Lead Machine Learning Engineer, LLM Infrastructure

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.

About the Role
We are seeking a Lead ML Engineer, LLM Post-Training Infrastructure to join the Salesforce AI Research Incubation Team. In this role, you will own the infrastructure and engineering systems that support LLM post-training, large-scale evaluation, and model deployment. You will build scalable, reliable
pipelines for training orchestration, rollout generation, reward and feedback pipelines, experiment management, and model iteration, helping translate research ideas into production-grade systems.
This is an engineering-first role focused on ML infrastructure, distributed systems, and training/evaluation workflows rather than developing new model architectures or algorithms. You will work closely with research scientists, agent engineers, and platform teams to operationalize post-training and feedback-driven learning methods into robust, reusable systems.This is a lead-level individual contributor role with deep ownership of model-facing infrastructure and strong cross-functional influence.
Key Responsibilities:
● Design, build, and maintain infrastructure for LLM post-training, evaluation, and
deployment.
● Own scalable pipelines for training orchestration, rollout generation, reward and
feedback processing, checkpointing, and experiment management.
● Build reliable systems for feedback-driven model improvement, including human or AI
feedback loops, large-scale offline evaluation, and regression detection.
● Partner closely with research scientists to turn new post-training methods into reusable
engineering workflows.
● Collaborate with agent engineers and platform teams to integrate training and evaluation
systems with production model and agent stacks.
● Optimize distributed training and inference workloads for reliability, throughput, cost
efficiency, and observability.
● Drive best practices for reproducibility, versioning, monitoring, deployment, and
operational excellence across ML systems.
Required Qualifications:
● 5+ years of experience in software engineering, ML systems, or distributed
infrastructure.
● Strong proficiency in Python and experience building production systems or large-scale
ML pipelines.
● Hands-on experience building infrastructure for model training, post-training, evaluation,
or serving.
● Experience designing reliable, scalable systems for distributed and GPU-based
workloads.
● Strong debugging skills across systems, pipelines, and model-facing failures.
● Experience building infrastructure for LLM post-training, including RLHF, preference
optimization, reward modeling, or related feedback-driven training workflows.
● Experience working cross-functionally with research scientists and engineers.
● Familiarity with cloud platforms (AWS, GCP) and containerized environments (Docker,
Kubernetes).
Preferred Qualifications:
● Experience with rollout systems, large-scale evaluation loops, or training data/feedback
pipelines.
● Familiarity with distributed training frameworks and modern ML infrastructure stacks.
● Experience supporting agent-based learning, simulation environments, or iterative model
improvement systems.
● Prior experience working closely with AI research or incubation teams.
Why Join Us?
● Own the systems that turn research models into production AI capabilities.
● Work at the intersection of AI research and large-scale engineering systems.
● Shape how models are trained, deployed, evaluated, and evolved.
● Competitive compensation, benefits, and strong long-term growth opportunities.
 

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: https://www.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.