Forward Deployed Engineer LLM Post-training
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Summary
Reflection is hiring a Forward Deployed Engineer for LLM post-training who fine-tunes open-weight language models for customer use cases — preparing and pipelining datasets, configuring and debugging training runs, building evaluation infrastructure, and deploying models across hybrid environments. Core stack: Python, GPU compute, and techniques like SFT, DPO, and RLHF.
You will prepare customer datasets, run language-model fine-tuning workflows, build evaluation infrastructure, and diagnose training issues. You will deploy adapted models across hybrid environments and translate customer requirements into training strategies.
Responsibilities
- Fine-tune open-weight models for customer-specific use cases
- Prepare datasets and configure training runs
- Build and maintain evaluation infrastructure
- Prepare, clean, format, and pipeline customer training data
- Debug training and inference issues
- Support deployments of fine-tuned models across hybrid environments
- Contribute to fine-tuning and evaluation playbooks and benchmarks
Requirements
- Applied ML experience fine-tuning language models
- Familiarity with SFT, DPO, RLHF, or similar techniques
- Understanding of evaluation methodology and training graphs
- Experience with GPUs, compute management, and training debugging
- Software engineering fundamentals in Python
- Experience with data pipelines and version control for datasets and experiments
- 3+ years of engineering experience with applied ML or ML engineering
- Customer-facing experience translating domain requirements into training strategies
Benefits
- Stock options
- Comprehensive medical, dental, vision, and life insurance
- Annual wellness allowance
- Daily office lunch and dinner
- 22 weeks of paid parental leave
- Unlimited paid time off in the U.S.
- 30 days of vacation in the U.K.
- Visa sponsorship support
- Regular off-sites, happy hours, and team celebrations
