Lead Machine Learning Engineer, LLM Infrastructure

Salesforce•Palo Alto, CA

1d•Hybrid

About The Position

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.

Requirements

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).

Nice To Haves

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.

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.