Lead Machine Learning Engineer

1 week ago


San Francisco, California, United States salesforce, inc. Full time
About the Role

We are seeking a highly skilled and experienced Lead Machine Learning Engineer to join our Marketing AI/ML Algorithms and Applications team within Salesforce's Marketing organization.

In this critical role, you will collaborate closely with Data Science, Data Engineering, Product, and Marketing teams to lead the design, implementation, and operations of end-to-end ML solutions at scale.

Key Responsibilities
  1. Technical Leadership: Define and drive the overall technical ML engineering strategy for the organization.
  2. ML Model Development: Lead the design, development, and deployment of ML model pipelines, in collaboration with Data Science and Data Engineering teams.
  3. Infrastructure and Operations: Implement infrastructure-as-code, CI/CD, monitoring, and automation to ensure reliable and efficient ML operations.
  4. MLOps Lifecycle: Own the MLOps lifecycle, including establishing best practices, overseeing model deployment processes, and handling occasional on-call support and incident response for production ML systems.
  5. Business Alignment: Collaborate closely with Product and Marketing teams to understand business requirements, provide technical feasibility input, and ensure ML solutions are aligned with product roadmaps and deliver measurable business impact.
  6. Team Leadership: Provide technical leadership to develop a team of ML Engineers, and mentor and guide junior ML Engineers to build a high-performing team.
Requirements
  1. Education: MS or PhD in Computer Science, AI/ML, Software Engineering, or related field.
  2. Experience: 8+ years of experience building and deploying ML model pipelines at scale.
  3. Technical Skills: Expert-level knowledge of containerization, orchestration, and workflow management technologies (e.g., Kubernetes, Docker, Apache Airflow). Advanced Python skills, including expertise in ML-specific libraries and best practices. Extensive experience with cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, Google Cloud AI). Proven track record in implementing MLOps best practices, including CI/CD, automated testing, and code quality assurance.
  4. Soft Skills: Strong communication skills, including the ability to explain complex technical concepts to diverse audiences and engage effectively with executive stakeholders. Strong problem-solving skills and ability to translate technical solutions into business value.


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