Lead Machine Learning Engineer

About the role

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category Software Engineering

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

The Experience

We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization.

We are seeking a highly motivated, hands-on lead machine learning engineer with strong business understanding to define and execute the technical ML strategy. This role involves full lifecycle development and optimization of ML pipelines, with a strong focus on MLOps, infrastructure-as-code, CI/CD, and thorough monitoring. The lead will manage multiple ML pipelines, work closely with cross-functional teams, mentor others, and requires deep expertise in various ML technologies to deliver measurable business impact within a security and compliance framework. A critical component of this role is the proven ability to productize Generative AI solutions and agentic workflows to actively enhance the security posture and resilience for the organization and all Salesforce customers.

What You'll Actually Be Doing

- Define and drive the technical ML strategy, emphasizing robust and performant model architectures and MLOps practices.

- Lead the end-to-end development of ML pipelines, focusing on automated retraining workflows and model optimization for cost and performance.

- Own a portfolio of multiple machine learning pipelines within security and compliance.

- Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation with a focus on model monitoring and drift detection.

- Design and implement comprehensive monitoring solutions for model performance, data quality, and system health.

- Collaborate with Data Science, Data Engineering, Research, and Product Management teams to deliver scalable ML solutions with measurable impact.

- Provide technical leadership in ML engineering best practices and mentor junior machine learning engineers.

You're Our Person If...

- Masters or PhD in a quantitative field

- Extensive experience (6+ years) in the end-to-end lifecycle of multi-model machine learning systems, from design and development to large-scale deployment.