Technical Program Manager, Model Alignment and Deployment

The role

You will lead planning and execution of cross-functional programs spanning data collection, alignment workflows, safety guardrails, and model serving. You will connect teams including Post-Training, Safety Engineering, ML Infra, and UXR to turn model development goals into executable roadmaps.

What you will do

• Lead planning and execution of cross-functional programs spanning data collection, annotation pipelines, alignment workflows, safety guardrails, and model serving.
• Establish scopes, goals, timelines, risks, and success metrics.
• 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.
• Develop and maintain custom evaluation frameworks to track model performance and user satisfaction.
• Drive quality evaluation initiatives alongside safety and toxicity baselines.
• 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.
• Partner with research, safety, product, and UXR leadership on prioritization, sequencing, and tradeoffs.
• Build and refine operational patterns, ontologies, and frameworks used to scale new capability development.

Required qualifications

• 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) and model deployment/serving.
• Proven ability to lead complex, multi-team programs in ambiguous, rapidly evolving environments.
• Strong analytical mindset comfortable working with data and user insights.
• Proficiency in SQL and Python.
• Exceptional communication skills.
• BS in a quantitative, scientific, or technical field.

Nice to have

• MS or PhD
• 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.

About the company

Character.AI empowers people to connect, learn and tell stories through interactive entertainment. The platform lets users engage with tens of millions of characters.