Lead Member of Technical Staff - Machine Learning Engineering

Salesforce; San Francisco (CA); On-site; USD 200,000 - 277,000; Full time

Job summary

A leading company in CRM seeks a highly motivated Lead Member of Technical Staff for Machine Learning Engineering in San Francisco. The role emphasizes the end-to-end development of ML pipelines, focusing on MLOps practices and deep collaboration across various teams. Ideal candidates possess a Master's or PhD in a quantitative field and extensive experience in ML technologies, driving significant business outcomes. Exceptional mentorship abilities and technical leadership are essential for success in this position.

Qualifications

• 6+ years experience in multi-model ML systems.
• Expertise in CI/CD, testing protocols, and model monitoring.
• Successful track record in generative AI solutions.

Responsibilities

• Define and drive technical ML strategy focused on model architectures.
• Lead end-to-end development of ML pipelines with automated workflows.
• Collaborate with cross-functional teams to achieve measurable impact.

Skills

Machine Learning

MLOps

Python

Docker

Kubernetes

Apache Airflow

Deep Learning

Data Quality

Education

Masters or PhD in a quantitative field

Tools

TensorFlow

PyTorch

Spark

Snowflake

Job description

Lead Member of Technical Staff - Machine Learning Engineering

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Lead Member of Technical Staff - Machine Learning Engineering

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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 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.

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.

Require 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.

Accommodations

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Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: 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.

Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.

For Washington-based roles, the base salary hiring range for this position is $184,000 to $253,000.

For California-based roles, the base salary hiring range for this position is $200,800 to $276,100.

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, benefits. More details about our company benefits can be found at the following link: https://www.salesforcebenefits.com.

Seniority level

• Seniority level

  Not Applicable

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development, IT Services and IT Consulting, and Technology, Information and Internet

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