AM.Dirac is an AI-forward quantitative trading firm applying machine learning and programmatic reasoning to public market strategies. We use frontier ML capabilities to automate and scale our alpha-generating infrastructure across asset classes. Our team combines deep expertise in ML engineering, quant research, and systems design, working collaboratively to develop intelligent agents that operate in real-time, data-rich environments.
Position Overview
We are looking for a full-time AI/ML Engineer to join the core team at AM.Dirac . This is an opportunity to build intelligent systems that power next-generation trading strategies, from idea generation to execution. You’ll work at the intersection of state-of-the-art ML (including LLMs and agentic systems), portfolio optimization, and real-time data pipelines. Your work will directly support live strategies in production and lay the foundation for autonomous investment agents.
Key Responsibilities
Alpha Generation: Design and refine predictive ML models trained on intraday financial data and alternative datasets to surface trade signals.
Data Infrastructure & Experimentation: Work with large-scale time series, macroeconomic, and unstructured datasets. Build pipelines, clean data, and rapidly iterate on hypotheses.
Agentic Systems Development: Help architect and implement systems using LLMs to automate alpha research, backtesting, and live trading tasks.
Portfolio Optimization: Contribute to the design of intelligent portfolio construction methods that integrate learned signals and risk constraints.
Tooling & Research Ops: Develop internal experimentation frameworks in Python/Jupyter and build reproducible research workflows across the team.
Collaboration: Work closely with quant researchers, founders, and systems engineers to scale model impact from prototype to production.
Qualifications
Required:
Education: BS in Computer Science, Machine Learning, or related field (MS/PhD preferred). Exceptional candidates without a degree but with strong evidence of technical ability will be considered.
Python & Jupyter Expertise: Demonstrated fluency with Python, NumPy, pandas, scikit-learn, and Jupyter for ML development and research.
ML Proficiency: Experience with supervised learning, time series modeling, and neural networks. Bonus points for hands-on use of LLMs or reinforcement learning frameworks.
Experimentation Mindset: Ability to rapidly test, measure, and iterate on model-based systems in noisy or nonstationary environments.
Curiosity for Markets: While no prior finance experience is required, you should be motivated to learn how markets behave and how ML can exploit inefficiencies.
Preferred:
LLM & Agentic Workflows: Familiarity with prompting strategies, RAG, fine-tuning, or frameworks like LangChain, Transformers, or OpenAI APIs. Experience working with messy and unstructured data as inputs to modeling tasks.
Open Source Contributions or Research: Active GitHub, blog posts, or published work showing initiative and originality in applied ML.
Quantitative Finance: Experience building backend infrastructure for algorithmic trading and conducting research to originate market signals and trading strategies highly preferred.
Why Join Us
High Impact, Early Team Role: Shape the design and function of our AI-native investment platform from the ground up.
Research-to-Production: See your work go from notebook to production in live trading strategies.
Fast Iteration, Deep Focus: No bureaucracy, no endless meetings. Just focused execution with elite peers.
Compensation: Competitive base salary + performance-linked bonus + benefits.
Culture: Intellectually honest, independently driven, and curious about the edge of AI and finance.
Application Process
Interested candidates should submit:
Resume/CV highlighting your technical background and ML experience Optional: Links to open-source projects, GitHub repos, or writing samples Brief note (if you'd like) on why you're excited about joining an AI-native trading firm AM.Dirac is an equal opportunity employer. We value diverse perspectives and are committed to building a team that reflects a wide range of backgrounds and experiences.
