Radix Trading

Research-focused trading firm using machine learning

AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeIn-person

About the Job

Requirements

At least 5 years of overall technical experience, including at least 2 years of hands-on experience designing and building generative AI or large language model applications in production.
A bachelor's, master's, or PhD in Computer Science, Data Science, Machine Learning, or a related technical field.
Strong proficiency in Python, solid software engineering principles, and deep understanding of the full AI/ML lifecycle from data preparation through observability.
Expert familiarity with agentic frameworks and modern AI orchestration and development tools such as LangChain, LangGraph, AutoGen, and LlamaIndex, along with deep practical prompt engineering skills.
Proven capability to evaluate and select foundational or open-weight models, balancing enterprise trade-offs across capability, context length, hosting, security, and cost.

Responsibilities

Build and accelerate the firmwide AI ecosystem and infrastructure to support AI-native systems and tooling.
Identify workflow opportunities for developers and researchers to introduce AI-first systems and automation.
Design, deploy, and evaluate production-grade AI applications and advanced agentic workflows using retrieval-augmented generation, semantic search, planning, and structured outputs.
Develop platform-level safeguards, including bespoke evaluation harnesses, guardrails, approval workflows, tool-use limits, source attribution, and kill switches.
Optimize core metrics across agent systems, focusing on reasoning quality, retrieval accuracy, latency, cost, safety, and user experience.

As a Quantitative Technologist, your goal is to enhance live trading systems, research infrastructure, and alpha generation through technology innovation.

We leverage cutting-edge software and hardware (mostly with modern C++) to push production level code daily and evolve fully-automated strategies, alpha generation processes, live trading systems, and machine learning research and simulation infrastructure used to analyze petabytes of data.

The Role

Build and accelerate the firmwide AI ecosystem and infrastructure to support AI-native systems and tooling
Identify workflow opportunities for developers and researchers to introduce AI-first systems and automation
Design, deploy, and evaluate production-grade AI applications and advanced agentic workflows (utilizing RAG, semantic search, planning, and structured outputs)
Develop platform-level safeguards, including bespoke evaluation harnesses, guardrails, approval workflows, tool-use limits, source attribution, and kill switches
Optimize core metrics across agent systems, focusing on reasoning quality, retrieval accuracy, latency, cost, safety, and user experience

About You

5+ years of overall technical experience, including 2+ years of hands-on experience designing and building Generative AI/LLM applications in production
Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Machine Learning, or a related technical field
Strong proficiency in Python alongside solid software engineering principles and a deep understanding of the full AI/ML lifecycle (data prep through observability)
Expert familiarity with agentic frameworks and modern AI orchestration and development tools (e.g., LangChain, LangGraph, AutoGen, LlamaIndex) alongside deep practical skills in prompt engineering
Proven capability to evaluate and select foundational or open-weight models, balancing enterprise trade-offs across capability, context length, hosting, security, and cost

COMPENSATION – Competitive base salary, plus quarterly bonus based on individual performance and contribution towards success of others and the firm.

About the Company

Radix Trading

Research-focused trading firm using machine learning

Radix Trading is a research-driven trading firm that uses machine learning and statistical methods to develop and monetize trading strategies. It operates in major electronic markets across North America, Europe, and Asia, supported by a proprietary automated research platform and continuously evolving strategies. The firm distinguishes itself by private funding with no outside investors, an open culture that favors rapid idea execution, and a team of interdisciplinary academics and technologists focused on longer-horizon, statistically driven opportunities rather than traditional high-frequency trading. Its goal is to generate returns by deploying advanced, independently developed trading strategies that stay ahead of the competition in electronic markets.

Company size: 51-200
Company stage: Private
Total funding: Not listed
Headquarters: Chicago, Illinois
Founded: 2014

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