Make intelligence open and accessible to all. Backed by Battery, Lightspeed and Sequoia.
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
More about this role
Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
Role Overview
We're looking for a core member of Reflection's Applied AI team to drive model fine-tuning and evaluations for enterprise customers. This team takes Reflection's open-weight models and adapts them for specific customer domains, tasks, and constraints. As a ML Engineer, you will work hands-on with customer data, run fine-tuning workflows, build evaluation harnesses, and deploy adapted models to production. You'll work directly with customers to understand what they need and with research teams to push what's possible.
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...
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