Description

The AgentForce Data Science team powers the core Large Language Models (LLMs) behind Salesforce’s production-grade AI agents. Our work directly impacts millions of users by enabling trustworthy, scalable, and high-performance AI systems across customer support, sales, marketing, analytics, and internal productivity workflows.

We operate at the intersection of cutting-edge research and real-world deployment, owning the full model development lifecycle—from research ideation and training to fine-tuning, evaluation, continuous learning, and production rollout.

Role Overview

We are seeking a strong Lead/Principal Applied Scientist to drive advanced LLM research and model development for AgentForce’s production services. This role requires hands-on involvement across the full model development lifecycle, in addition to technical leadership and mentorship.

The ideal candidate is both a strong individual contributor and a technical leader, serving as a primary point of contact (POC) for major AI initiatives while shaping long-term research and modeling strategy.

Key Responsibilities

Research, Modeling & Hands-On Execution

• 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.

Technical Leadership

• 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.

Mentorship & Thought Leadership

• 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.

Required Qualifications

Education & Research Background

• 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. [Nice to have]

Core Technical & Hands-On Requirements

• 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.

Coding & Tooling

• 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).

Leadership & Collaboration

• Experience mentoring and developing junior researchers or engineers.

• Strong communication skills across research, engineering, and executive stakeholders

Preferred Qualifications

• 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).

Why Join AgentForce?

• Work on mission-critical LLM systems at massive scale.

• Own models end-to-end, from research to production impact.

• Shape the future of enterprise-grade AI agents.

• Collaborate with world-class researchers and engineers.

• See your research ship, scale, and matter.

For roles in San Francisco and Los Angeles: 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.