Software Engineering LMTS100 Salesforce, Inc. • Palo Alto, California, United States
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Software Engineering LMTS
100 Salesforce, Inc.
- Palo Alto, California, United States
- Palo Alto, California, United States
À propos
The AgentForce Data Science team powers the core Large Language Models (LLMs) behind Salesforce’s production‑grade AI agents. This role directly impacts millions of users, enabling trustworthy, scalable, and high‑performance AI systems across customer support, sales, marketing, analytics, and internal productivity workflows. It involves full lifecycle ownership from research ideation and training to fine‑tuning, evaluation, continuous learning, and production rollout. Key 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 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 point of contact 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. Required Qualifications
Education & Research Background
PhD in Computer Science, Machine Learning, AI, or related field. Strong publication record in top‑tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact. [Nice to have] additional advanced research experience.
Core Technical & Hands‑On Requirements
Demonstrated hands‑on experience owning the full model development lifecycle. Deep expertise in large‑scale model training and fine‑tuning 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 (Hugging Face, Accelerate, PEFT). Experience with distributed training frameworks (e.g., DeepSpeed, FSDP). Experience with 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). Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via the Accommodations Request Form. Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. The policy applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between.
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Compétences linguistiques
- English
Avis aux utilisateurs
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