to ship results.

Skills

Distributed training

ML systems

Python

PyTorch

JAX

Data pipelines

System design

Job description

The Role

As a Research Engineer, you'll build the systems that let us post-train models continuously: the training pipelines, environment infrastructure, and evaluation harnesses that turn research into something that runs every day. You'll sit between research and product, taking methods that work in an experiment and making them work across deployments, at scale, without babysitting. That means building the machinery for trace collection, data curation, environment generation, and the RL stack that improves agents over time. When a training run fails at 2am or an eval disagrees with reality, you're the person who figures out why. The role rewards engineers who are rigorous about ML and unusually good at systems, or the reverse.

What You're Do

• Build and own the pipelines behind our post-training runs, from data ingestion through deployment.
• Turn research prototypes into reliable, repeatable systems that run against production data.
• Build infrastructure for generating, scaling, and versioning training environments and evals.
• Develop the tooling that feeds the learning loop: searching traces, mining them for signal, labeling and curating data.
• Build the instrumentation we use to inspect, debug, and understand training runs.
• Work with researchers to co-design methods and systems, and with product engineers to ship what comes out.

What We're Looking For

• Excellent software engineering fundamentals paired with genuine ML depth. You've trained models yourself, end to end.
• Hands‑on experience with distributed training, high‑throughput data systems, or running large fleets of experiments.
• Fluency in Python and PyTorch, JAX, or similar.
• You can take a paper or a rough prototype and turn it into something shippable.
• Prior work on RL environments, evals, or data quality systems is a strong plus.
• Organized under load. You keep several workstreams coherent without dropping rigor.
• Bias toward ownership: you take a system from idea to production and stay responsible for it.