Forward Deployed Engineer, Lead - LLM Post-training

About the role

The Forward Deployed Engineer Lead, Post-Training position at Our Mission Reflection is a critical role within the Applied AI team. This team focuses on adapting and deploying open-weight models tailored to specific customer domains, tasks, and constraints. The ideal candidate will lead the technical strategy for model customization, from data preparation to production deployment.

Responsibilities

• 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.
• 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: define what "better" means for each customer use case, build eval harnesses, curate test sets, and establish baselines that measure real-world performance.
• Own the data pipeline from raw customer data through training-ready datasets, including synthetic data generation, data quality inspection, cleaning, and format standardization.
• 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.
• 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.

Requirements

• Hands-on post-training experience with large language models at scale.
• Experience building synthetic data generation pipelines, reward models, and verifiers for reinforcement learning workflows.
• 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. 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.
• Experience in customer-facing technical roles, or a genuine interest in developing this skill.

Qualifications

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

Skills

• Hands-on post-training experience with large language models at scale.
• Experience building synthetic data generation pipelines, reward models, and verifiers for reinforcement learning workflows.
• 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. 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.
• Experience in customer-facing technical roles, or a genuine interest in developing this skill.

Benefits

• Comprehensive medical, dental, vision, and life insurance.
• An annual wellness allowance.
• Lunch and dinner provided in the office daily.
• 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
• Unlimited paid time off in the U.S. and 30 days in the U.K.
• Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
• Regular off-sites, happy hours, and team celebrations.

Pay

Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.

Schedule

Flexible work schedule to accommodate remote work and personal commitments.