Forward Deployed Engineer - LLM Post-training

OUR MISSION

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.

What You'll Do

- 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.

- Prepare training data from raw customer inputs: inspect data quality, clean and format datasets, identify adversarial or noisy samples, and build reproducible data pipelines.

- Debug and diagnose training and inference issues: interpret loss curves, catch data quality problems, and identify when training dynamics indicate something is wrong.

- Support end-to-end deployments of fine-tuned models across hybrid environments (public cloud, VPC, and on-premises), helping ensure inference performance and reliability in production.

- Contribute to evolving playbooks, evaluation benchmarks, and best practices as part of a growing fine-tuning and evals practice.

What We're Looking For

- Applied ML experience with hands-on fine...