LLM Engineer
$100,000–$150,000 year
RemoteUnited States
Job Summary
Design and execute fine-tuning experiments for large language models using supervised, DPO, and RLHF techniques. Lead dataset construction, curation, and quality assurance processes while building scalable training pipelines on modern distributed frameworks. Tune hyperparameters, implement parameter-efficient methods like LoRA, and design rigorous evaluation suites including automated benchmarks and safety probes. Operate large-scale training jobs on GPU clusters, diagnosing failures and optimizing throughput with mixed precision and efficient attention. Manage model artifacts, lineage tracking, and reproducibility across concurrent experiments. Collaborate with product and research teams to align roadmaps with business needs, document methodologies for technical and non-technical audiences, and mentor junior engineers on best practices. Stay current with LLM research to translate advances into production-ready recipes.
Required 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.
Desired 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
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