{"id":698397,"url":"https://alion.io/job/plus-seniorstaff-software-engineer-machine-learning-runtime-motion-planning","title":"Senior/Staff Software Engineer (Machine Learning Runtime), Motion Planning","company":{"id":22363,"name":"PlusAI","domain":"plus.ai","url":"https://alion.io/company/plus","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":43,"ghost_share":0.419,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":130000,"max":220000,"currency":"USD","period":"year","gross":null,"usd_annual":220000},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"Machine Learning","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"TensorRT","optional":false},{"name":"CI/CD","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-08-27T20:23:16Z","employer_posted_date":"2026-08-27","last_verified_at":"2026-10-31T07:13:19Z","board_verified":true,"closed_at":null,"days_open":64,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":64},"description":"Responsibilities\nDesign, implement, and optimize the production runtime pipeline for ML-based autonomous vehicle planning.\nIntegrate machine learning models into a scalable, safety-critical planning software stack.\nOptimize end-to-end inference performance through algorithm optimization, C++ implementation, profiling, memory optimization, and efficient execution on compute-constrained embedded platforms.\nDevelop high-performance C++ components for feature extraction, inference, post-processing, trajectory generation, and runtime orchestration.\nDesign runtime validation, safety guardrails, and fallback mechanisms to ensure generated trajectories are feasible, robust, and compliant with traffic rules.\nInvestigate complex runtime issues by tracing failures across the planning pipeline, from feature extraction through inference to trajectory generation.\nDebug difficult edge-case scenarios using logs, profiling tools, visualization, offline replay, and quantitative analysis to identify root causes and implement robust solutions.\nBuild tooling and infrastructure to improve debugging, observability, performance analysis, testing, and runtime monitoring.\nCollaborate closely with machine learning engineers to efficiently deploy new models into production while maintaining system reliability and performance.\nDrive continuous improvements in runtime architecture, scalability, reliability, and maintainability.\nEnsure technical work complies with the company's Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes.\nRequirements\nBS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related field.\n4+ years of experience developing high-performance production software.\nStrong C++ programming skills with experience building, optimizing, and maintaining large-scale software systems.\nExperience optimizing real-time systems, including latency, throughput, memory usage, and multithreaded performance.\nExperience deploying machine learning inference into production environments.\nFamiliarity with TensorRT, CUDA, ONNX Runtime, or similar inference acceleration frameworks.\nStrong software engineering fundamentals, including software architecture, testing, and code quality.\nExcellent debugging and analytical problem-solving skills, with the ability to systematically investigate complex software and system-level issues.\nExperience tracing failures across multi-stage pipelines, identifying root causes, and implementing reliable long-term solutions.\nExperience designing validation strategies, automated testing, runtime monitoring, and observability for production systems.\nStrong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment.\nExcellent communication skills and experience collaborating across machine learning and systems engineering teams.\nPreferred Skills\nExperience with autonomous driving planning, prediction, or robotics software.\nExperience profiling CPU and GPU performance using modern profiling tools.\nExperience deploying software on embedded GPU platforms.\nFamiliarity with PyTorch and machine learning workflows.\nExperience with distributed systems, CI/CD, and cloud infrastructure.\nBonus Qualifications\nCandidates who stand out typically have one or more of the following:\nDeep expertise in modern C++ (C++17/20) and high-performance software engineering.\nExperience optimizing production inference pipelines for low latency and high throughput.\nExperience developing internal debugging, visualization, profiling, or observability tools.\nProven ability to diagnose and resolve complex production issues involving machine learning, runtime software, and system integration.\nStrong systems thinking with the ability to balance correctness, performance, maintainability, scalability, and safety.\nDemonstrated technical leadership and experience mentoring engineers on large production software 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