We are looking for a Generative AI 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
Engineering discipline to operate complex training pipelines reliably
The ideal candidate combines:
Strong ML intuition with production-grade engineering practices
Comfort navigating 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
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
A track record of shipping meaningful work that holds up well in production
Qualifications
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
Preferred Qualifications
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
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to
[email protected]
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, 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:
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