Salesforce
Lead Machine Learning Engineer
$172K–$286K
California - San Francisco +1 · Office Tech-Flexible · Full-time · Posted Oct 13, 2026
- Location
- California - San Francisco
Washington - Bellevue - Experience
- 6+ years in the end-to-end lifecycle of multi-model machine learning systems.
- Education
- Master’s or PhD in a quantitative field.
About the job
Leads the technical strategy and lifecycle delivery of scalable machine learning pipelines for security and compliance, including production generative AI and agentic workflows.
What you'll do
- Own and optimize multiple ML pipelines, including automated retraining, model performance, and cost.
- Build MLOps automation, infrastructure-as-code, CI/CD, monitoring, and drift-detection capabilities.
- Collaborate across technical and product teams, set engineering practices, and mentor engineers.
What they ask for
- Master’s or PhD in a quantitative field.
- 6+ years of experience across the lifecycle of multi-model ML systems, from design and development through large-scale deployment.
- Practical experience with Docker, Kubernetes, Apache Airflow, Python, TensorFlow, and PyTorch.
- Experience delivering production generative AI solutions and implementing MLOps, monitoring, and infrastructure-as-code.
Nice to have
- Expertise in advanced NLP; research experience or collaboration with ML research teams.
- Mentoring experience; publications and/or patents in quantitative disciplines.
Pay
$172,500 - $260,100 annually (base salary); $207,800 - $285,800 annually (base salary)
Tools
Docker, Kubernetes, Apache Airflow, Python, TensorFlow, PyTorch, Spark, Snowflake
AI focus
Productize generative AI solutions and agentic workflows to improve security posture and resilience; develop and operate ML systems and pipelines.
Model-written summary · the original posting is authoritative