Company OverviewOur 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.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 $30 M 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 RoleThis 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 tasksImprove eval reliability, coverage, and signal quality through better rubrics, task design support, and scoringShip tools that let researchers and operators run experiments without reinventing the process each timeBuild APIs and backend services that power human-in-the-loop workflows,



task routing, and quality checksImprove the pipelines that turn expert work into structured training and evaluation dataMake systems more observable, scalable, and easier to operate through logging, metrics, and debuggingWrite transparent, maintainable code, take part in reviews and design discussions, and document decisions so others can build on themYou Bring:Strong coding fundamentals in Node.Js and Type ScriptStrong coding ability in Python and/or GoExperience building and owning production systems such as APIs, services, and pipelinesA solid understanding of distributed systems and engineering trade-offsComfort with AWS or GCP and modern infrastructure (containers, Kubernetes)A track record of shipping and maintaining systems other people rely on, not only prototypesStrong written communication and comfort working async in a distributed teamBonus Points:Experience with evaluation frameworks, experimentation platforms, or ML toolingExperience with data pipelines or workflow orchestrationExperience building internal platforms for operators or research teamsExperience in early-stage or high-ownership B2 B Saa S or platform teamsWhat'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 experienceReal ownership, with growth into bigger systems, deeper technical leadership, and projects core to how the company scalesA lean, async-first team that values clear writing, sound judgment, and follow-throughResearch-adjacent engineering at the frontier of AI, alongside practical platform workDirect impact on the data and evaluations used by leading AI labsInterview Process:Take-home assignment covering practical engineering and how you communicate decisionsApplication review by the teamFounding engineer screen, a deep dive on system design, trade-offs, and past ownershipWork trial on real-world work, focused on execution, quality, and collaborationOffer#J-18808-Ljbffr

📌 Software Engineer | Ai Training Data & Evals Lab (Vancouver)
🏢 Atomic - Remote Jobs
📍 Vancouver