Member of Technical Staff – Applied Research

Embedding VC

. Design and run model improvement workflows using supervised fine-tuning, preference optimization, and reinforcement learning approaches such as GRPO .

Posted 10/17/2026full-timeNew York City • New York • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing model improvement workflows using reinforcement learning and supervised fine-tuning techniques. Proficient in developing high-fidelity training trajectories and building scalable ML infrastructure with a strong foundation in machine learning fundamentals.

Highest-signal resume keywords

Machine Learning FundamentalsLLM Fine-TuningPython ProgrammingPyTorch DevelopmentReinforcement Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Supervised Fine-TuningPreference OptimizationReinforcement LearningLinear AlgebraOptimizationStochastic Gradient DescentReward ModelingTraining TrajectoriesDistributed GPU ClustersExperimentation

Soft Skills

Engineering JudgmentProblem-Solving

Tools & Technologies

PyTorchFastAPIML InfrastructureGPU ClustersEvaluation Harnesses

Industry Keywords

Model Improvement WorkflowsEnvironment DesignReward FunctionsData GenerationTraining-Run Debugging

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Design and run model improvement workflows using supervised fine-tuning, preference optimization, and reinforcement learning approaches such as GRPO
Design agent environments, tasks, tools, validators, reward signals, and evaluation harnesses
Create, inspect, and improve high-fidelity training trajectories
Develop reward functions, rubrics, validators, and analysis tools
Run experiments across distributed GPU clusters using PyTorch and FSDP
Build infrastructure supporting model training, evaluation, and data generation at scale
Translate frontier AI labs’ ambiguous research goals into concrete environments, datasets, experiments, and deliverables
Own end-to-end problems spanning environment design, training-run debugging, reward-function improvement, and evaluation-pipeline scaling

Requirements

What you’ll need

Masters / PhD-level knowledge of machine learning fundamentals, including linear algebra, optimization, and stochastic gradient descent
Hands-on experience with or deep understanding of LLM fine-tuning, preference data, reward modeling, or reinforcement learning for language models
Strong Python skills
Comfortable building with PyTorch, FastAPI, and modern ML infrastructure
Ability to work with lower-level primitives when needed
Strong engineering judgment across research ambiguity and production constraints
Approximately 3–5 years of experience envisioned, though there is no hard rule and more or less experience may be suitable