Technical Program Manager, Model Alignment and Deployment

About the Role

Model Alignment and Deployment is a critical, cross-functional effort spanning our Post-Training, Safety Engineering, Trust & Safety, ML Infra/Model Serving, and User Experience Research (UXR) teams. Together, these groups are responsible for transforming powerful pretrained language models into intelligent, engaging, safely aligned, and highly scalable products-working across data, compute, algorithms, infrastructure, and user insights to improve model performance and ensure reliable delivery.

You'll partner closely with research engineers, safety experts, data scientists, infrastructure engineers, and UX researchers to turn ambitious model development goals into well-scoped, well-tracked, cross-functional programs. You will ensure that the data pipelines, evaluation frameworks, alignment workflows, and serving infrastructure underpinning our models are moving fast and moving well.

You'll operate effectively in high-ambiguity environments, anticipate risks before they become blockers, and bring the kind of technical fluency needed to earn trust with researchers while communicating clearly to leadership.

What You’ll Do

• Program ownership: Lead planning and execution of cross-functional programs spanning data collection, annotation pipelines, alignment workflows (RLHF, DPO, Constitutional AI), safety guardrails (adversarial testing, red-teaming), and model serving. Establish scopes, goals, timelines, risks, and success metrics.
• Cross-functional coordination: Serve as the connective tissue between Post-Training, Safety Engineering, Trust & Safety, ML Infra, UXR, and Product. Translate model development, safety, and user experience priorities into executable roadmaps, keeping tightly coupled workstreams aligned from post-training through to production deployment.
• Evaluation & quality: Develop and maintain custom evaluation frameworks to track model performance and user satisfaction. Drive comprehensive quality evaluation initiatives alongside rigorous safety and toxicity baselines. Partner with UXR, researchers, and engineers to identify quality signals, incorporate human feedback, and surface actionable insights on model behavior in production.
• Operational excellence: Drive visibility into data pipeline health, annotation quality, training run progress, and deployment readiness. Identify bottlenecks across teams and lead efforts to improve tooling, process, and developer velocity.
• Strategic partnership: Partner with research, safety, product, and UXR leadership on prioritization, sequencing, and tradeoffs-balancing aggressive capability scaling with strict safety requirements, user needs, and infrastructure constraints.
• Process development: Build and refine the operational patterns, ontologies, and frameworks used to scale new capability development-from prompt engineering and data generation to model behavior specification and safety guidelines.
• Vendor & partner management: Own external partner relationships supporting these workstreams, including general and safety-focused annotation vendors, evaluation tooling providers, and data partners.

What You’ll Bring

• 5+ years of experience in technical program management, research operations, or product execution in a fast-moving AI, ML, or research environment.
• Deep familiarity with post-training and alignment concepts (supervised fine-tuning, RLHF, AI safety frameworks, LLM evaluation) as well as model deployment/serving, sufficient to engage substantively with both research and infrastructure engineers.
• Proven ability to lead complex, multi-team programs in ambiguous, rapidly evolving environments; track record of shipping with quality and speed.
• Strong analytical mindset; comfortable working with data and user insights to measure program health, identify trends, and drive decisions.
• Proficiency in SQL and Python.
• Exceptional communication skills - able to translate deep technical work into clear narratives for leadership, and to hold detailed technical conversations with engineers across different disciplines.
• Obsessive about data integrity, operational rigor, and process quality without letting process slow teams down.
• BS in a quantitative, scientific, or technical field; MS or PhD a plus.

Nice to Have

• Hands-on experience with data pipelines, annotation platforms, ML evaluation tooling, or human-in-the-loop workflows.
• Experience managing annotation vendors or external data partners.
• Familiarity with distributed training, experiment tracking, or ML infrastructure (Kubernetes, Docker, cloud) and model serving systems.
• Prior experience embedded in an AI research team, foundation model lab, or Trust & Safety engineering team.
• Direct experience managing AI safety, trust, quality eval, or red-teaming programs.