The posting
We are sharing a specialised full-time opportunity for experienced Research Engineers with deep expertise in reinforcement learning, ML-oriented data systems, evaluation infrastructure, and scalable experimentation to contribute to advanced AI research and development.
Selected professionals will operate at the intersection of research and production, building reinforcement-learning environments, training pipelines, synthetic data systems, automated evaluation frameworks, and scalable experimentation workflows. The role focuses on translating experimental ideas into robust technical systems that improve model capability, reliability, and research velocity.
Key Responsibilities
Reinforcement Learning Environment Design
- Architect self-contained reinforcement-learning environments that capture complex real-world tasks
- Design reward functions, verifiers, evaluation logic, and supporting environment components
- Structure environments to support reliable experimentation and measurable model improvement
- Translate research objectives into technically rigorous RL workflows
- Ensure environments remain reproducible, testable, and suitable for iterative model development
Training Pipelines & Experimentation Systems
- Design and scale episode pipelines and multi-component training processes
- Build reproducible experimentation workflows supporting reinforcement-learning research
- Develop systems for running, tracking, and analysing large-scale training experiments
- Improve reliability and efficiency across RL training infrastructure
- Support rapid iteration between environment design, training, evaluation, and model refinement
Synthetic Data & Automated Evaluation
- Build automated data-generation systems using synthetic data to accelerate training cycles
- Develop AI-driven evaluation and quality-assurance systems for grading, validation, and feedback
- Establish automated feedback loops that improve training-data and model quality
- Design verification systems that distinguish strong model behaviour from superficially plausible outputs
- Apply rigorous quality standards throughout data-generation and evaluation pipelines
Model Optimisation & Benchmarking
- Fine-tune and optimise open-source reinforcement-learning and machine-learning models
- Apply internally generated datasets and custom training strategies to improve model performance
- 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
Ideal Profile
- 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
Engagement Details
- Full-time engagement
- Fully remote
- Compensation: $400,000–$800,000/year
- Work will involve reinforcement-learning environment design, training pipelines, synthetic data generation, automated evaluation, model optimisation, and benchmarking
- Responsibilities will span both research experimentation and production-oriented technical implementation
- Research priorities, evaluation systems, and experimentation workflows may evolve as project requirements develop
- Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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