About the role

Lead/Principal Applied Scientist driving advanced LLM research and model development for Salesforce AI. Working on cutting-edge projects impacting millions of users in customer support, sales, and analytics.

Responsibilities

Own and execute hands-on work across the full model development lifecycle , including data preparation, model training, fine-tuning, evaluation, iteration, and deployment readiness.
Lead end-to-end research initiatives on LLM training, fine-tuning, alignment, and optimization for production use cases.
Design, implement, and iterate on reinforcement learning (RL) and continuous learning pipelines (e.g., RLHF, RLAIF, offline/online feedback loops).
Conduct rigorous experimentation, ablation studies, and failure analysis to drive measurable model improvements.
Translate research prototypes into production-grade models that meet latency, scalability, reliability, and safety requirements.
Serve as the technical POC for complex AgentForce AI projects, driving alignment across research, engineering, product, and platform teams.
Define best practices for model training, fine-tuning, evaluation, and release readiness.
Influence architectural and modeling decisions across the AgentForce AI stack.
Mentor junior scientists and engineers through direct technical guidance and code-level reviews.
Foster a culture of strong scientific rigor, reproducibility, and ownership.
Contribute to Salesforce’s external research presence through publications, talks, and collaborations .

PhD in Computer Science, Machine Learning, AI, or a related field
Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact.
Demonstrated hands-on experience owning the full model development lifecycle , not limited to research or design.
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 , with significant hands-on coding experience.
Deep experience with PyTorch, TensorFlow or similar deep learning packages.
Practical experience with modern LLM tooling, such as: Hugging Face (Transformers, Accelerate, PEFT)
Distributed training frameworks (DeepSpeed, FSDP, etc.)
ML orchestration and scaling tools (Ray, Kubernetes, internal platforms)
Strong data analysis and experimentation skills (NumPy, Pandas, custom evaluation pipelines).