AI/ML Engineerโœฆ anywhere

๐Ÿข AM.Dirac ยท company page
๐Ÿ“ Worldwide
๐Ÿ’ฐ USD 150,000 - 250,000 / annual
๐Ÿ“… Posted Oct 29, 2026 ยท via Himalayas
โœ… Verified open โ€” last confirmed 3d ago
๐Ÿท AI ML Engineer, Machine Learning Engineer, Quant Developer, Quantitative Researcher, Data Scientist, AI ML Software Engineer
Apply on original site โ†—

AI/ML Engineer โ€“ Full Time Location: Remote or Hybrid (Location Flexible)
Start Date: Rolling
Company: AM.Dirac , Quantitative Proprietary Trading Firm
About AM.Dirac

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. Youโ€™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

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Alpha Generation: Design and refine predictive ML models trained on intraday financial data and alternative datasets to surface trade signals.

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Data Infrastructure & Experimentation: Work with large-scale time series, macroeconomic, and unstructured datasets. Build pipelines, clean data, and rapidly iterate on hypotheses.

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Agentic Systems Development: Help architect and implement systems using LLMs to automate alpha research, backtesting, and live trading tasks.

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Portfolio Optimization: Contribute to the design of intelligent portfolio construction methods that integrate learned signals and risk constraints.

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Tooling & Research Ops: Develop internal experimentation frameworks in Python/Jupyter and build reproducible research workflows across the team.

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Collaboration: Work closely with quant researchers, founders, and systems engineers to scale model impact from prototype to production.

Qualifications
Required:

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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.

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Python & Jupyter Expertise: Demonstrated fluency with Python, NumPy, pandas, scikit-learn, and Jupyter for ML development and research.

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ML Proficiency: Experience with supervised learning, time series modeling, and neural networks. Bonus points for hands-on use of LLMs or reinforcement learning frameworks.

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Experimentation Mindset: Ability to rapidly test, measure, and iterate on model-based systems in noisy or nonstationary environments.

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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:

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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.

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Open Source Contributions or Research: Active GitHub, blog posts, or published work showing initiative and originality in applied ML.

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Quantitative Finance: Experience building backend infrastructure for algorithmic trading and conducting research to originate market signals and trading strategies highly preferred.

Why Join Us

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High Impact, Early Team Role: Shape the design and function of our AI-native investment platform from the ground up.

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Research-to-Production: See your work go from notebook to production in live trading strategies.

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Fast Iteration, Deep Focus: No bureaucracy, no endless meetings. Just focused execution with elite peers.

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Compensation: Competitive base salary + performance-linked bonus + benefits.

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Culture: Intellectually honest, independently driven, and curious about the edge of AI and finance.

Application Process
Interested candidates should submit:

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Resume/CV highlighting your technical background and ML experience

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Optional: Links to open-source projects, GitHub repos, or writing samples

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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.

Originally posted on Himalayas

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