
Member of Technical Staff (Research Engineering)
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Overview
Job Description
About us
ATG (Autonomous Technologies Group) is an AI lab deploying frontier reasoning systems within financial markets. Autonomous is an agentic wealth manager built on this foundation.
Founders: Early GPU cloud (9 figure exit).
Investors: Led by Garry Tan / YC with participation from one of the most successful quant funds, BoxGroup (Plaid, Ramp, Stripe), and other top-tier angels.
About the Role
You’ll bridge research and engineering—rapidly implementing, experimenting with, and scaling new algorithms and models. You’ll work closely with scientists and founders to translate ideas into high-performance systems, and will operate across the stack from prototyping to deployment.
Responsibilities
Prototype and scale experimental models (LLMs, RL agents, agentic systems) on large, real-world data.
Build tools and pipelines for training, evaluation, and analysis.
Implement state-of-the-art research from papers and iterate in collaboration with scientists.
Own the full ML lifecycle: data engineering, experimentation, training, and deployment.
Operate in a highly autonomous, engineering-driven environment.
Requirements
Strong background in deep learning, reinforcement learning, or computational modeling.
Expert-level Python and significant experience in ML frameworks (PyTorch, JAX, TensorFlow).
Ability to translate research into robust, scalable systems.
Experience with distributed computing or large-scale ML pipelines.
Demonstrated curiosity and hands-on experimentation skills.
Expertise in leveraging the latest AI tools (Cursor, Claude Code, Codex, etc) to increase productivity & code output while maintaining high code quality, maintainability, and structure.
Why Join ATG
We're building a small, elite team. If you're excited by AI, markets, and building from first principles, we’d love to meet you.
Work on AI with a massive market opportunity
Early team of repeat founders backed by top investors
High agency, talent dense, zero bureaucracy
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Submit 10x as many applications with less effort than one manual application.
