Forward Deployed Engineer, Lead - LLM Post-training

About the job

Responsibilities

• Lead post-training engagements with enterprise customers, including data assessment, training strategy definition, reward signal design, dataset preparation, training execution, and evaluation against benchmarks.
• Design and build RL training environments for model adaptation, including synthetic data generation pipelines, reward model training, and preference data collection workflows.
• Design and build evaluation infrastructure to define 'better' for customer use cases, build eval harnesses, curate test sets, and establish baselines for real-world performance.
• Own the data pipeline from raw customer data to training-ready datasets, encompassing synthetic data generation, quality inspection, cleaning, and standardization.
• Deploy post-trained models across hybrid environments (public cloud, VPC, on-premises), ensuring inference performance, cost efficiency, and scalability.
• Shape and scale the post-training and evaluation practice by defining playbooks, best practices, and technical standards, while mentoring engineers.

Requirements

• Hands-on post-training experience with large language models at scale, including building and operating RL training environments, designing preference optimization workflows on models 50B+ parameters, and shipping results to production.
• Experience building synthetic data generation pipelines, reward models, and verifiers for reinforcement learning workflows, including architecting data and feedback loops.
• Deep understanding of evaluation methodology for measuring performance, interpreting training dynamics, and distinguishing benchmark performance from real-world effectiveness.
• Practical experience with training infrastructure at scale, including multi-node GPU clusters, managing large training runs, debugging distributed training, and cost optimization.
• Strong software engineering fundamentals with experience in production-quality code, data pipelines, version control for datasets and models, and reproducible workflows.
• 6+ years of engineering experience, with at least 2 years focused on LLM post-training in a leadership capacity.
• Experience in customer-facing technical roles or a strong interest in developing this skill, with the ability to translate domain requirements into training strategies.
• Self-starter with high agency and ownership, capable of thriving in fast-paced startup environments.

Benefits

• Top-tier compensation (salary and equity)
• Stock options
• Comprehensive medical, dental, vision, and life insurance
• Annual wellness allowance
• Provided lunch and dinner in the office
• 22 weeks paid parental leave
• Unlimited paid time off in the U.S.
• 30 days paid time off in the U.K.
• Sponsorship support for visas and long-term immigration pathways
• Regular off-sites, happy hours, and team celebrations