Lead Machine Learning Engineer

Salesforce

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Francisco, CABellevue, WA
Salary: $172,500–$260,100 / yr
Employment: Full-time
Posted: 14 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $226k

This role $216k

$160k $290k

below market most similar roles pay here above market

This role pays less than 55% of similar roles. Most pay $198,000–$254,750 — the blue band above. At the midpoint, this role pays about $216k versus about $226k for comparable roles.

Based on 240 similar postings.

Employer

About Salesforce

Salesforce is the world''s leading customer relationship management (CRM) platform, offering cloud-based software for sales, service, marketing, analytics, and application development. Industry: Enterprise Software & Cloud Computing

Salesforce currently has 221 open roles on FindRole.

Listed pay typically runs $148,500–$260,100 across 125 roles with salary data.

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At a glance

TL;DR · Lead Machine Learning Engineer

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JOB TITLE: Lead Machine Learning Engineer The Lead Machine Learning Engineer joins the foundation machine learning platform team within the Trust Intelligence Platform organization. This role involves defining and executing the technical ML strategy to build and accelerate scalable, resilient machine learning pipelines across the security engineering organization. You will manage a portfolio of pipelines, focusing on full lifecycle development, MLOps, infrastructure-as-code, and CI/CD. Key responsibilities include productizing Generative AI solutions and agentic workflows to enhance security posture. You will utilize Python, TensorFlow, PyTorch, Docker, Kubernetes, Apache Airflow, Spark, and Snowflake. The role requires expertise in model monitoring, drift detection, and feature engineering. You will collaborate with cross-functional teams to deliver measurable business impact within a security and compliance framework while mentoring junior engineers on best practices.

Skills

Machine Learning MLOps Generative AI Python PyTorch TensorFlow Kubernetes Docker Apache Airflow CI/CD Infrastructure-as-Code Spark Snowflake NLP Feature Engineering Model Monitoring

What you'll do

Define and execute the technical machine learning strategy for the Trust Intelligence Platform.
Lead the end-to-end development and optimization of scalable machine learning pipelines.
Productize Generative AI solutions and agentic workflows to enhance organizational security.
Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation for model deployment.
Design comprehensive monitoring solutions for model performance, data quality, and system health.
Manage a portfolio of machine learning pipelines within security and compliance frameworks.
Provide technical leadership and mentor junior machine learning engineers on best practices.
Collaborate with cross-functional teams to deliver scalable ML solutions with measurable business impact.

What we're looking for

Masters or PhD in a quantitative field.
6+ years of experience in the end-to-end lifecycle of multi-model machine learning systems.
Mastery of Python and leading ML frameworks like TensorFlow or PyTorch.
Deep understanding of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow).
Demonstrated success in implementing MLOps methodologies, CI/CD pipelines, and model performance monitoring.
Significant experience developing and deploying generative AI solutions into production environments.
Expertise in infrastructure-as-code, monitoring tools, and big data technologies like Spark and Snowflake.
Expertise in advanced NLP methodologies (preferred).

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