{"id":1171090,"url":"https://alion.io/job/recruitment-room-control-system-engineer","title":"Control System Engineer","company":{"id":100174,"name":"Recruitment Room","domain":"recruitmentroom.net","url":"https://alion.io/company/recruitment-room","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"contractor","work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":57600,"max":96000,"currency":"USD","period":"month","gross":null,"usd_annual":1152000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Embedded C","optional":false},{"name":"PID Control","optional":false},{"name":"Python","optional":false},{"name":"ROS","optional":false},{"name":"SciPy","optional":false},{"name":"C","optional":true}],"status":"live","first_seen_at":"2026-09-22T08:40:00Z","employer_posted_date":null,"last_verified_at":"2026-09-22T08:40:00Z","board_verified":false,"closed_at":null,"days_open":40,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":40},"description":"Control System Engineer\nContract · Remote\nAnnual Compensation Equivalent: $57,600-$96,000\nHourly Rate: $30-$50/hr\nAnnual compensation equivalent is calculated at 40 hours per week across 48 workable weeks per year and is not a guaranteed salary.\nAbout the Role\nWe are seeking experienced Control System Engineers to apply advanced control engineering expertise to real-world systems and AI evaluation workflows.\nThe role focuses on designing, tuning, modeling, implementing, and validating robust control systems for physical applications such as robotics, drones, automotive systems, and industrial hardware.\nYou will work across classical and modern control approaches, develop plant models from first principles and empirical data, and translate control strategies into implementations suitable for real-world deployment.\nNo prior AI experience is required. The role is centered on your control engineering expertise and practical experience deploying controllers on physical systems.\nWhat You’ll Do\nDesign and tune PID controllers and advanced control strategies such as LQR, MPC, and Kalman filtering.\n\nDevelop controllers for physical systems including robotics, drones, automotive platforms, and industrial hardware.\n\nBuild plant models from first principles and validate them against empirical data.\n\nApply state-space and transfer-function modeling methodologies.\n\nImplement and verify control algorithms using Python.\n\nWork with open-source control and modeling tools such as Python Control, SciPy, CasADi, do-mpc, Julia ControlSystems, and OpenModelica.\n\nAnalyze real-world system performance and identify opportunities for improvement.\n\nIterate on controller design to improve stability, performance, and operational reliability.\n\nDocument control strategies, engineering decisions, assumptions, and validation results clearly.\n\nCommunicate technical findings effectively in written and verbal discussions.\n\nCollaborate remotely with interdisciplinary contributors while maintaining technical independence.\n\nWhat We’re Looking For\nBachelor’s degree or higher in Control Engineering, Electrical Engineering, Mechanical Engineering, Mechatronics, Aerospace Engineering, or a related discipline.\n\n5+ years of hands-on controller design experience after completing your degree.\n\nDemonstrated experience deploying control systems on real physical hardware, not simulation-only work.\n\nStrong experience developing and validating physical plant models using first-principles and data-driven approaches.\n\nExperience delivering classical control systems such as PID on real hardware.\n\nExperience implementing at least one modern control method, such as:\nLinear Quadratic Regulator (LQR)\n\nModel Predictive Control (MPC)\n\nKalman filtering\n\nStrong Python skills for control development, debugging, simulation, and validation.\n\nExperience with open-source control engineering toolchains.\n\nExcellent written and verbal English communication skills.\n\nAbility to explain technical decisions clearly and precisely.\n\nPreferred Technical Experience\nAdditional experience in the following areas is valuable:\nMaster’s or PhD in a relevant engineering discipline\n\nProduction MPC using CasADi or do-mpc\n\nModelica or OpenModelica\n\nJulia and Julia-based control systems\n\nSystem identification\n\nEmbedded C/C++\n\nROS\n\nNonlinear control\n\nRobust control\n\nAdaptive control\n\nTechnical publications\n\nOpen-source control engineering contributions\n\nEngagement & Compensation\nContract engagement\n\nRemote\n\nCompensation: $30-$50/hr\n\nAnnual compensation equivalent: $57,600-$96,000 based on a 40-hour workweek and 48 workable weeks per year\n\nActual earnings depend on hours worked and available project 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