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.

• Competitive coding winner (ACM-ICPC, etc).

• Startup founders in agentic AI, LLM, area (e.g., YC backed startup)

• Programming & Systems:

• Strong proficiency in Python; solid experience with C++ and/or Java

• Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.

• Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.

• Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.

• Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance

• Strong software engineering fundamentals (data structures, algorithms, system design)

• Experience building production-quality systems

• 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

• 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

• Infrastructure & Deployment

• Experience deploying ML systems using Docker and cloud platforms (AWS, GCP, or Azure)

• Familiarity with distributed training or inference and performance optimization

• A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.

• Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows

• Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.