{"schemaVersion":"jobsearcher.job.v1","id":"98e975ca80bb07eeac4518a6","url":"https://jobsearcher.com/jobs/98e975ca80bb07eeac4518a6","canonicalUrl":"https://jobsearcher.com/jobs/98e975ca80bb07eeac4518a6","title":"Post-Training -- Engineer / Algorithm Researcher [33248]","description":"Job DescriptionResponsibilitiesPost-training algorithm R&D. Own the full post-training pipeline for the coding agent—supervised fine-tuning (SFT), reward modeling, and reinforcement learning (RLHF/DPO/GRPO/PPO, etc.)—continuously improving code generation, debugging, and multi-step reasoning on real software-engineering tasks.Verifiable rewards & agentic RL. Design reward mechanisms based on verifiable signals (unit tests, compile/execution results, static checks, etc.) for coding scenarios (RLVR); build a multi-turn agentic RL training paradigm with tool-call and execution-feedback loops, improving success rate and stability on long-horizon tasks.Evaluation-model training. Develop evaluation/judge models for coding tasks (LLM-as-a-Judge, generative reward models, critic/verifier models, etc.); use post-training to give them highly consistent judgment of code correctness, executability, and quality; continuously improve alignment with human annotation and verifiable signals to reduce evaluation bias and noise.Data & reward-signal engineering. Lead the construction and governance of post-training data—preference-data collection, synthetic-data generation, difficulty grading, and quality filtering; identify and mitigate reward hacking and distribution drift to keep training and evaluation signals reliable.Training–evaluation loop. Partner with the evaluation team to build an end-to-end evaluation system for coding agents (SWE-bench-style benchmarks, in-house task sets); feed results back into post-training iteration to create a fast experiment–verify–converge cadence.Training at scale. Work closely with the infra team to land RL training efficiently on large clusters; optimize the coordination of rollout sampling, inference engines (vLLM/SGLang), and the training framework to raise overall throughput and sample efficiency.QualificationsEducation. Bachelor's degree or above in CS, AI, Mathematics, Statistics, or a related field; Master's/PhD preferred.Post-training experience. Deep understanding of the LLM post-training stack; complete hands-on experience in at least one of SFT, RLHF, DPO/GRPO/PPO, or reward modeling; able to independently run the full experiment loop from data to training to evaluation.Evaluation-model experience. Understanding of reward-model / judge-model training and evaluation; familiarity with LLM-as-a-Judge, pairwise/pointwise scoring, and verifier paradigms; experience with evaluation consistency, calibration, and bias analysis a plus.RL foundations. Solid grasp of RL fundamentals (policy gradients, value functions, advantage estimation, etc.); experience with stability, sample efficiency, and hyperparameter tuning of RL training in the LLM setting.Engineering ability. Proficient in Python with a solid foundation in data structures and algorithms; skilled with PyTorch and real usage or secondary development experience with mainstream post-training/RL frameworks (TRL, veRL, OpenRLHF, DeepSpeed-Chat, etc.).Coding-domain understanding. Understanding of code generation and software-engineering task characteristics; able to build effective training and evaluation signals around verifiable rewards, sandboxed execution, and test-case design.Research & debugging. Able to read and reproduce frontier papers; strong analysis and diagnosis of training-curve anomalies, reward collapse, model degradation, and evaluation drift.","company":"Stealth Startup","rawCompany":"stealth startup","city":"Edison","state":"NJ","isRemote":false,"isActive":false,"createdAt":"2026-06-21T00:02:36.674Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Post-Training -- Engineer / Algorithm Researcher [33248]","description":"Job DescriptionResponsibilitiesPost-training algorithm R&D. Own the full post-training pipeline for the coding agent—supervised fine-tuning (SFT), reward modeling, and reinforcement learning (RLHF/DPO/GRPO/PPO, etc.)—continuously improving code generation, debugging, and multi-step reasoning on real software-engineering tasks.Verifiable rewards & agentic RL. Design reward mechanisms based on verifiable signals (unit tests, compile/execution results, static checks, etc.) for coding scenarios (RLVR); build a multi-turn agentic RL training paradigm with tool-call and execution-feedback loops, improving success rate and stability on long-horizon tasks.Evaluation-model training. Develop evaluation/judge models for coding tasks (LLM-as-a-Judge, generative reward models, critic/verifier models, etc.); use post-training to give them highly consistent judgment of code correctness, executability, and quality; continuously improve alignment with human annotation and verifiable signals to reduce evaluation bias and noise.Data & reward-signal engineering. Lead the construction and governance of post-training data—preference-data collection, synthetic-data generation, difficulty grading, and quality filtering; identify and mitigate reward hacking and distribution drift to keep training and evaluation signals reliable.Training–evaluation loop. Partner with the evaluation team to build an end-to-end evaluation system for coding agents (SWE-bench-style benchmarks, in-house task sets); feed results back into post-training iteration to create a fast experiment–verify–converge cadence.Training at scale. Work closely with the infra team to land RL training efficiently on large clusters; optimize the coordination of rollout sampling, inference engines (vLLM/SGLang), and the training framework to raise overall throughput and sample efficiency.QualificationsEducation. Bachelor's degree or above in CS, AI, Mathematics, Statistics, or a related field; Master's/PhD preferred.Post-training experience. Deep understanding of the LLM post-training stack; complete hands-on experience in at least one of SFT, RLHF, DPO/GRPO/PPO, or reward modeling; able to independently run the full experiment loop from data to training to evaluation.Evaluation-model experience. Understanding of reward-model / judge-model training and evaluation; familiarity with LLM-as-a-Judge, pairwise/pointwise scoring, and verifier paradigms; experience with evaluation consistency, calibration, and bias analysis a plus.RL foundations. Solid grasp of RL fundamentals (policy gradients, value functions, advantage estimation, etc.); experience with stability, sample efficiency, and hyperparameter tuning of RL training in the LLM setting.Engineering ability. Proficient in Python with a solid foundation in data structures and algorithms; skilled with PyTorch and real usage or secondary development experience with mainstream post-training/RL frameworks (TRL, veRL, OpenRLHF, DeepSpeed-Chat, etc.).Coding-domain understanding. Understanding of code generation and software-engineering task characteristics; able to build effective training and evaluation signals around verifiable rewards, sandboxed execution, and test-case design.Research & debugging. Able to read and reproduce frontier papers; strong analysis and diagnosis of training-curve anomalies, reward collapse, model degradation, and evaluation drift.","datePosted":"2026-06-21T00:02:36.674Z","dateModified":"2026-06-21T00:02:36.674Z","hiringOrganization":{"@type":"Organization","name":"Stealth Startup","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Edison","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"98e975ca80bb07eeac4518a6"},"url":"https://jobsearcher.com/jobs/98e975ca80bb07eeac4518a6"}}