{"id":2038673,"url":"https://alion.io/job/mecka-research-engineer-rl-env","title":"Research Engineer, RL Env","company":{"id":678757,"name":"Mecka","domain":"mecka.ai","url":"https://alion.io/company/mecka","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":88,"open_postings":12,"ghost_share":0.083,"stale_share":0.083,"repost_share":0,"time_to_fill_p50_days":109,"computed_at":"2026-10-09T06:01:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":170000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embodied AI","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-10-07T15:29:54Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-10T01:44:26Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About Mecka AI\nMecka AI is building the data infrastructure layer for robotics and embodied AI.\nWe design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.\nWe work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.\nBuild reinforcement learning environments that help researchers train models and understand their capabilities. Across Mecka's Labs team, you'll translate real tasks into computational problems, implement environments for model training and test whether measured progress reflects useful behavior.\nThis is a hands-on research engineering role for someone who knows how to construct reinforcement learning environments. You'll write working software, investigate failures and develop methods with product colleagues, domain experts and engineers.\nWhat you will be doing:\nBuild environments: Define tasks, observations, actions and state transitions. Implement reset behavior, termination conditions and measurable outcomes in environments agents can interact with.\n\nDevelop rewards and scoring: Translate task objectives into feedback and evaluation criteria. Test whether agents can exploit scoring rules without completing the intended task.\n\nRun learning experiments: Implement baseline agents, train and compare policies, and design controlled experiments that isolate the effects of data, methods and environment changes.\n\nMake evaluation reliable: Separate training and held-out tasks, check for leakage, version experiments and repeat runs. Report uncertainty and performance across conditions alongside aggregate scores.\n\nInvestigate failures: Inspect trajectories and learning behavior to distinguish policy limitations from data, reward or environment problems. Use findings to prioritize the next experiment.\n\nBuild with the team: Work with domain experts to validate task assumptions and with engineers to turn research prototypes into reusable environments, evaluation tools and documented methods.\n\nWhat you bring:\nRL environment expertise: You have hands-on experience formulating problems, constructing environments, training agents and critically assessing results.\n\nStrong programming and software debugging skills; able to build and test research systems that other people can run and extend.\n\nSound experimental design and statistical reasoning, including controlled comparisons, evaluation splits, variability and the limits of benchmark results.\n\nAbility to reason about environment dynamics, reward design and agent behavior, and trace unexpected results to concrete causes.\n\nIndependent research judgment and clear communication; learn unfamiliar domains, work with specialists and explain assumptions, tradeoffs and findings.\n\nEven better if you have:\nExperience building interactive environments, simulators or benchmarks used by other researchers.\n\nWork on agent evaluation, reward design, imitation learning or learning from real-world data.\n\nResearch artifacts with reproducible experiments, useful baselines and evidence of investigating failures beyond headline scores.\n\nA Note on Applying\nStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply - we're looking for capability and trajectory, not a perfect checklist match.\nInclusive Hiring at Mecka\nWe are committed to creating an inclusive and supportive candidate experience. Should you require any accommodation whatsoever during the interview process, please inform us without any hesitation. Mecka is dedicated to ensuring equal treatment and opportunity in all phases of recruitment, selection, and employment, in compliance with employment law. We do not discriminate based on gender, race, religion, national origin, ethnicity, disability, gender identity/expression, sexual orientation, veteran or military status, or any other protected category. Mecka is proud to be an equal opportunity employer, fostering a culture of inclusivity and maintaining a work environment that is free from discrimination, harassment, and retaliation.\nUse of Artificial Intelligence in Recruitment\nMecka uses artificial intelligence (AI) responsibly to support administrative and efficiency-focused aspects of our recruitment process. This includes activities such as drafting job descriptions, generating interview questions, note-taking and recordings, and supporting sourcing and scheduling workflows. All candidate evaluations, interviews, and hiring decisions are made by members of the Mecka team. While AI tools may assist with screening and assessment, they do not replace human judgment in selection decisions. Our use of AI is intended to streamline routine tasks, improve consistency, and enhance the overall candidate experience. We are committed to upholding principles of fairness, transparency, and accountability in all hiring activities. 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