Staff Software Engineer

General Motors•Austin, TX

17d•Hybrid

About The Position

General Motors (GM) is committed to a future defined by Zero Crashes, Zero Emissions, and Zero Congestion. To achieve this, GM is investing in the people, software, and systems that enable safer, better, and more sustainable transportation. The Vision and Automation Services organization within GM develops innovative software solutions for global manufacturing, focusing on vision-as-a-service, automation, and AI enablement. This involves utilizing modern cloud platforms, plant-floor systems, computer-vision systems for inspection, data models, data platforms, and AI-assisted software engineering throughout the development lifecycle. The organization also applies agentic workflows and emerging technologies to create products that enhance worker safety and efficiency. This role seeks a Staff Software Engineer to take technical ownership of significant portions of a complex, production-critical platform. The position offers a high degree of autonomy and requires rapid onboarding to understand the full software development lifecycle, including requirements, design, testing, deployment, observability, and continuous improvement. At this level, the engineer will also facilitate cross-team collaboration, align technical dependencies and delivery plans, and guide teams in making and executing sound decisions. The role demands strong technical judgment, disciplined execution, and the ability to navigate ambiguity to achieve progress and alignment. There is a significant opportunity to influence technical direction and engineering practices for platform evolution. The work involves a sophisticated, highly integrated platform encompassing Python and FastAPI backend services, React and TypeScript frontend applications, Java and Spring Boot services, event-driven components, cloud infrastructure, data stores, security controls, and manufacturing-system integrations. The platform is deployed via highly automated CI/CD pipelines using GitHub Actions, Azure, and GitOps tooling. Contributions will also extend to computer-vision and edge-to-cloud solutions, such as in-plant monitoring systems that reliably connect cameras, plant infrastructure, machine-learning capabilities, operational services, and user-facing workflows. This position blends strategic architecture with hands-on implementation. The engineer will help set engineering direction, mentor other engineers, collaborate with manufacturing and product leaders, and deliver scalable solutions that operate reliably in real-world plant environments. Practical machine-learning expertise, including model fine-tuning and productionization for reliable inference, is also valued. The scope offers substantial opportunities for broader architectural ownership, increased influence across teams, and shaping the platform's future.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or a related field.
8+ years of professional software engineering experience, including substantial experience delivering production systems.
In-depth experience designing and developing distributed applications, microservices, APIs, and event-driven systems.
Strong professional experience with Python and modern backend frameworks such as FastAPI.
Strong professional experience with React, TypeScript, and modern frontend application development.
Familiarity with Java and Spring Boot services in distributed enterprise platforms.
Experience designing data models and working with databases, including relational, graph-based, and other fit-for-purpose technologies such as PostgreSQL.
Experience with modern cloud and container technologies such as Azure, Docker, and Kubernetes (K8s), along with infrastructure automation and comparable DevOps practices.
Experience building secure applications with identity, authentication, authorization, role-based access, secrets, and secure service-to-service communication.
Experience with automated testing, continuous integration and delivery pipelines, GitHub Actions or comparable technologies, source control, and production release practices.
Experience with observability, including structured logging, metrics, tracing, health checks, alerting, and operational dashboards.
Experience designing and supporting integrations with enterprise or industrial systems using APIs, messaging, or event-streaming technologies.
Ability to analyze complex systems, balance competing requirements, and make decisions that support long-term platform health.
Ability to communicate technical concepts clearly to software engineers, product leaders, manufacturing partners, and other stakeholders.
Willingness to travel periodically to support collaboration, plant deployments, production readiness, or other business needs.
Ability to work effectively in a highly collaborative, cross-functional environment.

Nice To Haves

Experience leading architecture and technical strategy for a platform used across multiple products, sites, or business units.
Experience delivering software for manufacturing, industrial automation, automotive, robotics, computer vision, or other operational environments.
Experience with plant-edge computing, industrial cameras, device health, telemetry, or edge-to-cloud architectures.
Experience integrating systems with different ownership models, data contracts, release cycles, and availability requirements.
Experience with Azure services such as Event Hubs, managed databases, object storage, Key Vault, managed identity, or equivalent cloud services.
Experience with Redis or comparable caching and real-time communication technologies.
Experience with infrastructure as code, Helm, GitOps, Argo CD, or comparable deployment automation.
Experience applying AI-assisted software engineering across design, implementation, testing, debugging, documentation, and continuous improvement, including familiarity with large-language-model-enabled development tools, retrieval-augmented generation, intelligent automation, agentic systems, or governed AI integrations.
Experience fine-tuning machine-learning models and productionizing them for reliable inference, including model evaluation, deployment, monitoring, and lifecycle management.
Familiarity with machine-learning or computer-vision systems and the software interfaces required to operate them reliably, without requiring deep specialization in model development.
Experience evolving shared frontend and backend capabilities across multiple teams.
Experience with data federation, graph-based models, domain ownership, or event-driven data synchronization.
Demonstrated ability to develop, inspire, and motivate engineers through technical leadership and mentorship while fostering collaboration across organizational and functional boundaries.
Ability to provide strategic perspective while remaining willing to work hands-on when needed, take intelligent risks, and champion change with sound judgment.
Evidence of integrity, accountability, initiative, and ownership from concept through production operation.
Strong problem-solving, written communication, verbal communication, and stakeholder-management skills.
Curiosity and a commitment to continuous learning in software engineering, cloud platforms, manufacturing technology, and artificial intelligence.

Responsibilities

Lead the architecture and delivery of scalable software capabilities used by manufacturing teams across multiple plants.
Translate business and operational needs into clear technical requirements, service boundaries, interface contracts, and delivery plans.
Design, build, test, and operate production-grade services across Python and FastAPI, React and TypeScript, adjacent Java and Spring Boot, and modern cloud technologies.
Develop event-driven workflows that ingest, normalize, persist, and distribute operational data and alerts.
Establish reliable integrations with manufacturing, workforce, equipment, analytics, and enterprise systems through well-defined APIs and messaging patterns.
Design data models and platform solutions using databases—including relational, graph-based, and other fit-for-purpose technologies—alongside caching, object storage, and metrics platforms.
Build secure software with strong authentication, authorization, plant-level access controls, secrets management, and defense-in-depth practices.
Improve platform reliability through observability, health monitoring, performance engineering, automated testing, incident learning, and operational readiness.
Lead engineering practices for highly automated continuous integration and delivery, infrastructure automation, containerized deployments, and environment promotion through GitHub, Azure, and GitOps tooling.
Guide the evolution of shared platform capabilities so that multiple manufacturing products can reuse common services, user experiences, and operational patterns.
Contribute to in-plant monitoring and related computer-vision solutions by helping connect plant-edge applications, cameras, machine-learning inference, cloud services, alerts, and operator workflows.
Apply artificial intelligence thoughtfully to software engineering and manufacturing operations, including sophisticated AI-assisted development, intelligent automation, and agentic capabilities that can operate across governed tools and services.
Contribute to machine-learning workflows by fine-tuning models, productionizing them for reliable inference, and helping establish dependable evaluation, deployment, monitoring, and lifecycle practices.
Evaluate emerging technologies and turn promising ideas into secure, maintainable prototypes and production solutions.
Make sound architectural tradeoffs among delivery speed, maintainability, performance, security, cost, and operational complexity while facilitating alignment across teams.
Provide technical leadership across teams by communicating decisions clearly, aligning stakeholders, promoting common engineering practices, and mentoring engineers.
Participate in technical planning, design reviews, code reviews, troubleshooting, and other related duties as assigned.