Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. They are seeking a Generative AI Engineer to design, execute, and operationalize workflows for fine-tuning large language models while collaborating with cross-functional teams to develop well-engineered solutions.
Responsibilities
- Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques
- Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data
- Build scalable training pipelines on top of modern distributed training frameworks
- Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning
- Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods
- Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes
- Implement safety, refusal, and policy evaluations to track model behavior across releases
- Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably
- Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations
- Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments
- Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs
- Document training methodology, results, and decisions clearly for technical and non-technical audiences
- Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment
- Stay current with LLM research and translate advances into production-ready fine-tuning recipes
Skills
- 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
- 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
Benefits
- Full-time, Direct W2
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