# Forward Deployed Engineer - LLM Post-training at Reflection AI

- Company: Reflection AI
- What the company does: Make intelligence open and accessible to all. Backed by Battery, Lightspeed and Sequoia.
- Company website: https://reflection.ai/
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco, CA
- Work setup: On-site
- Posted: 2026-04-22
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/reflectionai/9e0c3947-a4ca-4c09-b0e8-dfaf634f33d7
- Page: https://www.1752.vc/careers/jobs/reflection-ai-forward-deployed-engineer-llm-post-training/

## About the role

Fine-tune Reflection's open-weight models for customer-specific use cases: prepare datasets, configure training runs (SFT, preference optimization, reinforcement fine-tuning), and iterate based on evals. Build and maintain evaluation infrastructure: design eval suites, curate test sets, establish baselines, and measure whether fine-tuned models actually improve on the tasks customers care about.

## What they're looking for

- Applied ML experience with hands-on fine-tuning of language models. You have prepared datasets, run training loops, evaluated results, and shipped a fine-tuned model. Familiarity with SFT, DPO, RLHF, or similar techniques
- Understanding of evaluation methodology: how to design evals, interpret training graphs, and tell whether a model is actually better or just overfitting to the benchmark
- Comfort with training infrastructure: GPUs, compute management, debugging common training failures. You don't need to be an infra engineer, but you should not be afraid of a stack trace from a training loop
- Strong software engineering fundamentals (Python). You write clean, reproducible code. Experience with data pipelines and version control for datasets and experiments
- 3+ years of engineering experience with meaningful exposure to applied ML or ML engineering (e.g., MLE, Applied Scientist, Data Scientist who shipped models to production, or ML-focused SWE)
- Demonstrated ability and interest to work in customer-facing environments, understanding user needs and translating domain requirements into training strategies

Tags: Commercial