edge-deployment jobs in San Diego

As an Applied AI & Optimization Engineer, you will own optimization algorithms and ML systems for a manufacturing intelligence platform spanning planning, simulation, and AI assistance. You'll design scheduling and resource-allocation solvers, build analytics layers for operational intelligence, and lead LLM integration for both cloud and air-gapped DoD deployments.…See more The role requires 5+ years shipping production optimization or ML systems, deep Python proficiency, hands-on experience with MILP/constraint solvers or OR-Tools, and strong applied math skills in combinatorial optimization and statistical modeling.

Build production inference systems that turn ML research prototypes into deployed, performant components under strict latency, throughput, memory, and accuracy constraints. Own the full pipeline from model optimization through deployment: profile and tune tensor execution, write Rust/C++/CUDA where necessary, build evaluation machinery tied to product metrics, and maintain reproducible deployment contracts.…See more You'll collaborate with research teams to surface shipping risks early and translate algorithmic work into engineering reality with explicit performance budgets and regression gates.

Staff Engineer responsible for building and integrating core platform infrastructure for autonomous systems across SIL, HIL, VIL, and edge deployment workflows. The role involves developing reference implementations, platform tooling, and developer infrastructure while integrating hardware, sensors, controllers, and APIs.…See more Requires 5+ years of software engineering in robotics, autonomy, or embedded systems; strong C++ and Python skills; and hands-on experience debugging across Linux systems, hardware interfaces, and embedded communication using diagnostic tools.

Staff Engineer responsible for platform integration across simulation (SIL/HIL/VIL) and edge deployment workflows for autonomous systems. Will own reference implementations, developer tooling, and orchestration frameworks while integrating hardware, sensors, controllers, and APIs across Linux-based systems.…See more Requires 5+ years in robotics, autonomy, or embedded systems with strong C++ and Python skills, plus hands-on experience debugging embedded interfaces using oscilloscopes and protocol analyzers. Will partner cross-functionally to improve platform stability, scalability, and developer experience for mission-critical products.

Senior Staff Engineer responsible for designing and building an MLOps platform that supports distributed AI training, reinforcement learning, and foundation model development at scale. You will architect Kubernetes-native infrastructure for GPU workloads, design self-service AI development workflows, manage the data and model lifecycle, and lead platform distribution across cloud, on-premises, and air-gapped environments.…See more The role requires deep expertise in modern AI frameworks (PyTorch, Hugging Face Transformers), distributed systems, GPU scheduling, and cloud-native infrastructure, with a focus on enabling researchers and engineers to move from experimentation to production rapidly.