• Train, develop, and deploy state-of-the generative AI models like LLMs and diffusion models using NVIDIA's AI software stack • Leverage and build upon the torch 2.0 ecosystem to analyze and extract standardized model graph representation • Develop high-performance optimization techniques for inference • Collaborate with teams across NVIDIA to use performant kernel implementations within our automated deployment solution • Analyze and profile GPU kernel-level performance to identify hardware and software optimization opportunities • Continuously innovate on the inference performance

• 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.

Role Description 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. Qualifications - 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. Nice to Have - 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. - 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.

• 2–4 years of hands-on experience in software/data/ML engineering in production environments • Very strong Python - clean, production-quality code (not just notebooks); sharp problem-solving and the aptitude to pick up MLOps practices quickly • Experience building and deploying APIs/services (FastAPI, Flask, or similar) and working knowledge of AWS (EC2, S3, Lambda) for hosting and serving • Understanding of the end-to-end ML lifecycle - training vs inference pipelines, deployment, and monitoring • Working knowledge of SQL and familiarity with PySpark; exposure to Databricks or comparable platforms, with the ability to read, refactor, and convert code for other environments • CI/CD and version-control fundamentals (Git, testing, rollback); familiarity with Docker; strong ownership and comfort with a broad, evolving scope and global stakeholders • Prior hands-on MLOps tooling experience (MLflow, model registries, drift detection, ML observability) • Experience supporting GenAI or LLM workloads operationally (model serving, inference pipelines, cost/performance tuning)