Description
Salesforce AI Research is looking for a Machine Learning Engineer to incubate next-generation agentic AI platform. You will work with research scientists, software engineers, product managers and solution engineers closely to design, implement and iterate agentic AI systems with customers.
With your strong technical competence, strategic thinking and customer engagement, you will innovate at the frontier of the field, having the opportunity to create new solutions and define new categories of product with meaningful impact to Salesforce customers and beyond.
We are looking for candidates who
Has exceptional engineering skills.
Has deep ML knowledge with meaningful implementation track records.
Prioritize deep and strategic thinking.
Has dedication, patience and resilience to build exceptional product experience.
Collaborative and win with teams.
Proactive and bias to action, comfortable in fast-pacing environment.
For this role, we mainly look for BS/MS student instead of PHD student (For PhD, only look for candidate who fulfill one of the 5 criteria below, and interested in MLE instead of RS role)
Top school majoring in CS (Stanford, Berkeley, CMU, MIT)
Competitive coding winner (ACM-ICPC, etc). Look for global and competitive competition, Kaggle dose not count.
Startup founders in agentic AI, LLM, area (e.g., YC backed startup)
As first contributor, open source and maintain projects that gets traction (2k+ stars)
For BS/MS, as first author, publish at least one high-impact paper (500+ citations). For PhD, the bar will be higher (2-3 first-author high impact (500+ citations) papers or 1 first-author stellar (3000+citations) paper)
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Programming & Systems
Strong proficiency in Python; solid experience with C++ and/or Java
Strong software engineering fundamentals (data structures, algorithms, system design)
Experience building production-quality systems
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Agentic / LLM Systems
Practical experience with LLMs and agentic workflows (tool use, planning, memory, multi-step reasoning)
Experience building end-to-end AI agents or complex AI-driven applications
Familiarity with prompting, orchestration, and evaluation for LLM-based systems
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Machine Learning & AI
Hands-on experience with deep learning frameworks
Strong understanding of ML fundamentals
Experience implementing and debugging model training, evaluation, and inference pipelines
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Infrastructure & Deployment
Experience deploying ML systems using Docker and cloud platforms (AWS, GCP, or Azure)
Familiarity with distributed training or inference and performance optimization
For roles in San Francisco and Los Angeles: 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.