{"id":1189409,"url":"https://alion.io/job/united-machine-learning-engineering-manager","title":"Machine Learning Engineering Manager","company":{"id":18409,"name":"United","domain":"united.com","url":"https://alion.io/company/united","size_band":null,"is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Chicago, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":117610,"max":153146,"currency":"USD","period":"year","gross":null,"usd_annual":153146},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Fargate","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hadoop","optional":false},{"name":"Java","optional":false},{"name":"Keras","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Rust","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Apache Kafka","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"cuDNN","optional":true},{"name":"Flink","optional":true}],"status":"live","first_seen_at":"2026-09-24T15:46:11Z","employer_posted_date":null,"last_verified_at":"2026-09-24T15:46:11Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Achieving our goals starts with supporting yours. Grow your career, access top-tier health and wellness benefits, build lasting connections with your team and our customers, and travel the world using our extensive route network.Come join us to create what’s next. Let’s define tomorrow, together.\nDescription\nJob overview and responsibilities\n\nDevelops and programs integrated software algorithms to structure, analyze and leverage data in systems applications. Develops and communicates statistical modeling techniques to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy. Completes programming and implements efficiencies, performs testing and debugging. Completes documentation and procedures for installation and maintenance. Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations. Can work with large scale computing frameworks, data analysis systems and modeling environments.\nDesign and implement key components of the Machine Learning Platform infrastructure and establish processes and best practices\nWork cross-functionally with data scientists, data engineers, and IT teams to design, develop, deploy, and integrate high-performance, production-grade machine learning solutions and data intensive workflows\nPartner with data scientists and data engineers to create and refine features from underlying data and build reproducible feature pipelines to train models and serve features in production\nPartner with data platform and operations teams to solve complex data ingestion, pipeline and governance problems for machine learning solutions\nTake ownership of production systems with a focus on delivery, continuous integration, and automation of machine learning workloads\nProvide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers\nQualifications\nWhat’s needed to succeed (Minimum Qualifications):\n\nBachelor’s degree in computer science, engineering, or a related technical discipline\n3+ years of experience in managing technical teams and projects\n3+ years of experience in full software lifecycle development using Python\n3+ years of experience leading an ML Ops team familiar with large cloud environments, Big Data technologies\n3+ years in software development in Python, Java, PySpark\n3+ Years of Experience with Machine Learning and Machine Learning workflows\n3+ years of experience designing and developing using technologies as Docker, Kubernetes\nStrong software engineering experience with Python and at least one additional language such as Java, Go, Rust, or C/C++\nUnderstanding of machine learning principles and techniques\nExperience with data science tools and frameworks (e.g. PyTorch, Tensorflow, Keras, Pandas, Numpy, Spark)\nExperience designing and developing scalable cloud native solutions using technologies such as Docker and Kubernetes and serverless services such as AWS Lambda, EKS, ECS, Fargate\nExperience building infrastructure-as-code templates (e.g. AWS CloudFormation) and cloud-native CI/CD pipelines using tools such as AWS CodePipeline\nExperience building ETL pipelines and working with big data technologies (e.g. Hadoop, Spark, and serverless technologies such as EMR, Redshift, S3, AWS Glue, and Kinesis)\nKnowledge of distributed systems as it pertains to compute and data storage\nStrong desire to experiment with and learn new technologies and stay aligned with the latest community developments in ML Ops/Engineering and cloud native\nExcellent oral and written communication skills. Ability to prepare high-quality presentation materials and explain complex concepts and technical materials to less-technical audiences\nMust be legally authorized to work in the United States for any employer without sponsorship\nSuccessful completion of interview required to meet job qualification\nReliable, punctual attendance is an essential function of the position\nWhat will help you propel from the pack (Preferred Qualifications):\n\nAWS Certified Solution Architect (Associate or Professional)\nExperience working as a Machine Learning Engineer or Data Scientist building and productional machine learning solutions\nExperience building real-time event-driven stream processing solutions with technologies such as Kafka, Flink, and Spark\nExperience with GPU acceleration (e.g. CUDA and CuDNN)\nExperience with Kubernetes\nThe base pay range for this role is $117,610.00 to $153,146.00.\nThe base salary range/hourly rate listed is dependent on job-related, factors such as experience, education, and skills. This position is also eligible for bonus and/or long-term incentive compensation awards.\nYou may be eligible for the following competitive benefits: medical, dental, vision, life, accident & disability, parental leave, employee assistance program, commuter, paid holidays, paid time off, 401(k) and flight privileges.\nUnited Airlines is an Equal Opportunity Employer. We recruit, employ, train, compensate, and promote without regard to race, color, religion, national origin, gender identity, sexual orientation, disability, age, veteran status, or any other protected category under applicable law. We provide reasonable accommodations for applicants and employees with disabilities. 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