Senior / Lead Member of Technical Staff - AI Research
$173k - $260k • Remote • California - Palo Alto
Posted 29d ago
Job Location
California - Palo Alto
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Research Engineer
About the job
Salesforce is seeking a Senior / Lead Member of Technical Staff to join their AI Research team. This role involves innovating at the frontier of AI, creating new solutions, and defining new product categories with significant impact. You will work closely with software engineers, product managers, and solution engineers to design, implement, and iterate on agentic AI systems. The ideal candidate will possess exceptional engineering skills, deep ML knowledge, strategic thinking, and a dedication to building exceptional product experiences in a fast-paced environment.
Responsibilities
- Design, implement, and iterate on agentic AI systems.
- Innovate at the frontier of AI and create new solutions.
- Integrate AI agents seamlessly into human workflows to drive efficiency and innovation.
- Contribute to building and maintaining a shared system context for AI reliability.
- Critically evaluate code (human or AI-generated) for correctness, quality, security, and performance.
- Build and ship production-grade software with AI as a core part of the development workflow.
- Implement and debug model training, evaluation, and inference pipelines.
- Deploy ML systems using Docker and cloud platforms.
- Optimize distributed training or inference and performance.
Requirements
- Exceptional engineering skills.
- Deep ML knowledge with meaningful implementation track records.
- Prioritize deep and strategic thinking.
- Dedication, patience, and resilience to build exceptional product experience.
- Collaborative and able to win with teams.
- Proactive and bias to action, comfortable in fast-pacing environment.
- Competitive coding winner (ACM-ICPC, etc.) or startup founder experience in agentic AI/LLM areas.
- 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.
- 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.
- Hands-on experience with deep learning frameworks.
- Strong understanding of ML fundamentals.
- Experience deploying ML systems using Docker and cloud platforms (AWS, GCP, or Azure).
- Familiarity with distributed training or inference and performance optimization.
- Demonstrated, genuine AI-first approach to engineering.
- Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) in development workflows.
- Advanced prompt engineering skills and ability to write precise, structured prompts.