{"id":1141607,"url":"https://alion.io/job/24mag-remote-control-system-engineer-30-50hour","title":"Remote | Control System Engineer - $30-$50/hour","company":{"id":2667046,"name":"24Mag","domain":"24-mag.com","url":"https://alion.io/company/24mag","size_band":"51-200","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":null,"truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":null,"employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","hiring_geo_confidence":"inferred","locations":["New York, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":109000,"max_usd":240000,"period":"year","method":"role_country_seniority_unknown","sample_n":2086},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Embedded C","optional":false},{"name":"Julia","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":"2026-09-23T12:13:03Z","employer_posted_date":null,"last_verified_at":"2026-09-23T12:13:03Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"This a Full Remote job, the offer is available from: New York (USA)\nWe are sharing a specialised consulting opportunity for experienced Control System Engineers with strong expertise in PID control, plant modelling, controller design, Python-based control development, and real-system deployment to contribute to an advanced AI training and engineering-evaluation project.\nSelected professionals will design and evaluate controllers for physical systems, build and validate plant models, implement control algorithms using open-source technical stacks, and apply practical engineering judgement to real-world control scenarios. No prior experience in AI is required.\nKey Responsibilities\nController Design & Tuning\nDesign and tune PID controllers for physical systems\n\nApply modern control methods such as LQR, MPC, or Kalman filtering\n\nSelect control strategies appropriate to system dynamics and performance requirements\n\nEvaluate stability, responsiveness, robustness, and real-world operating behaviour\n\nRefine controller parameters based on measured system performance\n\nPlant Modelling & System Identification\nDevelop plant models from first principles and empirical system data\n\nApply state-space and transfer-function modelling techniques\n\nValidate mathematical models against real-world measurements\n\nIdentify modelling assumptions, uncertainties, and performance limitations\n\nRefine models as additional system data becomes available\n\nControl Software & Technical Implementation\nImplement control algorithms in Python using open-source engineering libraries\n\nWork with tools such as python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, or OpenModelica\n\nDebug and validate control code across realistic engineering scenarios\n\nTranslate mathematical control strategies into reliable technical implementations\n\nMaintain clear and reproducible control-development workflows\n\nReal-System Deployment & Performance Analysis\nDeploy and evaluate controllers on robotics, drones, automotive, industrial, or comparable physical systems\n\nAnalyse system behaviour under realistic operating conditions\n\nIdentify performance limitations, instability, or unexpected responses\n\nIterate on controller and model design to improve real-world operation\n\nApply practical judgement beyond simulation-only results\n\nEngineering Evaluation & Collaboration\nDocument control strategies, engineering decisions, and technical assumptions clearly\n\nProvide structured feedback on control-system designs and outputs\n\nReview engineering approaches for technical accuracy and practical feasibility\n\nCollaborate remotely with interdisciplinary technical contributors\n\nContribute domain expertise to AI training and engineering-evaluation workflows\n\nIdeal Profile\nBachelor's degree or higher in Control, Electrical, Mechanical, Mechatronics, Aerospace Engineering, or a closely related field\n\n5+ years of post-degree hands-on controller-design experience\n\nProven experience deploying control systems on real hardware rather than simulation-only environments\n\nStrong practical expertise with PID control\n\nExperience implementing at least one modern control approach such as LQR, MPC, or Kalman filtering\n\nStrong plant-modelling skills using first-principles and data-driven methods\n\nExperience with state-space and transfer-function techniques\n\nFluency in Python for control development, debugging, and validation\n\nFamiliarity with open-source control and optimisation tools\n\nStrong system-performance analysis and troubleshooting ability\n\nExcellent written and verbal English communication skills\n\nMaster's or PhD-level training is advantageous\n\nExperience with CasADi, do-mpc, Modelica/OpenModelica, Julia, system identification, embedded C/C++, ROS, or nonlinear, robust, or adaptive control is beneficial\n\nPublications or open-source contributions in relevant technical areas are also valuable\n\nNo prior AI-training or model-evaluation experience is required\n\nEngagement Details\nIndependent contractor engagement\n\nFully remote\n\nCompensation: $30-$50/hour\n\nWork will involve controller design, PID tuning, plant modelling, real-system deployment, Python-based control development, and technical evaluation\n\nStrong hands-on experience deploying controllers to physical systems is central to this engagement\n\nAssignments may involve robotics, drones, automotive platforms, industrial hardware, or comparable dynamic systems\n\nTechnical environments may include Python control libraries, optimisation frameworks, Modelica tools, Julia, ROS, or embedded systems\n\nProject scope, workload, control scenarios, and evaluation standards may evolve depending on project requirements\n\nWork must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party\n\nAbout the Platform\nThis opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.\nBy submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy","description_format":"text","description_chars":5224,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Upper-Intermediate (B2)","optional":false}]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-23T12:13:03Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":58,"reasons":["seen:0","win:early"],"computed_at":"2026-09-23T21:05:17Z"},"pay":null,"html_url":"https://alion.io/job/24mag-remote-control-system-engineer-30-50hour","json_url":"https://alion.io/job/24mag-remote-control-system-engineer-30-50hour.json","meta":{"generated_at":"2026-09-23T21:05:17Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}