Product Manager
San Francisco Office
- Remote-first
- Website
- @hellopareto 𝕏
- Search
Pareto.AI
Pareto matches AI companies with expert-vetted data labelers to fine-tune cutting-edge AI/LLM models. Meet the top 0.01% of AI labelers today.About us
At Pareto.AI, we’re on a mission to enable top talent around the world to participate in the development of cutting-edge AI models.
In coming years, AI models will transform how we work and create thousands of new AI training jobs for skilled talent around the world. We’ve joined forces with top AI and crowd researchers at Anthropic, Character.AI, Imbue, Stanford, and University of Pennsylvania to build a fair and ethical platform for AI developers to collaborate with domain experts to train bespoke AI models.
About this role
Support Pareto’s engineers to ship a best-in-class platform for scalable, secure delivery of extremely high-quality, curated data to AI companies. You will translate customer and research requirements into crystal-clear product objectives, orchestrate cross-functional execution, and ensure every release measurably advances throughput, quality, and profitability.
Key Qualifications
3+ years shipping data infrastructure or ML-adjacent SaaS products with measurable adoption.
Proven record of rapid, iterative releases—multiple significant deployments per quarter.
Competence with SQL and basic Python; can self-serve analysis without data-engineering support.
Familiarity with data-collection pipelines, LLM fine-tuning workflows, and privacy frameworks (GDPR, CCPA).
Comfortable translating technical detail (Django, React, Celery, Postgres) into clear product choices.
Exceptional written communication; produce single-page briefs that unblock design and engineering the same day.
Bachelor’s degree in CS, Engineering, Statistics, or equivalent practical expertise.
Willing to work on-site in South Bay three days per week and travel occasionally to customer or annotation sites.
Description
Join a small, fast-moving team building the platform that delivers curated data to the world’s most advanced AI companies. You will partner closely with engineers, researchers, and customers to transform ambiguous needs into precise product requirements and to shepherd features from concept through launch.
Own the end-to-end life cycle for assigned platform modules—requirements, prioritization, release, and iteration.
Define and monitor metrics for data latency, cost per datapoint, and quality compliance; drive course-corrections in real time.
Conduct regular customer and stakeholder interviews; translate insights into backlog refinements and roadmap updates.
Facilitate sprint rituals and decision reviews, ensuring blockers are identified and resolved within service-level targets.
Present concise status and recommendations to leadership, backing every argument with data.
Education & Experience
Bachelor’s in a technical field required; advanced degree a plus.
Prior experience in human-in-the-loop, crowdsourcing, or high-compliance data environments is advantageous but not mandatory.
Additional Requirements
This role is classified as Product Manager (IC); people management experience is not required.
More about the role:
Ground floor opportunity at a venture-backed startup.
International and dynamic work environment with flat hierarchies and space for ownership.
Challenging and diverse projects for continuous growth.
Competitive salary and equity package.
Annual stipend to support home office needs.
Medical insurance reimbursement.
We’re a remote-first company with an engineering team spread across the US, Africa and Latin America with a greater team that extends to Asia too. To make this work, we have core collaboration hours set from 8am to 1pm PST during which we have our stand-ups, planning meetings or retros.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Anthropic Django Engineering LLMs Machine Learning Pipelines PostgreSQL Privacy Python React Research SQL Statistics
Perks/benefits: Competitive pay Equity / stock options Home office stipend Startup environment
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