{"id":1066736,"url":"https://alion.io/job/clarios-machine-learning-engineer-technical-lead","title":"Machine Learning Engineer-Technical Lead","company":{"id":6276,"name":"Clarios","domain":"clarios.com","url":"https://alion.io/company/clarios","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":86,"open_postings":31,"ghost_share":0,"stale_share":0.548,"repost_share":0,"time_to_fill_p50_days":42,"computed_at":"2026-09-26T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Monterrey, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":58000,"max_usd":139000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":636},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-06-10T00:00:00Z","employer_posted_date":"2026-06-10","last_verified_at":"2026-09-26T20:40:25Z","board_verified":true,"closed_at":null,"days_open":109,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":108},"description":"The Role\nWe are looking for a seasoned Machine Learning Engineer with strong leadership capabilities to join the Connected Services AIML team as a Technical Lead. You will architect and implement the algorithmic solutions that power our industrial IoT platform, while guiding a team of engineers and scientists to deliver high-impact, production-grade systems - turning large-scale sensor data into intelligence that keeps connected assets running.\nWhat You’ll Do (Impact Areas)\nArchitect & deploy ML solutions: design and deploy models for predictive maintenance, anomaly detection, asset optimization, and time-series forecasting using large-scale sensor data from connected devices.\nBuild production infrastructure: develop robust data pipelines and real-time inference systems integrated with edge and cloud infrastructure.\nLead technical execution: own the end-to-end delivery of ML projects, from ideation to deployment.\nMentor & set the standard: guide a team of ML and software engineers; define and enforce best practices in model development, testing, and deployment.\nPartner cross-functionally: work with product managers and domain experts to align technical solutions with business goals.\nWhat Success Looks Like\nProduction-grade models for predictive maintenance and anomaly detection deployed and reliably serving real-time inference at scale.\nRobust, well-documented pipelines moving sensor data from edge to cloud with minimal latency and downtime.\nA high-performing ML and software engineering team operating against clear, enforced best practices.\nTechnical roadmaps that map directly to business outcomes and are delivered on time, from ideation to deployment.\nCore Competencies\nTechnical leadership and end-to-end project ownership in a production environment.\nStrong ML/AI foundations: supervised and unsupervised learning, classification, regression, clustering, and deep learning.\nProduction engineering mindset: data pipelines, real-time inference, and edge + cloud deployment.\nMentorship and the ability to raise the bar across an engineering team.\nExcellent communication and cross-functional collaboration.\nWhat You Bring (Qualifications)\nRequired\nBachelor’s degree in Computer Science, Electrical Engineering, Statistics, or a related field.\n5+ years of experience in machine learning and software engineering.\nProven experience leading technical teams or projects in a production environment.\nSolid understanding of core ML/AI algorithms: supervised and unsupervised learning, classification, regression, clustering, and deep learning techniques.\nStrong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn), SQL, and cloud platforms.\nExperience with time-series data.\nExcellent communication and cross-functional collaboration skills.\nPreferred\nAdvanced degree in Computer Science, Electrical Engineering, Statistics, or a related field.\nExperience in industrial sectors.\nKnowledge of MLOps tools (MLflow, Airflow, Docker, Kubernetes).\nExperience with one or more of signal processing, edge computing, and physics-informed ML models.\nAbout Clarios:\nClarios is the global leader in advanced, low-voltage battery technologies for mobility. Our batteries and smart solutions power nearly every type of vehicle and are found in 1 of 3 cars on the road today. With around 18,000 employees in over 100 countries, we bring deep expertise to our Aftermarket and OEM partners, and reliability, safety and comfort to everyday lives. We answer to the planet with a rigorous sustainability focus - advancing best-in-class sustainability practices and advocating for them across our industry. We work to ensure 100% of our products sold are recyclable, and we recycle 8,000 batteries an hour in our network. You can find more information here (PDF).\nTo All Recruitment Agencies: Clarios does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Clarios employees or any other company location. Clarios is not responsible for any fees related to unsolicited resumes/CVs.\nEqual Employment Opportunity:\nClarios, LLC is an equal employment opportunity and affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, status as a qualified individual with a disability, or any other characteristic protected by law. For more information, please view EEO is the Law, EEO is the Law (supplement), and Pay Transparency Non-discrimination. If you are an individual with a disability and you require an accommodation during the application process, please email .\nA Note to Job Applicants: please be aware of scams being perpetrated through the Internet and social media platforms. Clarios will never require a job applicant to pay money as part of the application or hiring process.","description_format":"text","description_chars":4935,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Energy Storage","Automotive Components"],"lifecycle":[{"event":"open","at":"2026-09-19T18:53:52Z"}],"liveness":{"score":11,"band":"cold","label":"Long shot","p_open":1,"p_active":0.377,"p_room":0.28,"age_days":108,"expected_fill_days":42,"reasons":["conf:5","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-09-26T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/clarios-machine-learning-engineer-technical-lead","json_url":"https://alion.io/job/clarios-machine-learning-engineer-technical-lead.json","meta":{"generated_at":"2026-09-27T00:34:21Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":485,"day_limit":5000,"remaining_today":4515,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}