Overview
On Site
USD 184,000.00 - 253,000.00 per year
Full Time
Skills
Artificial Intelligence
Customer Relationship Management (CRM)
Blaze
Value Engineering
Management
Optimization
Regulatory Compliance
FOCUS
Data Quality
Data Engineering
Product Management
IT Management
Docker
Workflow
Orchestration
Kubernetes
Apache Airflow
Python
TensorFlow
PyTorch
Software Engineering
Machine Learning Operations (ML Ops)
Continuous Integration
Continuous Delivery
Testing
Performance Monitoring
Generative Artificial Intelligence (AI)
Big Data
Apache Spark
Snow Flake Schema
Data Security
Collaboration
Data Science
Natural Language Processing
Machine Learning (ML)
Research
Mentorship
Publications
Patents
MEAN Stack
SAP BASIS
Law
Promotions
Training
LOS
Salesforce.com
Recruiting
Job Details
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.
Accommodations
If you require assistance due to a disability applying for open positions 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. 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:
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
If you require assistance due to a disability applying for open positions 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. 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:
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.