Forward Deployed Engineer, Lead - LLM Post-training

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.

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  Lead post-training engagements with enterprise customers: assess their data, define training strategies, design reward signals and verifiers, prepare datasets, run training loops, and evaluate results against customer-specific benchmarks.

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  Design and build RL training environments for model adaptation, including synthetic data generation pipelines, reward model training, and preference data collection workflows.

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  Design and build evaluation infrastructure: define what "better" means for each customer use case, build eval harnesses, curate test sets, and establish baselines that measure real-world performance.

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  Own the data pipeline from raw customer data through training-ready datasets, including synthetic data generation, data quality inspection, cleaning, and format standardization.

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  Deploy post-trained models across hybrid environments (public cloud, VPC, and on-premises), working with infrastructure teams to ensure inference performance, cost efficiency, and reliability at scale.

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  Shape and scale the post-training and evaluation practice by defining playbooks, best practices, and technical standards. Mentor engineers on the team and help define what great applied AI work looks like at Reflection.

• Hands-on post-training experience with large language models at scale. You have built and operated RL training environments, designed preference optimization workflows on models at 50B+ parameter scale, and shipped the results to production.

• Experience building synthetic data generation pipelines, reward models, and verifiers for reinforcement learning workflows. You've architected the data and feedback loops that make post-training work.

• Deep understanding of evaluation methodology: how to design evaluations that measure what matters, how to interpret training dynamics, and how to tell the difference between a model that looks good on a benchmark and one that actually works.

• Practical experience with training infrastructure at scale: comfortable working with multi-node GPU clusters, managing large training runs, debugging distributed training, and optimizing for cost.

• Strong software engineering fundamentals. You write production-quality code, not just notebooks. Experience with data pipelines, version control for datasets and models, and reproducible workflows.

• 6+ years of engineering experience, including 2+ years focused on LLM post-training in a leadership capacity (e.g., Tech Lead on a post-training team, Senior MLE owning preference optimization for a product, or Lead Applied Scientist running RL training pipelines in production).

• Experience in customer-facing technical roles, or a genuine interest in developing this skill. In either case, you are comfortable translating domain requirements into training strategies and delivering measurable outcomes.

• Self-starter with high agency and ownership, excelling in fast-paced startup environments where playbooks are still being written.