AI Engineer
Summary
AI engineer at quantitative trading firm Radix Trading builds firmwide AI infrastructure and production-grade LLM applications and agentic workflows (RAG, LangChain/LangGraph, guardrails, eval harnesses), using Python and modern C++ to support automated trading, alpha research, and petabyte-scale ML systems.
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.
Skills
As published by greenhouse · 17 questions · 1 written answer
Basics
First Name, Last Name, Email, Phone, Resume/CV, Cover Letter, Location
Short answers (4)
- What is your earliest possible start date?
- LinkedIn Profile optional
- Website optional
- If you heard about Radix from an employee here (other than our recruiters), please enter their full name below. optional
Pick from a list (9)
- Do you require employer-sponsored work authorization?
- Do you have an office location preference?
- Do you have a non-compete agreement?
- What year did you graduate with your most recent degree?
- What is your most recent degree?
- If you’re currently affiliated with an accredited university, what is the affiliation?
- Since your most recent degree completion, how many years of work experience do you have?
- Do you hold any outstanding offers or have any deadlines to accept an offer?
- How did you hear about Radix?
Written answers (1)
- If you answered Yes to the previous question, please share all relevant info (job title, company name, city, deadline, ect.). optional
Attachments (3)
- Please upload your unofficial undergrad transcript.
- Please upload your unofficial grad transcript. optional
- Please upload any additional transcripts. optional