Member of Technical Staff, Quantitative Systems

2 нед. назад

200k–300k USD / yearUSALead

optimizationsimulationbacktestingapi

Design and implement core quantitative systems for allocation, risk, and optimization in financial markets.

ATG () is an AI lab deploying frontier reasoning systems within financial markets.
Founders: Early GPU cloud (9 figure exit).
Investors: Garry Tan / YC + Founder of one of the most successful quant funds, BoxGroup (Plaid, Ramp, Stripe), top-tier angels.

Design and implement core quantitative systems for allocation, risk, and optimization in complex, real-world environments.
Formulate and analyze multi-objective optimization problems (e.g., risk/return, constraints, transaction and implementation frictions) and turn them into robust, solvable formulations.
Develop simulation, backtesting, and evaluation frameworks to understand system behavior under varying regimes and stress scenarios.
Collaborate closely with engineers and product to integrate quantitative logic into reliable, maintainable services and APIs.
Prototype new approaches quickly, run experiments, and iterate based on empirical results.
Establish standards for model quality, validation, monitoring, and numerical stability in production systems.
Document assumptions, constraints, and design rationales so the broader team can build on your work.

Strong quantitative background (e.g., mathematics, physics, computer science, operations research, statistics, or similar).
Deep expertise in at least one of: optimization (convex/non-convex), stochastic processes, numerical methods, or quantitative finance.
Experience designing and implementing quantitative or algorithmic decision systems (research + production, not just toy models).
Proficiency in Python and familiarity with scientific/ML tooling (NumPy, JAX/PyTorch, Pandas, etc.); experience with a systems language (C++/Rust/Go) is a plus.
Ability to reason from first principles, simplify complex objective spaces, and choose the right level of modeling detail for the problem.
Track record of turning mathematical ideas into clean, reliable, well-tested code that can run in production.
Comfort working with real-world data (noisy, incomplete, biased) and building pipelines and checks around it.
Habit of staying current on advances in optimization, quantitative methods, and AI tools—and using those tools (Cursor, Copilot-style assistants, etc.) to increase velocity while keeping code quality high.

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