Software Engineering SMTS/ LMTS - LLM Model building

Job description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Senior Applied Scientist – AgentForce Team Overview 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 Senior Applied Scientist to contribute to advanced LLM research and model development for AgentForce’s production AI services. This role requires strong hands-on involvement across the full model development lifecycle, including model training, fine-tuning, evaluation, reinforcement learning, optimization, and deployment support. The ideal candidate is a strong individual contributor who can independently drive technical execution while collaborating closely with research, engineering, product, and infrastructure teams. The candidate will work on production-scale AI systems supporting enterprise-grade agentic workflows, reasoning systems, evaluation services, and multi-modal AI capabilities. Key Responsibilities Research, Modeling & Hands-On Execution • Execute hands-on work across the full model development lifecycle, including: • Data preparation and curation • Synthetic data generation • Model training and fine-tuning • RLHF / RLAIF workflows • Evaluation and benchmarking • Error analysis and iteration • Inference optimization • Deployment readiness • Contribute to research and development efforts for: • Large language models • Tool-calling systems • Agentic reasoning workflows • Multi-modal AI models • Evaluation and guardrails systems • Continuous learning pipelines • Design and implement experimentation pipelines for: • Reinforcement learning • Preference optimization • Alignment tuning • Offline and online feedback learning • Conduct rigorous experimentation, benchmarking, and failure analysis to improve: • Accuracy • Latency • Reliability • Robustness • Cost efficiency • Translate research ideas into scalable production-ready AI solutions. • Support optimization initiatives including: • Quantization • Distillation • Distributed inference optimization • Throughput and serving efficiency improvements Technical Collaboration • Partner with senior scientists, engineers, and product teams to deliver production AI solutions. • Contribute to model training, evaluation, release readiness, and production support processes. • Collaborate with infrastructure teams on scalable training and inference workflows. • Help define and improve best practices for: • Model evaluation • Experiment tracking • Data quality • Continuous learning • Production monitoring • Participate in technical reviews, roadmap discussions, and cross-functional plann