Lead Machine Learning Engineer

100 Salesforce, Inc.; San Francisco (CA); On-site; USD 173,000 - 260,000; Full time

Benefits offered by this job

Time off programs

Medical insurance

Dental insurance

Vision care

Mental health support

Paid parental leave

Life and disability insurance

401(k)

Employee stock purchasing program

Job summary

Salesforce, the Customer Company, seeks a hands-on Lead Machine Learning Engineer to define and execute the technical ML strategy within the Trust Intelligence Platform. You will own multiple ML pipelines, emphasize MLOps, CI/CD, and model monitoring, and mentor engineers while collaborating with cross‑functional teams.

The role focuses on productionizing Generative AI solutions, cost-efficient model optimization, and implementing infrastructure‑as‑code across security and compliance domains.

Qualifications

• Masters or PhD in a quantitative field.
• 6+ years end-to-end lifecycle of multi-model ML systems.
• Deep understanding of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow).
• Mastery of Python and ML frameworks (TensorFlow, PyTorch).
• Experience implementing MLOps, CI/CD pipelines, testing and model monitoring.
• Foundation in feature engineering and feature stores.
• Experience deploying generative AI solutions.
• Proficiency with infrastructure-as-code, monitoring tools, and big data tech (Spark, Snowflake).
• Experience with ML governance and data security compliance.
• Track record delivering measurable business outcomes across teams.

Responsibilities

• Define and drive the technical ML strategy with robust model architectures and MLOps practices.
• Lead end-to-end development of ML pipelines, including automated retraining and cost/performance optimization.
• Own a portfolio of ML pipelines within security and compliance domains.
• Implement infrastructure-as-code, CI/CD, and MLOps automation with monitoring and drift detection.
• Collaborate with Data Science, Data Engineering, Research and Product Management to deliver scalable ML solutions; mentor juniors.

Skills

Docker

Kubernetes

Apache Airflow

Python

TensorFlow

PyTorch

MLOps

CI/CD

Monitoring

Feature stores

Spark

Snowflake

Generative AI

Security/Compliance

Education

Masters or PhD in a quantitative field

Tools

Docker

Kubernetes

Apache Airflow

Spark

Snowflake

TensorFlow

PyTorch

Job description

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.

Lead member of technical staff - Machine Learning Engineering

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 publications and/or patents in quantitative disciplines.

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. It means that at Salesforce, we believe in equality for all. We lead the path to equality by creating a workplace that’s inclusive and free from discrimination. Workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education. In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits.

Benefits

• time off programs
• medical
• dental
• vision
• mental health support
• paid parental leave
• life and disability insurance
• 401(k)
• employee stock purchasing program

Salary and Compensation

The typical base salary range for this position is $172,500 - $260,100 annually.

In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.

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.