Machine Learning Engineering Manager
About The Position
Develops 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 that improve product and system performance, quality, data management, and accuracy. Completes programming, implements efficiencies, performs testing and debugging, and documents procedures for installation and maintenance. Applies deep learning technologies and works with large-scale computing frameworks, data analysis systems, and modeling environments. The role designs and implements key components of the Machine Learning Platform infrastructure and establishes processes and best practices. It works cross-functionally with data scientists, data engineers, and IT teams to build, deploy, and integrate high-performance, production-grade machine learning solutions and data-intensive workflows. It partners with data and platform teams to develop reproducible feature pipelines, address data ingestion, pipeline, and governance challenges, and take ownership of production systems with a focus on delivery, continuous integration, and automation. The manager also provides technical mentorship and quality-focused code review to data scientists and ML engineers.
Requirements
- Bachelor’s degree in computer science, engineering, or a related technical discipline.
- At least 3 years of experience managing technical teams and projects.
- At least 3 years of experience in full software lifecycle development using Python.
- At least 3 years of experience leading an ML Ops team familiar with large cloud environments and Big Data technologies.
- At least 3 years of software development experience with Python, Java, and PySpark.
- At least 3 years of experience with machine learning and machine learning workflows.
- At least 3 years of experience designing and developing with technologies such as Docker and Kubernetes.
- Strong software engineering experience with Python and at least one additional language such as Java, Go, Rust, or C/C++.
- 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 and developing scalable cloud-native solutions using technologies such as Docker and 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 using tools such as AWS CodePipeline.
- Experience building ETL pipelines and working with big data technologies such as Hadoop and Spark, and serverless technologies such as EMR, Redshift, S3, AWS Glue, and Kinesis.
- Knowledge of distributed systems as they relate to compute and data storage.
- Desire to experiment with and learn new technologies and stay aligned with community developments in ML Ops/Engineering and cloud native.
- Excellent oral and written communication skills, including the ability to prepare high-quality presentation materials and explain complex concepts and technical materials to less-technical audiences.
- Must be legally authorized to work in the United States for any employer without sponsorship.
- Successful completion of an interview is required to meet job qualification.
- Reliable, punctual attendance is an essential function of the position.
Nice To Haves
- AWS Certified Solution Architect (Associate or Professional).
- Experience working as a Machine Learning Engineer or Data Scientist building and productionalizing machine learning solutions.
- Experience building real-time event-driven stream processing solutions with technologies such as Kafka, Flink, and Spark.
- Experience with GPU acceleration, such as CUDA and CuDNN.
- Experience with Kubernetes.
Responsibilities
- Develop and program integrated software algorithms to structure, analyze, and leverage data in systems applications.
- Develop and communicate statistical modeling techniques to develop and evaluate algorithms that improve product and system performance, quality, data management, and accuracy.
- Complete programming, implement efficiencies, perform testing and debugging, and document installation and maintenance procedures.
- Design and implement key components of the Machine Learning Platform infrastructure and establish processes and best practices.
- Work cross-functionally with data scientists, data engineers, and IT teams to design, develop, deploy, and integrate production-grade machine learning solutions and data-intensive workflows.
- Partner 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.
- Partner with data platform and operations teams to solve data ingestion, pipeline, and governance problems for machine learning solutions.
- Take ownership of production systems, focusing on delivery, continuous integration, and automation of machine learning workloads.
- Provide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers.
Benefits
- Medical, dental, and vision benefits.
- Life, accident, and disability benefits.
- Parental leave.
- Employee assistance program.
- Commuter benefits.
- Paid holidays and paid time off.
- 401(k).
- Flight privileges.
- Eligible for bonus and/or long-term incentive compensation awards.
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What This Job Offers
Job Type
Full-time
Career Level
Manager
