Research Engineer, Post-Training Data

Research Engineer, Post-Training Data

Location: San Fran

Employment Type: Full-time

Focus: Post-Training, Reinforcement Learning, Data Generation, Research Environments, Frontier AI

Profile: Researcher-engineer with strong ML and software engineering depth

About Our Client

Our client is building the next generation of post-training data infrastructure for frontier AI labs.

The company's core belief is that research and data production are inseparable. The best post-training data is not created through brute-force labeling or headcount alone - it requires domain expertise, research judgment, ML fluency, and the ability to build systems that scale data generation superlinearly.

Inbound demand from frontier AI customers is growing faster than the current team can support, and our client is hiring early technical talent to help meet that demand.

This is an opportunity to join a team building high-quality post-training data, reinforcement learning environments, long-horizon tasks, and domain-specific evaluation workflows for some of the most advanced AI systems in the world.

About the Role

Our client is hiring a Research Engineer, Post-Training Data to own the full lifecycle of post-training model and data work.

This role blends AI research, ML engineering, software engineering, and data production into one function. The ideal candidate is not just a researcher and not just an engineer - they are someone who can understand a domain deeply, identify what makes a task realistic and economically valuable, build the environment or data-generation system, and improve the model through high-quality post-training data.

The company is indexing heavily on research and ML horsepower, strong software ability, high slope, and data taste.

What You'll Do

• Own post-training workflows end to end, from infrastructure provisioning through data generation and curation
• Build and curate high-quality post-training data from model and environment generation processes
• Produce reinforcement learning environments and long-horizon tasks across complex domains
• Work on research-loop, science, chip, physics, and other technically deep task environments
• Build systems that scale data generation superlinearly through self-improving developer processes
• Improve data generation through better tools, workflows, automation, evaluation, and model feedback loops
• Exercise and develop strong data taste around what problems matter in a given domain
• Identify realistic, economically valuable tasks and environments that frontier AI labs will actually want
• Blend research and data production into a single technical function
• Debug ML pipelines, understand unfamiliar code quickly, and solve open-ended technical problems
• Help define the standards for what high-quality post-training data should look like across domains

What We're Looking For

• Genuine spike in ML, AI research, or a technical research domain
• Experience or strong interest in post-training, reinforcement learning, evaluations, interpretability, or related areas
• Strong software engineering ability and comfort building production-quality tools or research systems
• Ability to post-train models and build or curate high-quality post-training data
• Strong judgment around data quality, task design, and what makes a problem valuable to AI labs
• Fast problem-solving ability and strong code comprehension
• Ability to work across unfamiliar domains and ramp quickly
• High-slope learning profile with strong technical curiosity
• Comfort operating in ambiguous, research-heavy environments
• Evidence of deep commitment, strong output, and public artifacts or meaningful technical work

Ideal Background

Our client is especially interested in candidates who combine:

• Domain or research expertise in a field such as neuroscience, physics, chemistry, systems, chip design, science, or another technical discipline
• Strong ML or software engineering core
• Experience with PyTorch, ML pipelines, model debugging, or research tooling
• Experience building environments, evaluations, or data-generation systems
• Exposure to post-training, RL, long-horizon tasks, or frontier model workflows

Pedigree can be a helpful signal, but it is not the primary filter. The team cares more about slope, research horsepower, problem-solving speed, and the ability to build.

Bonus Experience

• Experience at frontier AI labs, AI infrastructure companies, or high-talent technical teams
• Experience with post-training data, RL environments, evals, model behavior, or interpretability
• Experience building data products, research tooling, or developer workflows for AI teams
• Experience in domains like chip design, physics, chemistry, biology, systems, or scientific computing
• Public artifacts, research projects, open-source work, technical writing, or demos that show exceptional ability
• Experience creating tasks or environments that require long-horizon reasoning
• Experience scaling data generation through automation rather than manual labor

Who Will Thrive Here

• A researcher-engineer who treats data as a research problem
• A high-slope polymath with a real technical spike
• Someone who can figure out unfamiliar domains quickly
• A builder who understands that the best data comes from strong research judgment
• Someone obsessed with creating realistic, economically valuable AI training and evaluation environments
• A deeply committed, results-driven technical operator
• Someone excited by frontier lab customers and the thesis that better systems can scale data generation superlinearly

Why This Opportunity

• Join an early team serving fast-growing demand from frontier AI customers
• Work on the core bottleneck behind better post-training outcomes: high-quality data
• Build RL environments and long-horizon task systems across technically deep domains
• Shape how research and data production come together as one function
• Work on systems designed to scale data generation through process, tooling, and model feedback - not simply more people
• Build at the frontier of post-training, evaluations, RL environments, and data quality
• Step into a role where research taste, engineering speed, and domain curiosity all matter

Ideal Candidate Profile

The ideal candidate is a research-minded engineer with a real spike in ML, AI research, or a technical domain.

They can read and understand code quickly, debug ML pipelines, build tools, reason about task quality, and identify what makes a data environment valuable to a frontier AI lab. They are not looking for a narrow research role or a pure software role - they want to build the systems and data that make models better.

This person is high-slope, obsessive, technically broad, and excited to help define what great post-training data looks like.