Member of Technical Staff, Research Engineering

Job not on LinkedIn

🔥 18 hours ago

🗽 New York – Remote

🗽 New York – Remote

💵 $400k - $800k / year

⏰ Full Time

🔴 Lead

📚 Research Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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24-MAG

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤝 B2B

💼 Consulting

B2B • Consulting

24-MAG is a commercial strategy and execution firm that helps B2B organizations design and implement systems, workflows, and operating rhythms for sales, client management, and cross-functional projects. They focus on transforming scattered processes into aligned, measurable, and scalable commercial functions—covering pipeline structure, account management frameworks, and operational discipline for teams seeking efficient, intentional growth.

📋 Description

• 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