Senior ML Engineer / ML Lead

• Zero bureaucracy, high autonomy, and the chance to work alongside people who share a scientific approach and a commitment to high-quality engineering.

• The opportunity to directly influence the product and see the results of your work reflected in real financial outcomes.

• Experience in algorithmic trading or deep domain expertise in cryptocurrency markets.

• Proficiency in C++ (specifically for reading and optimizing latency-critical code paths).

• Strong academic background: competition accolades, Kaggle achievements, scientific publications, or graduation from a top-tier DS program (e.g., SHAD).

• Interest in mentoring and team development.

We are looking for a Senior ML Engineer / ML Lead to join the small, elite team of engineers and researchers building ML-driven trading strategies in the cryptocurrency markets. The system is currently live in production, showing strong results, and scaling rapidly. We are looking for an exceptional expert to take ownership of the product, evolve our architecture, and exponentially scale the profitability of our models.

You will be working directly on real PnL. This position is designed for someone who wants to see the direct financial impact of their work, thrives in an environment of high autonomy, and is committed to writing high-performance code and building sophisticated mathematical models.

Responsibilities

• Develop and optimise the ML architecture for our trading strategies.

• Drive the full research cycle: independently generate hypotheses, implement them into production, and monitor real-world performance/profitability.

• Refine and improve existing ML models to boost effectiveness.

• Identify and eliminate bottlenecks in data processing pipelines and execution strategies.

• Python (primary language)

• NumPy, numba, PyTorch / JAX

• Experience in a Senior ML Engineer or ML Lead role.

• Proven track record of developing profitable ML models or trading strategies.

• Fundamental knowledge of mathematics, statistics, and probability theory.

• Ability to operate in high-uncertainty environments: formulating your own hypotheses and validating them against data.

• Experience in writing high-performance, production-ready code.