Bright Vision Technologies is seeking a Generative AI Engineer to design, implement, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep hands-on experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to run complex training pipelines reliably. The successful candidate will combine strong ML intuition with production-grade engineering practices and will navigate trade-offs between data quality, compute budgets, evaluation rigor, and shipping velocity. You will collaborate with cross-functional partners - product, design, engineering, operations, and business stakeholders - to translate ambiguous requirements into well-engineered solutions and will contribute through code reviews, design reviews, and mentorship of junior engineers. Strong communication and a track record of shipping meaningful work that stands up in production are essential. Required qualifications include a Master’s or PhD in Computer Science, Machine Learning, or a related field (or equivalent experience), six or more years of ML research and engineering experience with significant LLM exposure, proficiency in Python and PyTorch, hands-on experience fine-tuning transformer-based language models at non-trivial scale, familiarity with distributed training strategies (FSDP, ZeRO, pipeline parallelism), experience with RLHF, DPO, or other preference optimization techniques, strong evaluation methodology, experience opera

Bright Vision Technologies is seeking a Generative AI Engineer to design, implement, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep hands-on experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to run complex training pipelines reliably. The successful candidate will combine strong ML intuition with production-grade engineering practices and will navigate trade-offs between data quality, compute budgets, evaluation rigor, and shipping velocity. You will collaborate with cross-functional partners - product, design, engineering, operations, and business stakeholders - to translate ambiguous requirements into well-engineered solutions and will contribute through code reviews, design reviews, and mentorship of junior engineers. Strong communication and a track record of shipping meaningful work that stands up in production are essential. Required qualifications include a Master’s or PhD in Computer Science, Machine Learning, or a related field (or equivalent experience), six or more years of ML research and engineering experience with significant LLM exposure, proficiency in Python and PyTorch, hands-on experience fine-tuning transformer-based language models at non-trivial scale, familiarity with distributed training strategies (FSDP, ZeRO, pipeline parallelism), experience with RLHF, DPO, or other preference optimization techniques, strong evaluation methodology, experience opera