{"id":459120,"url":"https://alion.io/job/teleo-senior-autonomy-controls-engineer-learning-based-control","title":"Senior Autonomy Controls Engineer – Learning-Based Control","company":{"id":176548,"name":"Teleo","domain":"teleo.ai","url":"https://alion.io/company/teleo","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":8,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-01T05:45:00Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":131000,"max_usd":250000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":2941},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Interpretability","optional":false},{"name":"Model Predictive Control","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-02-13T23:37:13Z","employer_posted_date":"2026-02-13","last_verified_at":"2026-10-01T00:02:38Z","board_verified":true,"closed_at":null,"days_open":229,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":229},"description":"About the Role\nOwn the transition from manually tuned MPC-based vehicle control to learning-driven control policies that adapt across vehicles with minimal human intervention, while maintaining safety and interpretability.\nCore Responsibilities\nPractical understanding of vehicle dynamics and system identification\nPractical experience in generating test plans, collecting real-world data, and using real-world data for system identification of plant models for automatic control.\nDesign and implement learning-based control approaches (imitation learning, reinforcement learning, hybrid MPC + learning)\nReduce dependence on hand-tuned control parameters through data-driven methods\nIntegrate learned controllers into the existing vehicle control stack safely and incrementally\nDefine interfaces between classical control (MPC, PID, state estimation) and learning-based components\nWork closely with the Principal Controls Engineer to translate classical control insights into learning-friendly formulations\nEstablish validation criteria for learned control policies before real-vehicle deployment\nRequired Qualifications\n2-3 years of experience with experimental data collection and data analysis to estimate parameters of a plant model used for automatic control\nStrong software engineering skills in C, C++, or Python (production-quality code)\nDeep understanding of modern robotics control systems\nExperience with learning-based control or policy optimization for real-world systems\nComfort working close to hardware and real-time constraints\nPreferred Qualification\nReinforcement learning or imitation learning for control\nModel-based RL, residual learning, or hybrid MPC architectures\nControl under uncertainty and partial observability\nDebugging and validating control systems on physical platforms\nBonus Points\nExperience deploying learned controllers on vehicles or mobile robots\nFamiliarity with safety-constrained learning methods\nBackground spanning both classical and modern control 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