Lead Member of Technical Staff – AI Research

Salesforce

. Design, implement, and iterate agentic AI systems with customers .

Posted 9/15/2026full-timePalo Alto • California • United StatesSenior💰 $172,500 - $260,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates exceptional engineering skills with a deep understanding of machine learning and experience in building production-quality AI systems. Proficient in Python and familiar with C++ or Java, with hands-on experience in deploying ML systems and optimizing performance in complex AI-driven applications.

Highest-signal resume keywords

Python ProficiencyMachine Learning ExpertiseProduction-Quality System DevelopmentDeep Learning Frameworks ExperienceAI Tools Utilization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonC++JavaDeep Learning FrameworksModel TrainingModel EvaluationInference PipelinesDistributed Training OptimizationAI System Design

Soft Skills

Strategic ThinkingCollaborationCritical Evaluation

Tools & Technologies

DockerAWSGCPAzureClaude CodeGitHub CopilotCodexCursor

Industry Keywords

Agentic AILLMsAI-Driven ApplicationsPrompt EngineeringSystem Design

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformJavaPythonC++

About the role

Key responsibilities & impact

Design, implement, and iterate agentic AI systems with customers
Collaborate closely with software engineers, product managers, and solution engineers
Innovate at the frontier of AI and create new solutions
Define new product categories with meaningful impact for Salesforce customers
Build and ship high-quality, production-grade software using modern engineering practices
Design and orchestrate complex systems where AI agents integrate into human workflows
Contribute to the shared system context repository of system designs, constraints, and standards
Critically evaluate human- or AI-generated code for correctness, quality, security, and performance
Implement and debug model training, evaluation, and inference pipelines
Deploy ML systems using Docker and cloud platforms
Optimize distributed training or inference performance

Requirements

What you’ll need

Exceptional engineering skills
Deep ML knowledge with meaningful implementation track records
Strong technical competence and strategic thinking
Strong proficiency in Python
Solid experience with C++ and/or Java
Strong software engineering fundamentals in data structures, algorithms, and system design
Experience building production-quality systems
Practical experience with LLMs and agentic workflows, including tool use, planning, memory, and 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 implementing and debugging model training, evaluation, and inference pipelines
Experience deploying ML systems using Docker and cloud platforms such as AWS, GCP, or Azure
Familiarity with distributed training or inference and performance optimization
Demonstrated, genuine AI-first approach to engineering
Experience using AI tools such as Claude Code, GitHub Copilot, Codex, or Cursor in development workflows
Advanced prompt engineering skills
Ability to write precise, structured prompts and cultivate reliable, secure, and production-ready system context
Competitive coding winner (ACM-ICPC, etc.) or startup founder experience in agentic AI/LLM areas is mentioned

Benefits

Comp & perks

Time off programs
Medical insurance
Dental insurance
Vision insurance
Mental health support
Paid parental leave
Life insurance
Disability insurance
401(k)
Employee stock purchasing program
Incentive compensation may be available for certain roles
Equity may be available for certain roles
Reasonable accommodation during the application or recruiting process