Lead Applied Scientist (AI)

TL;DR

Lead Applied Scientist (AI): Driving advanced LLM research and model development for production-grade AI agents with an accent on the full model development lifecycle, from training to deployment. Focus on implementing reinforcement learning pipelines, optimizing model performance, and ensuring reliability in high-scale enterprise environments.

Location: Must be based in Palo Alto, California

Company

A global leader in CRM and enterprise cloud software, now pioneering production-grade AI agents through AgentForce.

What you will do

• Execute the full LLM development lifecycle including data preparation, training, fine-tuning, and deployment readiness.
• Lead research initiatives on LLM alignment and optimization using RLHF and RLAIF pipelines.
• Develop and maintain continuous learning systems utilizing real-world feedback signals.
• Serve as the technical point of contact for complex AI projects across research, engineering, and product teams.
• Mentor junior scientists and engineers through technical guidance and code-level reviews.

Requirements

• PhD in Computer Science, Machine Learning, AI, or a related field.
• Proven hands-on experience owning the full model development lifecycle for LLMs.
• Deep expertise in reinforcement learning, preference learning, or human-in-the-loop systems.
• Advanced proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Practical experience with Hugging Face, DeepSpeed, FSDP, Ray, and Kubernetes.

Nice to have

• Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
• Experience deploying and iterating on models in high-availability production systems.
• Background in enterprise AI, agentic systems, or LLM platforms at scale.
• Familiarity with trust, safety, or governance frameworks for AI systems.

Culture & Benefits

• Opportunity to work on mission-critical LLM systems at massive scale.
• Ownership of models end-to-end, from research ideation to production impact.
• Collaboration with world-class researchers and engineers.
• Influence over the future of enterprise-grade AI agents.