Associate Engineer - Edge AI

You will help build and sustain the platform alongside Wind River team that runs AI systems on the device - a Rust-based system that manages AI application lifecycles across a heterogeneous fleet of edge hardware. This is a hands-on role on hard systems problems: constrained compute, cross-architecture builds, OTA updates, and production-grade observability. You will start with well-scoped pieces of the platform, with mentoring and code review from senior engineers, and grow into larger areas of ownership. We expect you to make AI tooling a core part of how you work from day one.

What You'll Do:

• Implement and test components of the edge packaging and runtime system - a containerless / OCI-based approach to shipping AI systems to heterogeneous devices
• Contribute to the on-device orchestration platform and help support OTA update and lifecycle management for fielded devices
• Measure and improve inference and resource usage on constrained CPU/GPU/NPU hardware
• Work on cross-compilation and CI/CD pipelines for reproducible, secure multi-architecture builds
• Help integrate the device fleet with the cloud control plane and backend services (REST/gRPC, OpenAPI, MQTT)
• Reproduce, diagnose, and fix defects on real hardware, and turn what you learn into tests
• Use agentic AI coding tools daily for implementation, refactoring, review, and debugging - and build real judgment about where they help and where they don't
• Take part in the team's AI-assisted engineering practice: working patterns, guardrails, and what stays human-owned
• Keep the codebase and its documentation in a state that people can work in effectively

Mandatory Experience:

• Degree in Computer Science, Machine Learning, or a related field (or equivalent practical experience), and 1 to 3 years of professional software engineering experience
• Working proficiency in Rust or Python, and willingness to learn the other (C/C++ exposure is a plus)
• Hands-on experience using agentic AI tools in the software development workflow - AI coding assistants and agents for implementation, refactoring, and review - and an emerging point of view on their limits
• Comfortable on Linux and with Git-based workflows
• Experience with embedded Linux
• Systematic debugging instincts: you narrow problems down rather than guess
• Clear written communication and the ability to work with a team distributed across the NA, Europe, and India
• Genuine curiosity about the full stack - cloud, edge, and web/backend - and about how the networking stack works

Nice to have:

• Exposure to embedded Linux or systems programming
• ML frameworks or inference runtimes (PyTorch, ONNX, TensorRT, TFLite, OpenVINO) and the realities of running models on device
• NPU/GPU hardware such as Jetson, Qualcomm, Hailo, or Ambarella
• GenAI/LLM tooling or RAG pipelines; multi-arch builds; a major cloud provider (AWS, GCP, Azure); GitLab CI