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 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. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Lead member of technical staff - Machine Learning Engineering 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 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. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world. Accommodations If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form . Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options. 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. 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. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: salesforcebenefits.com.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. At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. 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.

Requirements

Must-haves

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
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.
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.

Nice-to-haves

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

Application prep

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Keywords to mirror

aimachine learningmlci/cddockerkubernetespython

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave