{"id":698517,"url":"https://alion.io/job/plus-seniorstaff-machine-learning-engineer-motion-planning","title":"Senior/Staff Machine Learning Engineer, 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":"Diffusion Models","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"C++","optional":true},{"name":"CUDA","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-08-27T19:27:00Z","employer_posted_date":"2026-08-27","last_verified_at":"2026-10-31T21:32:45Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":64},"description":"We’re looking for a machine learning engineer to train and deploy the latest generation of ML-based planning algorithms on the extensive data we collect every day across our autonomous trucking fleet.\nResponsibilities\nDevelop state-of-the-art machine learning models for autonomous vehicle planning using rich map, perception, routing, and contextual sensor data.\nDesign and implement model architectures for trajectory generation, behavior planning, and decision making that balance accuracy, robustness, interpretability, and runtime efficiency.\nOwn the end-to-end machine learning lifecycle, including data curation, feature engineering, experimentation, training, evaluation, deployment, monitoring, and continuous improvement.\nDesign rigorous offline evaluation methodologies, validation pipelines, and metrics to measure planning quality, safety, robustness, and generalization.\nAnalyze model behavior, investigate failure cases, and improve performance through systematic error analysis and targeted experimentation.\nCollaborate closely with runtime, perception, prediction, mapping, and systems teams to deploy scalable machine learning solutions into production.\nDesign validation strategies and rule-based guardrails to ensure generated trajectories are feasible, safe, and compliant with traffic rules.\nStay current with advances in machine learning, robotics, and autonomous driving, translating research innovations into production systems.\nEnsure technical work complies with the company's Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes.\nRequired Skills\nBS, MS, or PhD in Computer Science, Robotics, Machine Learning, or a related field.\n4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems.\nStrong Python programming skills and experience with modern deep learning frameworks such as PyTorch.\nStrong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures.\nExperience designing datasets, experiments, validation methodologies, and metric-driven model evaluation.\nStrong software engineering skills with experience developing and maintaining production-quality software.\nExcellent debugging and analytical problem-solving skills, with the ability to investigate complex issues across datasets, model behavior, and production systems.\nExperience analyzing edge cases, tracing failures to their root cause, and improving model robustness through systematic experimentation.\nExperience designing validation methodologies, automated testing, and monitoring to ensure correctness, safety, and production reliability.\nStrong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment.\nExcellent communication skills and experience collaborating across cross-functional engineering teams.\nPreferred Skills\nExperience with planning, prediction, motion forecasting, or trajectory generation.\nExperience deploying machine learning models into production environments.\nWorking knowledge of modern C++ and production software development.\nExperience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization.\nExperience with large-scale distributed training or cloud-based ML infrastructure.\nPublications or open-source contributions in machine learning, robotics, or autonomous driving.\nBonus Qualifications\nCandidates who stand out typically have one or more of the following:\nDeep expertise in autonomous vehicle planning or motion prediction.\nExperience developing production ML systems for safety-critical applications.\nExperience leading technical direction for large ML projects.\nStrong intuition for balancing model quality, robustness, latency, and deployment constraints.\nDemonstrated ability to solve ambiguous, cross-functional engineering problems involving machine learning, software systems, and autonomous 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