ML Engineering Manager

Description

Job overview and responsibilities

The ML Engineering Manager is responsible to develop and program 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.

• 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 high-performance, 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 complex data ingestion, pipeline and governance problems for machine learning solutions
• Take ownership of production systems with a focus 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

Qualifications

What's needed to succeed (Minimum Qualifications):

• Bachelor's Degree in Computer Science, Engineering, or a related technical discipline
• 4-8 years of experience in managing technical teams and projects
• 4+ years of experience in full software lifecycle development using Python
• 4+ years in software development in Python, PySpark
• 4+ Years of Experience with Machine Learning and Machine Learning workflows
• Experience designing and developing using technologies as Docker, Kubernetes
• Hands-on experience leading an ML Generative AI
• 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 (e.g. PyTorch, Tensorflow, Keras, Pandas, Numpy, 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, Fargate
• Experience building infrastructure-as-code templates (e.g. AWS CloudFormation) and cloud-native CI/CD pipelines using tools such as AWS CodePipeline
• Experience 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)
• Knowledge of distributed systems as it pertains to compute and data storage
• Strong desire to experiment with and learn new technologies and stay aligned with the latest community developments in ML Ops/Engineering and cloud native
• Excellent oral and written communication skills
• 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 interview required to meet job qualification
• Reliable, punctual attendance is an essential function of the position

What will help you propel from the pack (Preferred Qualifications):

• 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 (e.g. CUDA and CuDNN)
• Experience with Kubernetes