Foundation Model Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: Foundation Model Engineer Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $100,000–$150,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary: We are looking for an Foundation Model 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. Key Responsibilities Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniquesLead dataset construction, curation, and quality assurance processes for instruction tuning and preference dataBuild scalable training pipelines on top of modern distributed training frameworksTune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuningImplement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methodsDesign rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probesImplement safety, refusal, and policy evaluations to track model behavior across releasesOperate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliablyOptimize training throughput using mixed precision, sequence packing, and efficient attention implementationsManage model artifacts, lineage tracking, and reproducibility across many concurrent experimentsCollaborate with product, research, and platform teams to align fine-tuning roadmaps with business needsDocument training methodology, results, and decisions clearly for technical and non-technical audiencesMentor engineers on fine-tuning best practices, evaluation rigor, and responsible deploymentStay current with LLM research and translate advances into production-ready fine-tuning recipes Required Qualifications Master's or PhD in Computer Science, Machine Learning, or a related field; or equivalent experienceSix or more years of combined ML research and engineering experience, with significant LLM exposureStrong proficiency in Python and modern deep learning frameworks, especially PyTorchHands-on experience fine-tuning transformer-based language models at non-trivial scaleFamiliarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelismExperience with RLHF, DPO, or other preference optimization techniquesStrong understanding of evaluation methodology, benchmarks, and human evaluation designExperience operating training jobs on GPU clusters and recovering from failuresStrong written and verbal communication skillsTrack record of shipping or publishing impactful LLM work Preferred Qualifications Publications at top-tier ML venuesExperience with multimodal model fine-tuningFamiliarity with synthetic data generation and dataset distillationOpen-source contributions to LLM training librariesExposure to responsible AI evaluation and red-teaming practices How To Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to Harry@bvteck.com or contact us at (908)676-4399. Learn more about Bright Vision Technologies at www.bvteck.com. Bright Vision Technologies is an Equal Opportunity Employer Equal Employment Opportunity (EEO) Statement Bright 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, disabili