{"id":1945356,"url":"https://alion.io/job/zenteiq-ai-research-engineer","title":"AI Research Engineer","company":{"id":1338,"name":"ZenteiQ","domain":"zenteiq.ai","url":"https://alion.io/company/zenteiq-ai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Chain-of-Thought","optional":false},{"name":"Constitutional AI","optional":false},{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GRPO","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLAIF","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-10-06T05:45:51Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-13T01:31:12Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Role: Reinforcement Learning & Post-Training Engineer\nRole Overview\nAs a founding post-training specialist, you will own the end-to-end alignment and reasoning stack taking base pre-trained models through advanced alignment. You will design and execute novel post-training recipes spanning RLAIF, Constitutional AI, and dense process supervision (PRMs) to unlock frontier reasoning and domain alignment directly on raw base weights.\nKey Responsibilities\nBase Model Post-Training: Design and scale full-spectrum post-training workflows on base foundation models using algorithms like GRPO, PPO, DPO, and iterative rejection sampling.\nRLAIF & Constitutional AI: Architect automated preference-data synthesis, iterative multi-turn critique loops, and rubric-driven constitutional refinement pipelines to eliminate human annotation bottlenecks.\n Process Reward Modeling (PRMs): Construct and train step-level PRMs to evaluate granular reasoning steps, intermediate chain-of-thought derivations, and dense domain verification tasks.\nVerifiable Reward & Environment Design: Implement deterministic verifiers, execution sandboxes for code and math, and hybrid reward functions combining outcome checks with dense step feedback.\nAlignment Infrastructure: Optimize distributed RL training and rollout loops across accelerator clusters, maximizing rollout throughput, memory management, and inference-engine integration.\nCandidate Requirements\n 3+ Years Hands-On RL / Fine-Tuning Experience: Demonstrated track record executing post-training on LLM base models (not generic software engineering or high-level API wrapper development).\nAlgorithmic Depth: Deep theoretical and practical command of modern policy gradient methods, credit assignment, value function estimation, and reward-shaping dynamics.\n Dense Reward & Alignment Mastery: Concrete experience designing reward models (ORMs/PRMs), preference optimization objectives, or constitutional self-critique mechanisms.\n High-Throughput RL Stack: Strong fluency with distributed training and rollout architectures (such as Ray, vLLM, DeepSpeed, or custom JAX/PyTorch accelerator pipelines).\nStartup Velocity & Ownership: Ability to independently formulate research hypotheses, diagnose reward hacking or mode collapse, and deploy robust training runs under fast production cycles.\nWhat We Offer\nDirect Architecture Ownership: Full technical authority over the post-training, alignment, and reasoning methodology of foundation models.\n High-Density Compute: Dedicated accelerator clusters to validate novel RL formulations and train production models end-to-end.","description_format":"text","description_chars":2606,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Machine Learning","STEM Education","Simulation & Digital Twin Software","AI for Science"],"lifecycle":[{"event":"open","at":"2026-10-06T06:24:24Z"}],"visa":[],"liveness":{"score":88,"band":"hot","label":"Hiring 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