Associate Principal Scientist supporting regulatory strategies for Vaccine & Infectious Disease products. Collaborating globally and assisting submissions for regulatory compliance in the pharmaceutical industry.
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 .
Requirements
- 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).
Benefits
- time off programs
- medical
- dental
- vision
- mental health support
- paid parental leave
- life and disability insurance
- 401(k)
- employee stock purchasing program
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