Applied Scientist

Location: In-person - 3 days in-office in NYC or Seattle

Employment Type: Full-time

Profile: Research-first applied researcher with deep hands-on RL experience

Our client works directly with frontier AI labs on some of their hardest post-training problems.

As demand from advanced research customers continues to grow, the team is hiring applied researchers who can help design better environments, datasets, reward signals, evaluation harnesses, and post-training loops for frontier models.

This is a research role first. The team cares most about depth of understanding in reinforcement learning, post-training, and training data quality. Strong engineering ability is useful, but the core bar is research judgment, taste, and the ability to improve model behavior through better data and training signals.

Our client is hiring an Applied Researcher, Post-Training to work directly with customer research labs and help turn ambiguous research goals into concrete experiments and deliverables.

This person will design and run post-training loops, inspect high-fidelity trajectories, build reward functions and eval harnesses, and determine which signals actually improve model capability.

The ideal candidate has done real, hands-on reinforcement learning work — at a frontier lab, AI research lab, data lab, or through a serious independent project — and has results to show for it.

• Work directly with customer research labs to understand their hardest post-training problems
• Turn unclear research goals into concrete environments, datasets, experiments, and deliverables
• Design and run post-training loops, including reinforcement learning, preference optimization, and supervised fine-tuning
• Work with methods such as GRPO, RLHF-style training, preference data, reward modeling, and related post-training approaches
• Figure out which signals actually improve model performance and which ones only look good on paper
• Shape, inspect, and improve high-fidelity trajectories and training data
• Review data closely for correctness, edge cases, realism, and overall quality
• Build reward functions, rubrics, validators, and evaluation harnesses
• Design evals that hold up against reward hacking and show real capability gains
• Read post-training signals and experiment results across distributed GPU clusters
• Decide what experiment, dataset, reward signal, or training adjustment to try next

• Deep hands-on experience with reinforcement learning or post-training
• Strong understanding of training data quality and how data impacts model behavior
• Experience running or contributing to real post-training experiments
• Experience with reinforcement learning, preference optimization, SFT, reward functions, evals, or model behavior research
• Ability to reason from messy experiment results and identify what to try next
• Strong taste around trajectories, tasks, environments, and training data quality
• Ability to identify edge cases, reward hacking, bad incentives, and misleading evaluation results
• Comfort working directly with research customers on ambiguous technical problems
• Evidence of real results from frontier lab work, AI lab work, data lab work, or serious independent research projects
• Strong communication skills and ability to translate research goals into concrete execution

• Experience with GRPO or related reinforcement learning methods
• Experience building reward functions, validators, eval harnesses, or rubrics for model training
• Experience working with distributed GPU training environments
• Experience with RLHF, RLAIF, preference optimization, synthetic data generation, or long-horizon task environments
• Experience at a frontier AI lab, emerging AI lab, post-training data company, or applied…