Member of Technical Staff, Synthetic Data

Who is Recruiting from Scratch: Recruiting from Scratch is a specialized talent firm dedicated to helping companies build exceptional teams. We partner closely with our clients to deeply understand their needs, then connect them with top-tier candidates who are not only highly skilled but also the right fit for the company's culture and vision. Our mission is simple: place the best people in the right roles to drive long-term success for both clients and candidates. Title of Role: Member of Technical Staff - Synthetic Data Location: San Francisco, CA or Manhattan, NY Company Stage of Funding: Seed-stage, venture-backed Office Type: Onsite (5 days per week) Salary: $150,000 - $350,000 base + equity Company Description Our client is a fast-growing, venture-backed AI startup building next-generation infrastructure to enable more capable and reliable AI agents in enterprise environments. Backed by top-tier investors and operating at the frontier of AI, the team focuses on creating highly realistic synthetic environments and data that allow agents to train, evaluate, and operate in complex, real-world scenarios. This is a small, deeply technical team with a strong culture of ownership, speed, and precision. They are scaling quickly and tackling problems that sit at the intersection of data engineering, simulation, and applied AI research. What You Will Do As a Member of Technical Staff focused on Synthetic Data, you will play a critical role in building and owning the company's synthetic data and simulation pipelines. This role is data engineering-first, with meaningful exposure to applied research. You will: Design, build, and maintain end-to-end data engineering pipelines, including ingestion, transformation, storage, and schema evolution Develop large-scale synthetic data pipelines that simulate realistic enterprise environments, including logs, usage data, and operational metrics Work with massive datasets (tens of millions of rows) and optimize systems for scalability, performance, and cost Create realistic, noisy, and imperfect data that mirrors real enterprise complexity and inconsistency Ensure cross-system consistency so AI agents can navigate multi-application workflows Read, interpret, and apply research related to simulation and synthetic data generation Collaborate closely with a highly technical team to iterate quickly and ship impactful infrastructure Ideal Candidate Background We're looking for candidates who are excited by early-stage environments and high-ownership roles. You may be a strong fit if you have: 2+ years of experience in data engineering, or are a recent graduate with substantial internships or research experience Strong programming skills in Python and SQL Experience building or maintaining data pipelines in production environments Comfort working with ambiguity and rapidly evolving requirements A strong sense of ownership and the ability to move quickly without sacrificing quality Preferred While not required, the following are strong pluses: Experience with synthetic data, simulations, or related research Open-source contributions or published research Experience working at early-stage or fast-growing startups Familiarity with AI systems, agent workflows, or applied ML infrastructure Experience working with large-scale datasets and distributed systems Compensation, Benefits, and Other Details Base Salary: $150,000 - $350,000 (depending on experience) Equity: Competitive equity package Employment Type: Full-time Work Authorization: Visa sponsorship may be available for the right candidate Location Requirement: Onsite in San Francisco or Manhattan Hiring Goal: 2 hires, with high urgency Why This Role Is Exciting This is an opportunity to work directly on core infrastructure that enables the next generation of AI agents. You'll operate at the intersection of data engineering and AI research, have real ownership over critical systems, and collaborate with a small team moving at exceptional speed. If you're motivated by impact, technical depth, and building from the ground up in a high-growth startup environment, this role offers both challenge and upside.

Created: 2026-03-04