Senior Machine Learning Engineer

Job Summary

SAE is seeking an experienced Senior Machine Learning Engineer to join our rapidly growing Enterprise Data team. The ideal candidate will have a strong background in machine learning, MLOps, and data engineering, with a passion for leading teams and collaborating with data scientists to develop innovative solutions. The Senior Machine Learning Engineer will be responsible for creating, maintaining, and improving our ML pipeline and establishing MLOps practices within our data science group. Experience with Neo4j and working with graph databases is essential for this role.

Essential Functions

• Lead the design, development, and deployment of machine learning models and solutions, ensuring they meet business objectives and performance requirements.
• Establish and maintain a robust ML pipeline, including data ingestion, feature engineering, model training, evaluation, and deployment.
• Implement MLOps practices to streamline the ML lifecycle, including version control, testing, continuous integration, and continuous deployment.
• Collaborate closely with data scientists, data engineers, and other stakeholders to understand requirements, provide technical guidance, and ensure successful delivery of machine learning solutions.
• Stay current with the latest advancements in machine learning, MLOps, and data engineering, and drive the adoption of new technologies and best practices within the team.
• Develop and maintain documentation related to machine learning models, pipelines, and processes.
• Provide mentorship, guidance, and support to junior team members, fostering a collaborative and inclusive team culture.

Minimum Requirements

Bachelor's or Master's degree in Computer Science, Data Science, or a related field. At least 5 years of experience as a Machine Learning Engineer, with a proven track record of leading teams and working with data science groups. Strong experience with MLFlow, Databricks, and other machine learning platforms. Deep understanding of machine learning algorithms, data structures, and optimization techniques. Proficiency in Python and experience with ML libraries such as TensorFlow, PyTorch, or Scikit-learn. Familiarity with MLOps tools and practices, including version control (e.g., Git), CI/CD, and containerization (e.g., Docker, Kubernetes). Extensive experience working with Neo4j and other graph databases, along with a solid understanding of graph algorithms and data modeling.

Working Conditions And Physical Requirements

Ability to work for long periods at a computer/deskStandard office environment

About The Organization

Fullsight is an integrated brand of our three primary affiliate companies – SAE Industry Technologies Consortia, SAE International and Performance Review Institute – and their subsidiaries. As a collective, Fullsight enables a robust resource of innovative programs, products and services for industries, their engineers and technical experts to work together on traditional and emergent complex issues that drive their future progress.

SAE Industry Technologies Consortia® (SAE ITC) enables organizations to define and pilot best practices. SAE ITC industry stakeholders are able to work together to effectively solve common problems, achieve mutual benefit for industry, and create business value.

The Performance Review Institute® (PRI) is the world leader in facilitating collaborative supply chain oversight programs, quality management systems approvals, and professional development in industries where safety and quality are shared values.

SAE International® (SAEI) is a global organization serving the mobility sector, predominantly in the aerospace, automotive and commercial-vehicle industries, fostering innovation, and enabling engineering professionals. Since 1905, SAE has harnessed the collective wisdom of engineers around the world to create industry-enabling standards. Likewise, SAE members have advanced their knowledge and understanding of mobility engineering through our information resources, professional development, and networking.