Lead Machine Learning Engineer

Join a foundation machine learning platform team at Salesforce to build and accelerate scalable, resilient ML pipelines across the security engineering organization. This onsite role in San Francisco, CA focuses on defining and executing a technical ML strategy, with end-to-end ownership of pipelines, MLOps, monitoring, and infrastructure-as-code. You will also help productize Generative AI and agentic workflows to strengthen security posture and resilience.

Salary: USD 172,500 - 285,800 per year. Experience: 6+ years. Education: Masters or PhD in a quantitative field.

What you’ll do

• Define and drive the technical ML strategy, emphasizing robust, performant model architectures and strong MLOps practices.
• Lead end-to-end ML pipeline development, including automated retraining workflows and model optimization for cost and performance.
• Own a portfolio of multiple machine learning pipelines spanning security and compliance use cases.
• Implement infrastructure-as-code and CI/CD pipelines, along with MLOps automation focused on model monitoring and drift detection.
• Design and deliver monitoring solutions for model performance, data quality, and system health.
• Collaborate with Data Science, Data Engineering, Research, and Product Management to deliver scalable ML solutions with measurable impact.
• Provide technical leadership by applying ML engineering best practices and mentoring junior machine learning engineers.

What you bring

• Masters or PhD in a quantitative field.
• 6+ years delivering end-to-end, multi-model machine learning systems from design and development through large-scale deployment.
• Deep practical experience with containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
• Strong Python expertise, including leading ML frameworks such as TensorFlow and PyTorch, with adherence to software engineering best practices.
• Proven success implementing comprehensive MLOps, including CI/CD, testing protocols, and model performance monitoring.
• Solid foundation in feature engineering and experience building with feature stores.
• Significant experience developing and deploying Generative AI solutions in production.
• Expertise in infrastructure-as-code, monitoring tools, and big data technologies such as Spark and Snowflake.
• Experience formulating ML governance policies and ensuring compliance with data security regulations.
• Demonstrated ability to lead ML initiatives with quantifiable business outcomes.
• Strong collaboration skills across Data Science, Platform Engineering, Research, and Product teams.

Technologies

Python, TensorFlow, PyTorch, Docker, Kubernetes, Apache Airflow, CI/CD, CI/CD pipelines, Spark, Snowflake, Generative AI, agentic workflows, feature stores, infrastructure-as-code, monitoring tools, ML governance policies

Benefits

• Time off programs
• Medical
• Dental
• Vision
• Mental health support
• Paid parental leave
• Life and disability insurance
• 401(k)
• Employee stock purchasing program

Preferred skills

• Expertise in advanced Natural Language Processing (NLP) methodologies.
• Experience conducting research or collaborating with ML research teams.
• Mentoring experience for junior engineers.
• Track record of publications and/or patents in quantitative disciplines.