Salary - $150,000 - $250,000
What You'll Own
Build and manage relationships with frontier AI labs and highly technical customers
Translate customer needs into internal product and research priorities
Operate as a strategic partner to both external customers and internal technical teams
Drive cross-functional execution across research, product, operations, and partnerships
Help identify operational bottlenecks and implement scalable systems and processes
Support high-priority strategic initiatives across the company
Work closely with researchers, engineers, and leadership on ambiguous, fast-moving projects
Navigate technical conversations around datasets, model performance, infrastructure, and AI workflows
Help shape how Sieve scales customer engagement and internal operations as the company grows
Contribute wherever needed as a high-leverage generalist operator
Requirements
Must-Have
Excellent general problem solving skills
Bachelor's degree in computer science (or equivalent technical degree)
Strong understanding of ML and AI workflows, particularly around data pipelines and model training
Enough technical depth to hold substantive conversations with engineers and researchers at frontier AI labs
Familiarity with how data quality, pipeline architecture, and dataset composition affect model training outcomes
Excellent written and verbal communication that commands credibility with senior technical stakeholders
Comfort translating between engineering and business audiences fluently
High curiosity and high EQ
Scrappy and able to pick things up on the job in a fast-moving startup environment
In-person at Sieve SF HQ 5 days a week
Nice-to-Have
Senior track: existing relationships at AI labs or frontier model companies (highest-signal background for the senior track)
Senior track: background at a human data company (Scale, Surge, Sapien, Mercor, Invisible, Toloka, Snorkel, Defined.ai, Datasaur)
Generalist track: engineering degree paired with 1-2 years at MBB, top consulting, banking, or APM at an AI company
Background in video, media, or content-related technologies
Prior early-stage startup experience, especially as an early hire who saw a customer-facing function get built from zero
Experience in product, technical operations, or a customer-facing technical role
