Lead Machine Learning Engineer, Llm Infrastructure

Salesforce

Base: $172,500 - $260,100 annually (select cities:...
Not specified (assumed flexible based on industry standards)
Llm post-training infrastructure
Scalable pipelines
Python production systems
Salesforce is seeking a Lead Machine Learning Engineer for their AI Research Incubation Team, focusing on large language model (LLM) post-training infrastructure. The role emphasizes building scalable systems for training orchestration and model deployment while collaborating with cross-functional teams

Job Summary

  • You will own the infrastructure and engineering systems that support LLM post-training, large-scale evaluation, and model deployment.
  • This is a lead-level individual contributor role with deep ownership of model-facing infrastructure and strong cross-functional influence.
  • Own the systems that turn research models into production AI capabilities and work at the intersection of AI research and large-scale engineering systems.

Matching Summary

Match Score: 85

Salesforce is seeking a Lead Machine Learning Engineer for their AI Research Incubation Team, focusing on large language model (LLM) post-training infrastructure. The role emphasizes building scalable systems for training orchestration and model deployment while collaborating with cross-functional teams.

Salary

Base: $172,500 - $260,100 annually (select cities: $207,800 - $285,800 annually); Bonus/Equity: Not specified; Benefits: Not specified

Skills & Requirements

Must-have

  • LLM post-training infrastructure
  • scalable pipelines
  • Python production systems
  • distributed and GPU-based workloads
  • LLM post-training experience
  • cross-functional collaboration

Nice-to-have

  • AI research incubation teams
  • agent engineers
  • platform teams
  • iterative model improvement
  • AI CRM

Key Requirements

  • 5+ years of experience
  • Python proficiency
  • production systems or large-scale ML pipelines
  • model training, post-training, evaluation, or serving infrastructure
  • reliable, scalable systems for distributed and GPU-based workloads
  • LLM post-training infrastructure experience
  • cross-functional work with research scientists and engineers
  • cloud platforms (AWS, GCP)
  • containerized environments (Docker, Kubernetes)

Work Rights

Not specified

Tailored Resume

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