scientific-computing jobs in San Diego

Design, implement, and optimize advanced algorithms for radar, optical, and infrared sensor systems, working with scientists and engineers to build high-performance backend systems for scientific computing in distributed environments. Translate existing code for GPU/CUDA acceleration, integrate and refactor scientific codebases, and work in Linux/Unix environments.…See more Requires 6+ years of C/C++ backend development (or 4+ with a Master's), expertise in high-performance computing, parallel/distributed processing (MPI), GPU/CUDA programming, and foundational knowledge of AI/ML and LLM concepts.

Own the complete release and deployment strategy for Elemynt's secure AI infrastructure platform, building systems that ensure every release is versioned, reproducible, observable, and safe to operate across enterprise and scientific computing environments. Design and implement deployment automation, CI/CD pipelines, artifact management, and operational visibility (logs, metrics, alerts) that work reliably in customer-managed and restricted environments.…See more Debug incidents, identify root causes, and systematize fixes into reusable automation. Partner with product and engineering teams to embed deployment readiness into the platform from the start.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team.…See more The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.