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About the Role

This role sits at the intersection of applied research, agent systems, and production engineering. You’ll work on the core behavior and reasoning systems that power the platform, including tool use, context construction, multi-agent coordination, evaluation systems, scoring models, and long-horizon reasoning workflows. You’ll own ambiguous technical problems end-to-end: designing experiments, improving model behavior, building evaluation frameworks, and shipping systems into production. This is a highly technical, hands-on role working closely with the founders and engineering team in a fast-moving environment where research quickly becomes product.

Responsibilities

Build and improve the core agent systems powering the platform
Develop tool-use strategies, context management systems, and multi-step reasoning workflows
Design and implement evaluation pipelines, regression detection systems, and benchmarking frameworks
Research and improve model behavior across complex revenue and business tasks
Build scoring and prediction systems using structured and unstructured data
Improve context engineering, retrieval systems, entity resolution, and knowledge graph quality
Experiment with reinforcement learning and agent optimization approaches
Work across the full lifecycle from experimentation to production deployment
Partner closely with founders and engineering leadership on product direction and technical strategy

Requirements

Strong experience working with LLMs, deep learning systems, or modern AI infrastructure
Experience building or evaluating agentic systems, including tool use, reasoning workflows, or multi-agent coordination
Strong software engineering fundamentals and ability to ship production-quality systems
Ability to independently scope ambiguous technical problems and drive toward solutions
Experience designing experiments, evaluation frameworks, or benchmarking systems
Strong understanding of model behavior, prompting, fine-tuning, or RLHF concepts
Comfortable operating in fast-moving startup environments with high ownership
Experience with retrieval systems, vector databases, or knowledge graphs
Experience building AI products in production environments
Background in reinforcement learning or optimization systems
Publications or research experience in ML, NLP, or related fields

Skills

LLMs
Deep learning systems
Modern AI infrastructure
Agentic systems
Tool use
Reasoning workflows
Multi-agent coordination
Software engineering
Experiment design
Evaluation frameworks
Benchmarking systems
Model behavior
Prompting
Fine-tuning
RLHF
Retrieval systems
Vector databases
Knowledge graphs
AI products
Reinforcement learning
Optimization systems
ML
NLP

Location

San Francisco

Work Type

In-office

Experience Level

Senior
Early technical hire

Education Level

MS or PhD in Computer Science, Mathematics, Statistics, or related quantitative discipline

Salary/Compensations

$150,000–$220,000

Benefits

Competitive compensation
Equity
Comprehensive medical, dental, and vision coverage
401(k)
Unlimited PTO
Daily in-office lunch
Fully stocked kitchen

About the Company

Early-stage, well-funded AI startup
Building an AI-powered revenue intelligence platform designed to help modern revenue teams make faster, better decisions
Platform acts as “AI headcount” for GTM teams, helping Sales, RevOps, and Leadership teams understand what’s happening in their business and why by connecting CRM data, customer calls, emails, internal knowledge, and other business systems through a conversational AI interface
Recently launched publicly and is already working with well-known B2B SaaS organizations
Backed by top-tier investors including Insight Partners, GV (Google Ventures), Sequoia Capital, and Lightspeed

Equal Opportunity

We evaluate candidates based on merit and qualifications. We are committed to building an inclusive environment and welcome applicants of all backgrounds.