Lead/Principal Applied Scientist
Salesforce
•
San Francisco, CA
0
0
0
0
Not Applicable
Posted April 17, 2026
Job link
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Responsibilities
Responsibilities
- Research, Modeling & Hands-On Execution
- 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.
- Mentorship & Thought Leadership
- 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
Not Met Priorities
What still needs stronger evidence
Requirements
- Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact. [Nice to have]
- 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.
- 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, 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).
- Leadership & Collaboration
- Experience mentoring and developing junior researchers or engineers.
- Strong communication skills across research, engineering, and executive stakeholders
Preferred Skills
- Strong background in reinforcement learning, preference learning, or human-in-the-loop learning.
- Strong communication skills across research, engineering, and executive stakeholders
- 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).
- Work on mission-critical LLM systems at massive scale.
- Own models end-to-end, from research to production impact.
- Shape the future of enterprise-grade AI agents.
- Collaborate with world-class researchers and engineers.
Education
- (Not required) – Education & Research Background
- (Not required) – PhD in Computer Science, Machine Learning, AI, or a related field.
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.
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 Lead/Principal Applied Scientist to drive advanced LLM research and model development for AgentForce’s production services. This role requires hands-on involvement across the full model development lifecycle, in addition to technical leadership and mentorship.
The ideal candidate is both a strong individual contributor and a technical leader, serving as a primary point of contact (POC) for major AI initiatives while shaping long-term research and modeling strategy.
Key Responsibilities
Research, Modeling & Hands-On Execution
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.
Technical Leadership
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.
Mentorship & Thought Leadership
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 a related field.
Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact. [Nice to have]
Core Technical & Hands-On Requirements
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.
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, 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). 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).
Why Join AgentForce?
Work on mission-critical LLM systems at massive scale.
Own models end-to-end, from research to production impact.
Shape the future of enterprise-grade AI agents.
Collaborate with world-class researchers and engineers.
See your research ship, scale, and matter.
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 require assistance due to a disability applying for open positions please submit a request via this 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. 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.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $172,500 - $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $344,700 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
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.
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 Lead/Principal Applied Scientist to drive advanced LLM research and model development for AgentForce’s production services. This role requires hands-on involvement across the full model development lifecycle, in addition to technical leadership and mentorship.
The ideal candidate is both a strong individual contributor and a technical leader, serving as a primary point of contact (POC) for major AI initiatives while shaping long-term research and modeling strategy.
Key Responsibilities
Research, Modeling & Hands-On Execution
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.
Technical Leadership
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.
Mentorship & Thought Leadership
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 a related field.
Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact. [Nice to have]
Core Technical & Hands-On Requirements
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.
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, 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). 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).
Why Join AgentForce?
Work on mission-critical LLM systems at massive scale.
Own models end-to-end, from research to production impact.
Shape the future of enterprise-grade AI agents.
Collaborate with world-class researchers and engineers.
See your research ship, scale, and matter.
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 require assistance due to a disability applying for open positions please submit a request via this 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. 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.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $172,500 - $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $344,700 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.