Machine Learning Engineering Manager (ML Ops)

Location: Chicago, Illinois, United States, or Arlington Heights, Illinois, United States

Base pay range: $117,610–$153,146 per year; bonus and/or long-term incentive compensation may also apply.

Company

hirify.global operates an airline network and provides passenger air travel services.

What you will do

• Design and implement components of the machine learning platform infrastructure, including processes and engineering best practices.
• Work with data scientists, data engineers, and IT teams to develop, deploy, and integrate production-grade machine learning solutions and data-intensive workflows.
• Build reproducible feature pipelines and support model training and feature serving in production.
• Solve complex data ingestion, pipeline, and governance problems with data platform and operations teams.
• Own production systems, focusing on delivery, continuous integration, and automation of machine learning workloads.
• Provide technical mentorship, guidance, and quality-focused code reviews to data scientists and ML engineers.

Requirements

• Bachelor’s degree in computer science, engineering, or a related technical discipline.
• 3+ years of experience managing technical teams and projects, including 3+ years leading an ML Ops team familiar with large cloud environments and Big Data technologies.
• 3+ years of full software lifecycle development using Python and 3+ years of software development with Python, Java, and PySpark; strong Python engineering experience plus at least one additional language such as Java, Go, Rust, or C/C++.
• 3+ years of experience with machine learning and machine learning workflows.
• 3+ years of experience designing and developing with Docker and Kubernetes.
• Understanding of machine learning principles and techniques; experience with data science tools and frameworks such as PyTorch, TensorFlow, Keras, Pandas, NumPy, and Spark.
• Experience designing scalable cloud-native solutions using Docker, Kubernetes, and serverless services such as AWS Lambda, EKS, ECS, and Fargate.
• Experience building infrastructure-as-code templates such as AWS CloudFormation and cloud-native CI/CD pipelines such as AWS CodePipeline.
• Experience building ETL pipelines and working with Big Data technologies such as Hadoop, Spark, EMR, Redshift, S3, AWS Glue, and Kinesis; knowledge of distributed systems for compute and data storage.
• Strong desire to experiment with and learn new technologies, excellent oral and written communication skills, and the ability to explain complex technical concepts to less-technical audiences.
• Must be legally authorized to work in the United States for any employer without sponsorship.
• Successful completion of an interview and reliable, punctual attendance are required.

Nice to have

• AWS Certified Solutions Architect certification at the Associate or Professional level.
• Experience as a Machine Learning Engineer or Data Scientist building and productionizing machine learning solutions.
• Experience building real-time event-driven stream-processing solutions with Kafka, Flink, and Spark.
• Experience with GPU acceleration such as CUDA and cuDNN.
• Additional Kubernetes experience.

Culture & Benefits

• Medical, dental, vision, life, accident and disability coverage.
• Parental leave, employee assistance program, commuter benefits, paid holidays, and paid time off.
• 401(k) and flight privileges.
• Additional bonus and/or long-term incentive compensation may be available.