FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
LLM Fine-Tuning Engineer
Bright Vision TechnologiesLLM Fine-Tuning Engineer designing and operationalizing fine-tuning workflows for large language models. Join a forward-thinking software development company to help transform business processes.
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Comprehensive benefits
- Competitive compensation packages
- Supportive work-life balance
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
fine-tuninglarge language modelssupervised learningDPORLHFhyperparameter tuningLoRAQLoRAPyTorchdistributed training
Soft Skills
communicationmentoringcollaborationdocumentation
Certifications
Master’s in Computer SciencePhD in Machine Learning