Member of Technical Staff - Cloud & Edge AI Platforms

ABOUT WIND RIVER 

Wind River is a global leader in delivering software for mission-critical intelligent systems. For more than four decades, the company has been an innovator and pioneer, powering billions of systems that require the highest levels of security, safety, and reliability.  

Wind River helps customers across automotive, aerospace, defense, industrial, medical, and telecommunications industries solve complex technology challenges on their journey toward the new intelligent machine economy. The company’s software powers generation after generation of the safest, most secure systems in the world.  Examples include playing a key role in NASA space missions such as Artemis I, the James Webb Space Telescope, and multiple Mars rovers. We’ve achieved recent 5G milestones including the world’s first successful 5G data session with Verizon and building one of the largest Open RAN networks in the world with Vodafone. 

The company has received industry recognition for its technology innovation and leadership, and for its workplace culture, including global Great Place to Work certification and being named a “Top Workplace” for ten consecutive years. If you want to be part of a unique culture where the lived experience is based on our cultural attributes of growth mindset, customer-focus, and diversity, equity, inclusion & belonging, come join us and help advance the future software defined world. 

About the Role

We are looking for a Member of Technical Staff (MTS) to play a key technical leadership role in designing and advancing Wind River’s next‑generation intelligent systems platform. This position is ideal for an engineer who thrives at the intersection of cloud‑native infrastructure, edge computing, and AI/ML systems—and who wants to shape the architecture and implementation of distributed AI platforms used in mission‑critical environments.

What You’ll Do

Architecture & Platform Development

• Design and implement core components of Wind River’s cloud‑to‑edge AI platform, including orchestration layers, data pipelines, and model lifecycle management.
• Develop scalable, modular, and secure software architectures for distributed AI workloads across heterogeneous edge environments.
• Build cloud‑native services and APIs that integrate seamlessly with Wind River Studio and edge operating systems (VxWorks, Linux).
• Contribute to architectural decisions involving microservices, containerization, service mesh, and hybrid cloud deployments.

AI/ML Infrastructure & Optimization

• Implement systems for model deployment, versioning, CI/CD for AI, and real‑time inference pipelines.
• Integrate AI frameworks (TensorFlow, PyTorch, ONNX Runtime, TensorRT) into cloud‑edge workflows.
• Optimize inference performance across diverse hardware accelerators (GPU, NPU, FPGA, VPU).

Performance, Reliability & Security

• Ensure platform components meet stringent requirements for determinism, safety, and reliability in mission‑critical industries.
• Implement security best practices for distributed AI, including model protection, secure communication, and data integrity.
• Profile, tune, and optimize system performance across cloud and edge environments.

Collaboration & Technical Leadership

• Work closely with architects, senior engineers, and product managers to translate platform vision into robust implementations.
• Mentor junior engineers and contribute to engineering best practices, design reviews, and technical roadmaps.
• Engage with customers and partners to understand requirements and support advanced solution development.
• Contribute to open‑source initiatives and represent Wind River in technical communities when appropriate.

What You Bring

Technical Depth

• 8+ years of experience in software engineering, distributed systems, cloud platforms, or embedded systems.
• Strong hands‑on experience with cloud‑native technologies (Kubernetes, containers, microservices).
• Solid understanding of AI/ML infrastructure, model deployment, and edge inference optimization.
• Proficiency in C/C++, Python, and modern DevOps practices (CI/CD, GitOps).
• Experience with Linux‑based systems, r