Company Overview
Our client is a fast-growing applied research lab building the data layer for frontier AI. They partner with leading AI labs and enterprises to deliver two things: proprietary, expert-generated datasets, and rigorous evaluation and benchmarking. The goal is for AI systems to get better at real workflows, not just polished demos.

Founded in 2025 and backed by a $30M Series A, the company is fully remote. It works on problems such as turning real-world work into clean training signals, building evaluations for software engineering agents and finance workflows, and proving data quality by measuring actual performance lift.

Your Role
This is not a "pick up tickets and wait for specs" role.

• This is a broad builder seat that combines platform engineering with evaluation and experimentation infrastructure. You'll design, build, and run systems in production that researchers and operators depend on every day.

• The company is scaling the pipelines, evaluation harnesses, and training environments that make its work repeatable, and it needs engineers who can own a system and deliver reliably.

• In your first 30 to 90 days, success means shipping at least one meaningful production improvement and becoming the go-to owner of a core system.

You'll:

• Build and maintain evaluation harnesses that measure model and agent performance on real tasks

• Improve eval reliability, coverage, and signal quality through better rubrics, task design support, and scoring

• Ship tools that let researchers and operators run experiments without reinventing the process each time

• Build APIs and backend services that power human-in-the-loop workflows, task routing, and quality checks

• Improve the pipelines that turn expert work into structured training and evaluation data

• Make systems more observable, scalable, and easier to operate through logging, metrics, and debugging

• Write clear, maintainable code, take part in reviews and design discussions, and document decisions so others can build on them

You Bring:

• Strong coding fundamentals in Node.js and TypeScript

• Strong coding ability in Python and/or Go

• Experience building and owning production systems such as APIs, services, and pipelines

• A solid understanding of distributed systems and engineering trade-offs

• Comfort with AWS or GCP and modern infrastructure (containers, Kubernetes)

• A track record of shipping and maintaining systems other people rely on, not only prototypes

• Strong written communication and comfort working async in a distributed team

Bonus Points:

• Experience with evaluation frameworks, experimentation platforms, or ML tooling

• Experience with data pipelines or workflow orchestration

• Experience building internal platforms for operators or research teams

• Experience in early-stage or high-ownership B2B SaaS or platform teams

What's Offered:

• Full-time, fully remote role with a LATAM focus and meaningful overlap with U.S. time zones

• $7,000–10,000 USD/month, based on experience

• Real ownership, with growth into bigger systems, deeper technical leadership, and projects core to how the company scales

• A lean, async-first team that values clear writing, sound judgment, and follow-through

• Research-adjacent engineering at the frontier of AI, alongside practical platform work

• Direct impact on the data and evaluations used by leading AI labs

Interview Process:
1️⃣ Take-home assignment covering practical engineering and how you communicate decisions
2️⃣ Application review by the team
3️⃣ Founding engineer screen, a deep dive on system design, trade-offs, and past ownership
4️⃣ Work trial on real-world work, focused on execution, quality, and collaboration
5️⃣ Offer