Technical Program Manager, Model Alignment and Deployment

Character AI Character AI · AI Frontier · Redwood City, CA · Safety

Character AI is seeking a Technical Program Manager to lead cross-functional efforts in model alignment and deployment, connecting Post-Training, Safety Engineering, Trust & Safety, ML Infra/Model Serving, and UXR teams. The role involves driving planning and execution of programs related to data pipelines, alignment workflows (RLHF, DPO, Constitutional AI), safety guardrails, and model serving. Responsibilities include developing evaluation frameworks, ensuring operational excellence, and partnering with leadership on prioritization and tradeoffs. Requires 5+ years of TPM experience in AI/ML, familiarity with post-training and alignment concepts, and proficiency in SQL and Python.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 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.

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.
  • MS or PhD a plus.

What the JD emphasized

  • critical, cross-functional effort
  • technically complex and high-stakes work
  • ambiguous environments
  • technical fluency
  • fast-moving AI, ML, or research environment
  • shipping with quality and speed
  • Obsessive about data integrity, operational rigor, and process quality

Other signals

  • model alignment
  • deployment
  • safety engineering
  • ML infra
  • model serving
  • evaluation frameworks
  • RLHF
  • DPO
  • Constitutional AI
  • adversarial testing
  • red-teaming