Generative AI Engineer

Bright Vision Technologies • Artesia, CA

1d • $90,000 - $115,000 • Remote

About The Position

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The Generative AI Engineer will design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. The role calls for practical expertise with modern training stacks, dataset construction, evaluation methods, and reliable operation of complex training pipelines. The engineer will balance data quality, compute budgets, evaluation rigor, and delivery speed while applying production-grade engineering practices.

The engineer will work with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. The role also includes contributing to code and design reviews and mentoring more junior engineers. Candidates should bring strong ML intuition, clear communication, and a record of delivering impactful work that performs well in production.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
• Six or more years of combined ML research and engineering experience, with significant LLM exposure.
• Strong proficiency in Python and modern deep learning frameworks, especially PyTorch.
• Hands-on experience fine-tuning transformer-based language models at non-trivial scale.
• Familiarity with distributed training strategies, including FSDP, ZeRO, and pipeline parallelism.
• Experience with RLHF, DPO, or other preference optimization techniques.
• Strong understanding of evaluation methodology, benchmarks, and human evaluation design.
• Experience operating training jobs on GPU clusters and recovering from failures.
• Strong written and verbal communication skills.
• Track record of shipping or publishing impactful LLM work.

Nice To Haves

• Publications at top-tier ML venues.
• Experience with multimodal model fine-tuning.
• Familiarity with synthetic data generation and dataset distillation.
• Open-source contributions to LLM training libraries.
• Exposure to responsible AI evaluation and red-teaming practices.

Responsibilities

• Design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.
• Construct datasets and apply rigorous evaluation methodologies for LLM training and fine-tuning.
• Operate complex training pipelines reliably, including training jobs on GPU clusters and recovery from failures.
• Translate ambiguous requirements into engineered solutions in collaboration with product, design, engineering, operations, and business stakeholders.
• Contribute to code reviews and design reviews.
• Mentor more junior engineers.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior