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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.