Lead Member of Technical Staff - Machine Learning Engineering

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

Job summary

A leading company is seeking a highly motivated lead machine learning engineer to define and execute technical ML strategies, focusing on ML pipelines, MLOps, and collaboration across teams. This position offers the opportunity to work on cutting-edge technology while mentoring junior engineers and driving impactful business outcomes.

Qualifications

• 6+ years in machine learning lifecycle from design to deployment.
• Experience in implementing CI/CD pipelines and testing protocols.
• Deep understanding of containerization and workflow orchestration.

Responsibilities

• Define and drive the technical ML strategy.
• Lead development of ML pipelines focusing on automated workflows.
• Collaborate with cross-functional teams for scalable ML solutions.

Skills

Python programming

Machine Learning technologies

MLOps

Data quality monitoring

Natural Language Processing

Education

Masters or PhD in a quantitative field

Tools

Docker

Kubernetes

Apache Airflow

TensorFlow

PyTorch

Job description

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

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.

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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.