Descripción de la oferta
\Iqana is a Barcelona-based fintech startup delivering systematic, risk‑managed exposure to digital assets. Our mission is to make institutional‑grade crypto investing accessible through robust quantitative strategies and optimised portfolio construction.\
\We help investors—ranging from high‑net‑worth individuals to Family Offices, hedge funds, and asset managers—gain secure, automated access to crypto markets via separately managed accounts (SMAs). Powered by proprietary algorithms and advanced risk frameworks, Iqana is building the next‑generation infrastructure for digital asset investing: from data ingestion, strategy design, and execution to monitoring and reporting.\
\We've just closed a successful pre‑seed round and are backed by top investors and strategic advisors. We're scaling our founding team to shape the future of how the world invests in digital assets.\
\About the Opportunity
\Iqana is seeking a Quantitative AI / ML Engineer to lead the research, development, and deployment of advanced machine learning models driving our algorithmic trading strategies and risk management systems.\
\This role bridges financial data science, quantitative research, and MLOps — playing a key part in enhancing our proprietary models and expanding our quantitative capabilities. You'll work directly with the founding team to turn cutting‑edge research into production‑grade models while shaping our quantitative roadmap.\
\Are you passionate about building ML models that are live in the markets? Do you want to turn research into real‑world quantitative strategies that manage millions? Then this role is for you.\
\We're looking for a Quantitative AI / ML Engineer to lead research, development, and deployment of advanced ML systems powering our algorithmic strategies. You'll design predictive models, optimise real‑time trading logic, and ensure performance and explainability, bridging financial data science, quantitative research, and MLOps.\
\You’ll work closely with the CTO, Head of Quant Research, Quant Engineers, and software team to translate ideas into deployed code, while also owning our MLOps pipelines and research stack.\
\What a Typical Week Might Look Like
\Collaborate with the CTO, Head of Quant Research and Quant Engineers, to refine and implement new strategy components.\
\Research and prototype ML models (classification, regression, RL, etc.) for alpha generation, dynamic position sizing, signal filtering, etc.\
\Integrate macro, on‑chain, and sentiment data into multi‑layered predictive frameworks.\
\Join weekly strategic and product sessions with the founding team to align technical work with investor needs.\
\Support model deployment, real‑time monitoring, and performance analytics.\
\Explore and implement concept drift detection, model explainability, and robust evaluation practices.\
\Review research papers, explore market dynamics, and integrate new data sources into live strategies.\
\Key Responsibilities
\Design and develop dynamic ML frameworks to optimise trading strategies, improve risk management, and drive alpha generation.\
\Enhance trading performance through techniques such as meta‑labeling, position sizing, signal refinement, and real‑time prediction.\
\Research and prototype new ML models, including deep learning, reinforcement learning, and probabilistic approaches for time‑series data.\
\Collaborate in building our backtesting engine and model validation pipelines to ensure robustness and avoid overfitting.\
\Ensure model explainability, auditability, and transparency, especially for models driving financial decisions.\
\Develop reproducible research workflows and contribute to our internal library of notebooks and experiments.\
\Co‑lead research on cutting‑edge topics in quantitative finance & ML applications to it.\
\Collaborate with software engineers to deploy models into production and real‑time trading systems.\
\Explore generative AI applications (LLMs or multi‑modal models) to enhance market analysis or decision‑support tools.\
\What We’re Looking For
\MSc or Ph D in Machine Learning, Applied Mathematics, Physics, or Financial Engineering is highly preferred and will be considered a strong plus.\
\Expert in Python, data science libraries (NumPy, pandas, scikit‑learn), and ML frameworks (PyTorch, TensorFlow).\
\Cloud computing experience (AWS preferred, or Azure).\
\Strong grasp of time‑series modelling and ML (ideally for financial data).\
\Comfortable reading academic papers and turning theory into production‑ready tools.\
\Passion for research, speed, and solving hard problems with real‑world impact.\
\Knowledge of reinforcement learning, Bayesian optimisation, or probabilistic models is a strong plus.\
\Strong research skills, capacity to read and digest academic papers, and the ability to quickly apply new methods.\
\Experience building and deploying ML models (versioning, retraining, monitoring).\
\Skilled in feature engineering and integrating alternative data sources.\
\Bonus: Knowledge of C++ / Rust for performance‑critical components.\
\Nice to Have
\Experience designing and deploying algorithmic trading strategies.\
\Experience applying reinforcement learning or Bayesian optimisation to financial use cases.\
\Understanding or substantial interest in financial markets — preferably with exposure to crypto markets, De Fi, or trading systems.\
\Familiarity with on‑chain data extraction and analysis (Web3 / crypto).\
\Prior work experience in a hedge fund, prop trading firm, or crypto trading desk.\
\What’s in it for you?
\Founding team member opportunity — Be one of our first hires, with direct influence on our trading models from day one.\
\High‑impact projects — Your work will directly shape live strategies used by top‑tier investors managing millions.\
\Fast‑track career growth in a high‑performance environment where your impact is visible and rewarded, and where you scale into leadership roles as the company and tech expand.\
\Meaningful equity package — Build this with us and share in the upside.\
\Be part of a top‑tier fintech startup — Backed by leading investors and advisors, building the future of digital asset investing.\
\High ownership and autonomy — Lead, build, and ship — no layers, no bureaucracy.\
\We’re committed to investing in your future—whether it’s funding courses, certifications, or other educational opportunities, we want you to continually grow and excel.\
\As one of our first Quantitative Engineers, you’ll begin by owning core research and model development, evolve into Quant Lead, and later take on a Head of Quant Trading role as the team expands. You’ll help define our research agenda, shape the next generation of Iqana strategies, and mentor new team members as we scale our infrastructure and client base.\
\Compensation
\Base Salary Range: €38,000 – €52,000 gross annual + 10% bonus.\
\Equity Package: Meaningful equity package — share the upside as we grow from Pre‑Seed to global scale.\
\Total Compensation: Competitive and designed to grow as we scale, with compensation reviews twice per year.\
\Quantitative aiml engineer • Madrid, Spain
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