Forward Deployed Engineer - LLM Post-training

Reflection is seeking a Forward Deployed Engineer to drive LLM fine-tuning and evaluations for enterprise customers. You'll work hands-on with customer data, run fine-tuning workflows, build evaluation harnesses, and deploy models to production. You'll collaborate directly with customers and research teams.

• 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 improvement.
• 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 training dynamics issues.
• Support end-to-end deployments across hybrid environments (public cloud, VPC, on-premises), ensuring inference performance and reliability.
• Contribute to evolving playbooks, evaluation benchmarks, and best practices.

• Applied ML experience with hands-on fine-tuning of language models; familiarity with SFT, DPO, RLHF, or similar techniques.