Member of Technical Staff, Research Engineering

24-MAG

. Architect self-contained reinforcement-learning environments for complex real-world tasks .

Posted 9/15/2026full-timeRemote • New York • United StatesLead💰 $400,000 - $800,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting reinforcement-learning environments, designing reward functions, and developing scalable training pipelines. Proficient in building automated systems for data generation and evaluation, ensuring reproducibility and reliability in model training and experimentation.

Highest-signal resume keywords

Reinforcement Learning ExpertiseRL Environment DesignAutomation and Synthetic Data GenerationModel Validation and Quality AssuranceScalable Infrastructure for RL Experimentation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningReward Function DesignTraining Pipeline DevelopmentAutomated Evaluation SystemsOpen-Source Model Fine-TuningBenchmarking FrameworksExperimentation WorkflowsData AnalysisModel Behaviour AnalysisEvaluation Logic Design

Soft Skills

Technical WritingCommunication SkillsCollaborationAdaptabilityProblem-Solving

Industry Keywords

Research EvaluationModel QualityTraining DynamicsFast-Paced EnvironmentsCollaborative Environments

About the role

Key responsibilities & impact

Architect self-contained reinforcement-learning environments for complex real-world tasks
Design reward functions, verifiers, evaluation logic, and supporting environment components
Structure environments for reliable experimentation and measurable model improvement
Translate research objectives into rigorous RL workflows
Ensure environments are reproducible, testable, and suitable for iterative model development
Design and scale episode pipelines and multi-component training processes
Build reproducible experimentation workflows for reinforcement-learning research
Develop systems for running, tracking, and analysing large-scale training experiments
Improve reliability and efficiency across RL training infrastructure
Build automated synthetic data-generation systems
Develop AI-driven evaluation and quality-assurance systems for grading, validation, and feedback
Establish automated feedback loops to improve training-data and model quality
Design verification systems that distinguish strong model behaviour from superficially plausible outputs
Fine-tune and optimise open-source reinforcement-learning and machine-learning models
Develop benchmarking frameworks measuring capability, robustness, and data quality
Analyse model behaviour across internal and external evaluation environments
Contribute to the development, release, and interpretation of research evaluations and benchmark results
Operate across research experimentation and production-oriented technical implementation
Adapt research priorities, evaluation systems, and experimentation workflows as project requirements evolve

Requirements

What you’ll need

Deep professional or research experience in reinforcement learning
Strong understanding of RL environment design, reward structures, training dynamics, and evaluation
Demonstrated experience building and scaling RL systems, training pipelines, or experimentation frameworks
Strong experience with automation and synthetic data-generation workflows
Familiarity with automated evaluation, model validation, and quality-assurance systems
Experience fine-tuning and evaluating open-source machine-learning models
Strong technical writing and communication skills
Ability to operate effectively in fast-paced, research-driven, and highly collaborative environments
Experience publishing benchmarks, evaluations, or research artifacts is advantageous
Familiarity with modern evaluation ecosystems and benchmarking frameworks is beneficial
Experience with scalable infrastructure supporting large-scale RL experimentation is strongly valued
Must work without using confidential or proprietary information belonging to any employer, client, institution, or other third party

Benefits

Comp & perks

Fully remote work
Full-time engagement
Compensation of $400,000–$800,000/year
Remote consulting opportunities across technical, evaluation, and project-based workstreams