Senior AI Engineer

Joby Aviation Joby Aviation · Robotics · San Carlos, CA · Factory Systems

Senior AI Engineer to build production-grade AI capabilities for manufacturing and enterprise workflows, focusing on AI platform foundations, scalable AI services, ML/LLM infrastructure, agent frameworks, evaluation strategies, and safety mechanisms. The role involves end-to-end delivery from data to monitoring and lifecycle management.

What you'd actually do

  1. Identify, prototype, and deploy AI/ML solutions into production to improve internal workflows (e.g., knowledge retrieval, document automation, quality/process insights, decision support).
  2. Architect and implement the company’s AI platform foundations so teams can ship AI safely and consistently.
  3. Design and build AI services that are scalable, secure, and observable.
  4. Build and maintain core ML/LLM infrastructure (model gateways, prompt/agent orchestration patterns, feature/embedding pipelines, vector search, caching, and rate limiting).
  5. Design and operationalize event-based agent frameworks that can react to changes across internal systems.

Skills

Required

  • Python
  • building ML/AI systems
  • implementing and operating surrounding infrastructure
  • designing and implementing production ML/LLM systems end-to-end
  • service architecture
  • data pipelines
  • deployment
  • monitoring
  • incident response
  • APIs
  • data access patterns
  • reliable system design
  • communication skills
  • collaborating across functions

Nice to have

  • modern ML tooling
  • PyTorch
  • Hugging Face
  • LLM frameworks
  • LangChain
  • LlamaIndex
  • MLOps practices
  • data/versioning
  • model evaluation
  • CI/CD for ML
  • reproducible training/inference pipelines
  • internal AI frameworks/platforms
  • retrieval-augmented generation (RAG)
  • embeddings
  • vector databases
  • search/re-ranking approaches
  • integrating AI into real workflows
  • human-in-the-loop
  • prompt evaluation
  • guardrails
  • failure-mode analysis
  • data systems
  • streaming data
  • batch data
  • analytics ecosystems

What the JD emphasized

  • production-grade AI
  • AI platform foundations
  • scalable, secure, and observable AI services
  • ML/LLM infrastructure
  • event-based agent frameworks
  • evaluation strategies for LLM and ML systems
  • guardrails and safety mechanisms
  • end-to-end delivery

Other signals

  • production-grade AI capabilities
  • AI platform foundations
  • scalable, secure, and observable AI services
  • ML/LLM infrastructure
  • event-based agent frameworks
  • evaluation strategies for LLM and ML systems
  • guardrails and safety mechanisms
  • end-to-end delivery