Member of Technical Staff, Platform Engineering
Job Description
ABOUT ABUNDANT
As the need for scaling data becomes the core bottleneck to progress, moving from general knowledge to domain expertise, and from chatbots to agents. Abundant is solving this by designing and operating simulation infrastructure where next-generation models learn to reason and solve complex problems.
Our team is composed of high-impact former founders, ML engineers, roboticists, and data leads from companies like Waymo and Google, with experience deploying models at 1B user scale. We partner with a majority of the top AI labs, frontier startups, and F500 enterprises to enable AGI, ASI, and physical intelligence.
THE ROLE
As a Member of Technical Staff, you will work directly with the Head of Engineering to build, scale, and deploy projects. You'll be expected to transition seamlessly between customer-facing products and our internal infrastructure.
WHAT YOU’LL DO
In your first 30 days, you will take over key parts of the core simulation engine, adding new features or improving performance, driving key metrics such as throughput and cost. You'll be responsible for making sure our software can scale to 10B+ events and tens of thousands of monthly users (both human and virtual). You'll be responsible for establishing our engineering culture as we continue to scale our technical team.
WHO YOU ARE
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Technically fluent : Deep understanding of evaluations, RL, and how LLMs and Agents work.
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Research-oriented — Able to read papers and keep up with SOTA literature.
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Extreme owner — You have extreme agency and a founder mentality; you are resourceful and scrappy.
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Creative problem solver — You leverage creativity and research skills to design projects from the ground up.
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Must be highly proficient with coding agents such as Codex or Claude Code.
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Job Details
- Category
- Software
- Employment Type
- Full Time
- Location
- San Francisco, Argentina
- Posted
- Last updated
- May 26, 2026, 01:40 PM
About Abundant
Hello! 👋 We are a team of former ML engineers, founders, roboticists and ops leads who obsess about data and its impact on safe, reliable AI. We specialize in creating environments and datasets for RL by leveraging our experience in simulation and model training. By the numbers: • Powering 3 of the top 6 global AI labs and multiple Fortune 500 enterprises • Billions of training tokens generated each month, 2x month over month • Exclusive, global network of over 500 domain experts We believe humans are inherently creative, and thrive by pushing the frontier. We are working towards an abundant future--one where everyone has access to infinite intelligence, services and goods. Based in San Francisco, CA. We enjoy good food and good company. -- more info below -- Abundant is building the NVIDIA of training data. AI models rely on two fundamental ingredients: compute and data. NVIDIA, the leader in compute, has a peak market cap of $5T and generated $130B in revenue last year as the need for scaling compute has exploded. We believe the need to scale data is just beginning, as we move beyond SFT and human supervision to RL and Learning from Experience. Our founding team consists of second-time founders, ML engineers and data leads from Waymo, Google, Meta and AWS. Our team has previously collaborated with DeepMind to classify hate speech in YouTube videos, trained SOTA models for self-driving, and scaled data pipelines with thousands of human annotators. Our pioneering work in human computation, synthetic data, imitation learning and RL give us a solid advantage in delivering results to our customers. Why now? Training data is more important and more scarce than ever before. Scaling laws dictate that linear improvement in model performance demands an exponential increase in training data. But there is only one World Wide Web and most of it has already been trained on. The next advances will require new, diverse, and high-quality datasets, making training data more important and scarce than ever before. What happens if we succeed? Abundant will be the core enabler for AGI and beyond. Most of the challenges in model training are already solved. What’s missing is the data necessary to move from general knowledge to domain expertise; from chatbots to agents; and from digital intelligence to physical AI. Ask any AI researcher or roboticist: the core bottleneck to progress is the availability of data, i.e. “abundant data”. Abundant works with the most advanced AI labs and startups, as well as F500 enterprises.
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