AI/ML Engineer – Full Time
Location: Remote or Hybrid (Location Flexible)
Start Date: Rolling
Company: AM.Dirac, Quantitative Proprietary Trading Firm
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 OverviewWe 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 ResponsibilitiesAlpha 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.
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.
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.
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.
Skills Required
- BS in Computer Science, Machine Learning, or related field
- Fluency with Python, NumPy, pandas, scikit-learn, and Jupyter
- Experience with supervised learning, time series modeling, and neural networks
- Ability to rapidly test, measure, and iterate on models in noisy or nonstationary environments
- Motivation to learn market behavior and apply ML to trading
- MS/PhD in relevant field
- Familiarity with LLM workflows, prompting, RAG, fine-tuning, LangChain, Transformers, or OpenAI APIs
- Open source contributions, published research, or active GitHub showcasing applied ML work
- Experience building backend infrastructure for algorithmic trading and originating market signals
What We Do
AM.Dirac is an AI-forward quantitative proprietary trading firm that applies frontier machine learning research and programmatic reasoning to global financial markets and public market strategies. By leveraging advanced AI and programmatic reasoning, the firm focuses on developing high-performance strategies for public markets, operating at the intersection of quantitative finance and cutting-edge machine learning research.
