{"schemaVersion":"jobsearcher.job.v1","id":"5bbf3f0aa4174ccf098e24fd","url":"https://jobsearcher.com/jobs/5bbf3f0aa4174ccf098e24fd","canonicalUrl":"https://jobsearcher.com/jobs/5bbf3f0aa4174ccf098e24fd","title":"LLM Engineer","description":"Job Title\nLLM Engineer\n\nLocation\n100% Remote (Continental United States)\n\nPosition Type\nIn‑house Bright Vision Technologies SOW engagement (no third‑party client or vendor)\n\nExperience\n6+ years\n\nSalary\n100K – 150K per year\n\nJob Summary\nWe are looking for an LLM Engineer to design, execute, and operationalize fine‑tuning workflows for large language models across supervised, preference‑based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production‑grade engineering practices, and is comfortable navigating the trade‑offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross‑functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well‑engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.\n\nKey Responsibilities\n\nDesign and execute fine‑tuning experiments for large language models using supervised, DPO, RLHF, and related techniques.\n\nLead dataset construction, curation, and quality assurance processes for instruction tuning and preference data.\n\nBuild scalable training pipelines on top of modern distributed training frameworks.\n\nTune hyperparameters, optimizer configurations, and training stability strategies for large‑model fine‑tuning.\n\nImplement parameter‑efficient fine‑tuning techniques such as LoRA, QLoRA, and adapter‑based methods.\n\nDesign rigorous evaluation suites including automated benchmarks, human evaluation, and capability‑specific probes.\n\nImplement safety, refusal, and policy evaluations to track model behavior across releases.\n\nOperate large‑scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably.\n\nOptimize training throughput using mixed precision, sequence packing, and efficient attention implementations.\n\nManage model artifacts, lineage tracking, and reproducibility across many concurrent experiments.\n\nCollaborate with product, research, and platform teams to align fine‑tuning roadmaps with business needs.\n\nDocument training methodology, results, and decisions clearly for technical and non‑technical audiences.\n\nMentor engineers on fine‑tuning best practices, evaluation rigor, and responsible deployment.\n\nStay current with LLM research and translate advances into production‑ready fine‑tuning recipes.\n\nRequired Qualifications\n\nMaster’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience.\n\nSix or more years of combined ML research and engineering experience, with significant LLM exposure.\n\nStrong proficiency in Python and modern deep learning frameworks, especially PyTorch.\n\nHands‑on experience fine‑tuning transformer‑based language models at non‑trivial scale.\n\nFamiliarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism.\n\nExperience with RLHF, DPO, or other preference optimization techniques.\n\nStrong understanding of evaluation methodology, benchmarks, and human evaluation design.\n\nExperience operating training jobs on GPU clusters and recovering from failures.\n\nStrong written and verbal communication skills.\n\nTrack record of shipping or publishing impactful LLM work.\n\nPreferred Qualifications\n\nPublications at top‑tier ML venues.\n\nExperience with multimodal model fine‑tuning.\n\nFamiliarity with synthetic data generation and dataset distillation.\n\nOpen‑source contributions to LLM training libraries.\n\nExposure to responsible AI evaluation and red‑teaming practices.\n\nEqual Employment Opportunity (EEO) Statement\nBright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.\n\nBV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.\n\n#J-18808-Ljbffr","company":"Bright Vision Technologies","rawCompany":"bright vision technologies","city":"Bartlett","state":"IL","isRemote":false,"isActive":true,"createdAt":"2026-06-30T03:25:46.947Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"LLM Engineer","description":"Job Title\nLLM Engineer\n\nLocation\n100% Remote (Continental United States)\n\nPosition Type\nIn‑house Bright Vision Technologies SOW engagement (no third‑party client or vendor)\n\nExperience\n6+ years\n\nSalary\n100K – 150K per year\n\nJob Summary\nWe are looking for an LLM Engineer to design, execute, and operationalize fine‑tuning workflows for large language models across supervised, preference‑based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production‑grade engineering practices, and is comfortable navigating the trade‑offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross‑functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well‑engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.\n\nKey Responsibilities\n\nDesign and execute fine‑tuning experiments for large language models using supervised, DPO, RLHF, and related techniques.\n\nLead dataset construction, curation, and quality assurance processes for instruction tuning and preference data.\n\nBuild scalable training pipelines on top of modern distributed training frameworks.\n\nTune hyperparameters, optimizer configurations, and training stability strategies for large‑model fine‑tuning.\n\nImplement parameter‑efficient fine‑tuning techniques such as LoRA, QLoRA, and adapter‑based methods.\n\nDesign rigorous evaluation suites including automated benchmarks, human evaluation, and capability‑specific probes.\n\nImplement safety, refusal, and policy evaluations to track model behavior across releases.\n\nOperate large‑scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably.\n\nOptimize training throughput using mixed precision, sequence packing, and efficient attention implementations.\n\nManage model artifacts, lineage tracking, and reproducibility across many concurrent experiments.\n\nCollaborate with product, research, and platform teams to align fine‑tuning roadmaps with business needs.\n\nDocument training methodology, results, and decisions clearly for technical and non‑technical audiences.\n\nMentor engineers on fine‑tuning best practices, evaluation rigor, and responsible deployment.\n\nStay current with LLM research and translate advances into production‑ready fine‑tuning recipes.\n\nRequired Qualifications\n\nMaster’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience.\n\nSix or more years of combined ML research and engineering experience, with significant LLM exposure.\n\nStrong proficiency in Python and modern deep learning frameworks, especially PyTorch.\n\nHands‑on experience fine‑tuning transformer‑based language models at non‑trivial scale.\n\nFamiliarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism.\n\nExperience with RLHF, DPO, or other preference optimization techniques.\n\nStrong understanding of evaluation methodology, benchmarks, and human evaluation design.\n\nExperience operating training jobs on GPU clusters and recovering from failures.\n\nStrong written and verbal communication skills.\n\nTrack record of shipping or publishing impactful LLM work.\n\nPreferred Qualifications\n\nPublications at top‑tier ML venues.\n\nExperience with multimodal model fine‑tuning.\n\nFamiliarity with synthetic data generation and dataset distillation.\n\nOpen‑source contributions to LLM training libraries.\n\nExposure to responsible AI evaluation and red‑teaming practices.\n\nEqual Employment Opportunity (EEO) Statement\nBright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.\n\nBV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.\n\n#J-18808-Ljbffr","datePosted":"2026-06-30T03:25:46.947Z","dateModified":"2026-06-30T03:25:46.947Z","hiringOrganization":{"@type":"Organization","name":"Bright Vision Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bartlett","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"5bbf3f0aa4174ccf098e24fd"},"url":"https://jobsearcher.com/jobs/5bbf3f0aa4174ccf098e24fd"}}