Software Engineering Lmts

Salesforce Salesforce · Enterprise · Palo Alto, CA

Lead Applied Scientist role focused on powering Salesforce's production-grade AI agents with core LLMs. The role involves hands-on work across the full model development lifecycle, including research, training, fine-tuning, evaluation, and deployment readiness, with a strong emphasis on reinforcement learning and continuous learning pipelines. Requires technical leadership, mentorship, and a PhD in a related field. The role operates at the intersection of research and deployment, aiming to translate prototypes into production-grade models.

What you'd actually do

  1. Own and execute hands-on work across the full model development lifecycle, including data preparation, model training, fine-tuning, evaluation, iteration, and deployment readiness.
  2. Lead end-to-end research initiatives on LLM training, fine-tuning, alignment, and optimization for production use cases.
  3. Design, implement, and iterate on reinforcement learning (RL) and continuous learning pipelines (e.g., RLHF, RLAIF, offline/online feedback loops).
  4. Conduct rigorous experimentation, ablation studies, and failure analysis to drive measurable model improvements.
  5. Translate research prototypes into production-grade models that meet latency, scalability, reliability, and safety requirements.

Skills

Required

  • PhD in Computer Science, Machine Learning, AI, or a related field
  • Demonstrated hands-on experience owning the full model development lifecycle
  • Deep expertise in large-scale model training and fine-tuning, especially for LLMs
  • Strong background in reinforcement learning, preference learning, or human-in-the-loop learning
  • Experience building and maintaining continuous learning systems using real-world feedback signals
  • Solid understanding of model evaluation, alignment, and robustness in production environments
  • Advanced proficiency in Python
  • Deep experience with PyTorch, TensorFlow or similar deep learning packages
  • Practical experience with modern LLM tooling (Hugging Face, Distributed training frameworks, ML orchestration and scaling tools)
  • Strong data analysis and experimentation skills

Nice to have

  • Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact
  • Experience deploying and iterating on models in production, high-availability systems
  • Background in enterprise AI, agentic systems, or LLM platforms at scale
  • Familiarity with trust, safety, or governance frameworks for AI systems
  • Experience with large-scale distributed compute environments (multi-GPU / multi-node training)

What the JD emphasized

  • full model development lifecycle
  • LLM training
  • fine-tuning
  • alignment
  • optimization
  • reinforcement learning
  • continuous learning pipelines
  • production-grade models
  • technical POC
  • publications

Other signals

  • LLM training
  • fine-tuning
  • alignment
  • optimization
  • reinforcement learning
  • continuous learning pipelines
  • production rollout
  • agentic systems