jazzhr
Large Language Model Specialist
Bright Vision Technologies
Remote, USA
Full Time, Contract
Senior Level
Remote
USD 100000-150000 yearly
Discovered 1 weeks ago
Free
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- 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.
- 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.
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