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Toyota North America · 1 day ago

Artificial Intelligence Engineer Manager

Plano, TX

Full-time

Onsite

Senior Level, Lead/Staff

8+ years exp

Toyota North America is a leading brand in the automotive industry, focused on innovative mobility solutions. They are seeking an Artificial Intelligence Engineer Manager to lead teams in designing and operating production-grade AI systems, enhancing efficiency and driving AI adoption across the organization.

Manufacturing

Comp. & Benefits

No H1B

Responsibilities

Own end-to-end delivery of AI services: requirements to production, including architecture, development, testing, deployment, and operations

Define reference architectures, reusable components, and patterns for model serving, vector search, data pipelines, and retrieval-augmented generation, and drive adoption across teams

Establish and enforce software development lifecycles for AI: code reviews, automated testing, model evaluation gates, performance and cost budgets, release management, and incident response

Operate production AI systems to reliability objectives: monitor latency, throughput, drift, and cost; drive root-cause analysis and continuous improvement

Integrate security and Responsible AI guardrails: secrets management, data protection, content safety, human-in-the-loop, audit evidence, and policy-aligned deployment checks

Lead capacity planning, backlog, and roadmap; coordinate cross-team dependencies; manage vendor engagements and optimize cloud spend

Coach and grow engineers and tech leads; set goals, conduct design reviews, and develop succession pipelines

Qualification

AI systems architectureProduction AI deliveryTeam leadershipModern AI stacksSecurityComplianceSoftware development lifecycleCapacity planningCoaching engineersCross-team coordination

Required

Bachelor's degree or similar in Computer Science, Artificial Intelligence, Data Science, Business Administration, or a related field, or equivalent experience

Minimum of 8–12 years in software engineering with 3+ years leading teams or managing managers delivering production AI systems

Proven launch of at least two AI applications or platforms serving 10k+ daily requests or equivalent load, with documented service level objectives for latency and availability

Hands-on experience with modern AI stacks: containerized microservices, APIs, vector databases, model serving frameworks, and CI/CD pipelines

Track record of reducing production incidents or latency and cost by measurable amounts

Security and compliance in practice: implemented secrets management, access controls, data minimization, and audit-ready logging across services

Preferred

Delivered retrieval-augmented generation systems at scale, including prompt and version management, guardrails, evaluation harnesses, and offline A/B testing

Experience with edge or on-vehicle inference and mixed cloud and edge deployments, including model optimization and hardware acceleration

Built shared platforms or reusable AI components adopted by product teams, with published documentation and SDKs