Senior AI Engineer

Company Overview

Imagine a piloted air taxi that takes off vertically, then quietly carries you and your fellow passengers over the congested city streets below, enabling you to spend more time with the people and places that matter most. At Joby, we've been working to make that dream a reality since 2009 and we're now in the final stages of certifying our aircraft with the FAA.

With plans to launch our aircraft in the US and Dubai, we're now scaling manufacturing and preparing for the launch of our commercial service.

Overview

We’re looking for a Senior AI Engineer to help bring pragmatic, production-grade AI capabilities into the systems that power Joby’s manufacturing and enterprise workflows. In this role, you’ll partner closely with software engineers, product, and operations teams to identify high-impact opportunities, build reliable AI services, and ensure they are safe, observable, and maintainable in real-world use. You’ll work in a fast-moving, high-ownership environment at the intersection of software, data, and aircraft manufacturing—where the output of your work directly improves how we build and scale.

Responsibilities

Identify, prototype, and deploy AI/ML solutions into production to improve internal workflows (e.g., knowledge retrieval, document automation, quality/process insights, decision support)
Architect and implement the company’s AI platform foundations so teams can ship AI safely and consistently

Design and build AI services that are scalable, secure, and observable

Build and maintain core ML/LLM infrastructure (model gateways, prompt/agent orchestration patterns, feature/embedding pipelines, vector search, caching, and rate limiting)
Design and operationalize event-based agent frameworks that can react to changes across internal systems

Develop evaluation strategies for LLM and ML systems (offline metrics, human-in-the-loop review, regression testing, prompt/model benchmarking)
Implement guardrails and safety mechanisms (policy checks, data controls, failure-mode analysis, fallback behaviors)
Own end-to-end delivery: data access/quality, model selection or training, deployment, monitoring, and lifecycle management

Contribute to engineering best practices: leading the effective use of AI coding tools to maximize developer velocity, code and data pipeline reviews, probabilistic testing and model evaluations, MLOps/operational readiness, and on-call/operational readiness

Required

B.S. or M.S. in Computer Science, Engineering, or equivalent practical experience5+ Years of software engineering experience

Strong Python skills and experience building ML/AI systems (LLMs and/or classical ML), including implementing and operating the surrounding infrastructure

Experience designing and implementing production ML/LLM systems end-to-end (service architecture, data pipelines, deployment, monitoring, and incident response).Solid understanding of APIs, data access patterns, and reliable system design

Strong communication skills and a track record of collaborating across functions to deliver outcomes

Desired

Experience with modern ML tooling (PyTorch, Hugging Face) and LLM frameworks (Lang Chain, Llama Index)

Experience with MLOps practices: data/versioning, model evaluation, CI/CD for ML, reproducible training/inference pipelines

Experience building internal AI frameworks/platforms (shared components, standards, guardrails, evaluation, and enablement) used by multiple teams

Familiarity with retrieval-augmented generation (RAG), embeddings, vector databases, and search/re-ranking approaches

Experience integrating AI into real workflows (human-in-the-loop, prompt evaluation, guardrails, failure-mode analysis)
Familiarity with data systems (streaming and/or batch) and analytics ecosystems

Additional…