Lead Machine Learning Engineer, LLM Infrastructure

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Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together.

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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.

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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.

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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.

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.

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