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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 planning efforts.

Mentorship & Growth

• Mentor junior team members through technical guidance and collaboration.

• Contribute to a strong culture of:

  • Scientific rigor
  • Ownership
  • Reproducibility
  • Fast iteration
  • Operational excellence

• Stay current with advancements in:

  • LLMs
  • Reinforcement learning
  • Agentic AI
  • Multi-modal AI
  • Distributed AI systems

• Contribute to internal technical knowledge sharing and innovation initiatives.

Required Qualifications

Education & Research Background

• PhD or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

• Strong research or industry experience in areas such as:

  • LLMs
  • NLP
  • Reinforcement learning
  • Multi-modal AI
  • Agentic systems

Core Technical & Hands-On Requirements

• Demonstrated hands-on experience in model development, training, fine-tuning, evaluation, and experimentation.

• Strong expertise in:

  • Large language model fine-tuning
  • Model evaluation
  • Inference optimization
  • Continuous learning workflows

• Experience with:

  • Reinforcement learning
  • Preference learning
  • Human-in-the-loop systems
  • Production AI evaluation

• Understanding of:

  • AI safety
  • Guardrails
  • Reliability
  • Production AI systems

• Experience working with distributed training or large-scale inference systems.

Coding & Tooling

• Strong proficiency in Python with solid software engineering fundamentals.

• Experience with:

  • PyTorch
  • TensorFlow

• Familiarity with modern LLM tooling and infrastructure, including:

  • Hugging Face (Transformers, PEFT, Accelerate)
  • DeepSpeed
  • FSDP
  • Ray
  • Kubernetes
  • vLLM

• Strong experimentation and data analysis skills using:

  • NumPy
  • Pandas
  • Custom evaluation pipelines

Leadership & Collaboration

• Strong collaboration and communication skills across:

  • Research
  • Engineering
  • Product
  • Infrastructure teams

• Ability to independently drive technical projects and deliver high-quality execution.

• Comfortable working in fast-moving, highly iterative AI development environments.

Preferred Qualifications

• Experience deploying and supporting production AI systems at scale.

• Background in:

  • Enterprise AI systems
  • Agentic AI workflows
  • Tool-calling systems
  • Multi-agent systems

• Familiarity with:

  • AI trust and safety systems
  • Governance frameworks
  • Responsible AI practices

• Experience with:

  • Multi-modal AI
  • Long-context models
  • Retrieval-augmented systems
  • Planner and reasoning systems

• Experience with multi-GPU or distributed compute environments.

Why Join AgentForce?

• Work on mission-critical AI systems operating at massive enterprise scale.
• Build and deploy production-grade LLM systems used by millions of users.
• Collaborate with world-class researchers and engineers.
• Solve challenging problems in reasoning, reinforcement learning, multi-modal AI, and agentic systems.
• See your work directly impact real-world AI products and enterprise customers.

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.

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Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.