Senior/Staff Software Engineer, PlusAI

• BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related field
• 4+ years of experience developing high-performance production software
• Strong C++ programming skills with experience building, optimizing, and maintaining large-scale software systems
• Experience optimizing real-time systems, including latency, throughput, memory usage, and multithreaded performance
• Experience deploying machine learning inference into production environments
• Familiarity with TensorRT, CUDA, ONNX Runtime, or similar inference acceleration frameworks
• Strong software engineering fundamentals, including software architecture, testing, and code quality
• Excellent debugging and analytical problem-solving skills, with the ability to systematically investigate complex software and system-level issues
• Experience tracing failures across multi-stage pipelines, identifying root causes, and implementing reliable long-term solutions
• Experience designing validation strategies, automated testing, runtime monitoring, and observability for production systems
• Strong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment
• Excellent communication skills and experience collaborating across machine learning and systems engineering teams

Desirable

• Design, implement, and optimize the production runtime pipeline for ML-based autonomous vehicle planning
• Integrate machine learning models into a scalable, safety-critical planning software stack
• Optimize end-to-end inference performance through algorithm optimization, C++ implementation, profiling, memory optimization, and efficient execution on compute-constrained embedded platforms
• Develop high-performance C++ components for feature extraction, inference, post-processing, trajectory generation, and runtime orchestration
• Design runtime validation, safety guardrails, and fallback mechanisms to ensure generated trajectories are feasible, robust, and compliant with traffic rules
• Investigate complex runtime issues by tracing failures across the planning pipeline, from feature extraction through inference to trajectory generation
• Debug difficult edge-case scenarios using logs, profiling tools, visualization, offline replay, and quantitative analysis to identify root causes and implement robust solutions
• Build tooling and infrastructure to improve debugging, observability, performance analysis, testing, and runtime monitoring
• Collaborate closely with machine learning engineers to efficiently deploy new models into production while maintaining system reliability and performance
• Drive continuous improvements in runtime architecture, scalability, reliability, and maintainability
• Ensure technical work complies with the company's Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes