Lead Machine Learning Engineer

Salesforce; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Salesforce seeks a Lead member of technical staff in ML Engineering to define and drive the ML strategy, focusing on scalable ML pipelines, MLOps, and generative AI capabilities within a security/compliance framework.

You will lead end-to-end development, mentor engineers, and collaborate with cross-functional teams to deliver measurable business impact for Salesforce customers.

Qualifications

• 6+ years in the end-to-end lifecycle of multi-model ML systems.
• Deep understanding of containerization (Docker) and orchestration (Kubernetes, Airflow).
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch).
• Experience implementing MLOps including CI/CD, testing, monitoring.

Responsibilities

• Define and drive the ML strategy with robust model architectures and MLOps practices.
• Lead end-to-end ML pipeline development with automated retraining workflows.
• Own a portfolio of ML pipelines within security and compliance.
• Implement IaC, CI/CD, and monitoring for model performance and drift.
• Collaborate with DS, Data Eng, Research, and PM teams to deliver scalable ML solutions.
• Provide technical leadership and mentor junior engineers.

Skills

Python programming

MLOps

CI/CD pipelines

Model monitoring

Docker

Kubernetes

Apache Airflow

TensorFlow

PyTorch

Feature stores

Big data

Data governance

Education

Masters or PhD in a quantitative field

Tools

Docker

Kubernetes

Airflow

Spark

Snowflake

Job description

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.

Lead member of technical staff - Machine Learning Engineering

Job Category: Software Engineering

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too - driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good - you’ve come to the right place.

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.

Your impact:

• 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.

Required skills:

• 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.
• Deep understanding and practical application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
• Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
• Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.
• Solid foundation in feature engineering techniques and the implementation of feature stores.
• Significant experience in developing and deploying generative AI solutions into production environments.
• Expertise in infrastructure-as-code principles, monitoring tools, and big data technologies (Spark, Snowflake).
• Experience in formulating ML governance policies and ensuring adherence to data security regulations.
• Successfully led machine learning initiatives, consistently delivering significant and quantifiable business outcomes.
• Exceptional collaboration abilities, with a strong capacity to work effectively across Data Science, Platform Engineering, Research and Product teams.

Preferred skills:

• Expertise in advanced Natural Language Processing (NLP) methodologies.
• Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
• Previous experience in a mentoring role for junior engineers.
• Track record of publicatio