Member of Technical Staff, Research Engineering
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Job Description
Research Engineer building reinforcement-learning environments, training pipelines, synthetic data systems, and evaluations. Supporting 24-MAG’s remote technical and AI research opportunities.
Responsibilities:
- 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:
- 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:
- Fully remote work
- Full-time engagement
- Compensation of $400,000–$800,000/year
- Remote consulting opportunities across technical, evaluation, and project-based workstreams
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