Staff Software Engineer – AI Platform

General Motors•Austin, TX

1d•$189,300 - $333,400•Hybrid

About The Position

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Role: We are looking for a Staff Individual Contributor who combines exceptional software-engineering execution with enterprise architecture leadership to design and build the next generation of customer-facing AI systems. This role will lead the architecture and hands-on implementation of reusable modules for conversational AI, agent orchestration, grounding, memory, tool integration, policy enforcement, and AI platform services. The successful candidate will help establish durable technical patterns across cloud services and customer experiences while personally contributing production code in Python and Go and/or Java. This is a senior technical leadership role, not a people-management position. The engineer will influence architecture, mentor through technical leadership, raise engineering standards, and drive execution through design reviews and implementation—not through direct reports.

Requirements

8+ years of software engineering experience, including ownership of distributed, cloud-native, or customer-facing systems.
Track record as a hands-on technical architect, staff/principal engineer, or lead programmer delivering production systems at scale.
Expert Python and strong Go and/or Java programming skills.
Strong command of distributed-system design, service boundaries, API and data contracts, asynchronous processing, eventing, caching, consistency, and fault tolerance.
Production experience with LLM applications, agent orchestration, RAG, embeddings/vector search, tool use, MCP, A2A, and chatbot or workflow-based systems, including quality, safety, grounding, and regression evaluation.
Experience with inference and serving for low latency, high throughput, concurrency, autoscaling, traffic management, and cost/performance optimization.
Cloud and delivery experience with GCP and/or Azure, containers/Kubernetes, IAM, secrets, messaging, managed data services, CI/CD, infrastructure as code, and automated testing.
Ability to define and implement architecture artifacts and non-functional requirements covering security, privacy, observability, SLOs/SLIs, capacity, BCP, DR, and operational resilience.
Strong written and verbal communication, technical judgment, and ability to influence without direct authority.

Nice To Haves

Experience building conversational AI for automotive, mobility, contact center, consumer, or other high-scale customer-facing domains.
Experience with Vertex AI, Azure AI services, model gateways, vector databases, retrieval/evaluation platforms, and model observability.
Experience with privacy-aware personalization, governed memory, grounding, tool access, auditability, and deletion workflows.
Experience integrating CRM, identity, knowledge, telephony, messaging, or other enterprise systems.
Experience converging duplicated platforms through incremental adoption and leading cross-organization architecture initiatives without direct authority.

Responsibilities

Own architecture and hands-on delivery of complex customer-facing AI modules from PRD through production.
Turn product needs into clear component boundaries, APIs, data models, ERDs, sequence flows, deployment designs, test strategies, and AI evaluation plans.
Build reusable services for orchestration, agents, grounding, retrieval, memory, tool integration, conversation state, and channel adapters.
Lead technical decisions for inference, serving, reliability, security, observability, capacity, and operational readiness.
Establish reference implementations and engineering patterns for AI-enabled services across GCP and Azure.
Drive CI/CD, automated testing, AI evaluation and regression pipelines, release readiness, and production operations.
Partner with product, security, privacy, data, infrastructure, and application teams to resolve cross-system tradeoffs.
Provide technical leadership through architecture reviews, design documentation, code reviews, incident analysis, and production-readiness reviews.
Identify opportunities to simplify duplicated capabilities and standardize reusable platform interfaces.