Senior Product Manager - Algorithmic Trading

Kraken

Kraken is building the execution infrastructure that will power the next generation of professional and institutional crypto trading and, increasingly, the AI agents that trade on their behalf. This role sits at the heart of that mission: owning the Algorithmic Execution & Strategy Engine, Kraken s platform for complex order types, smart execution algorithms, and programmable strategy construction.

We are looking for a product manager who has lived and breathed electronic trading in a Tier 1 bank or algorithmic trading firm someone who has shipped execution algos, spoken FIX with quant developers, and understands why basis points matter. Your mission is to port the best of TradFi execution into crypto and build the platform that both humans and AI agents will rely on to execute with precision.

The opportunity

• Own and drive the roadmap for Kraken s Algorithmic Execution & Strategy Engine, defining the multi-year vision and aligning it with Kraken s institutional and pro-trading growth strategy.

• Design and deliver new execution algorithms TWAP, VWAP, Implementation Shortfall, Participation, scaled orders, and conditional order flows drawing on deep knowledge of TradFi execution best practice.

• Build out the strategy construction interface a programmable layer that allows professional traders and institutions to compose, test, and deploy execution strategies without writing code.

• Own the API and FIX protocol specification for programmatic access to the algo engine, working closely with engineering and client solutions to ensure low-latency, high-reliability access for institutional clients.

• Define transaction cost analysis (TCA) and execution quality frameworks; work with the data science team to instrument, measure, and continuously improve execution performance.

• Establish the agentic integration framework: design the interfaces and capability set that allow AI trading agents to interact safely and effectively with Kraken s execution infrastructure.

• Partner with Quant Research and Engineering to evaluate and integrate ML/AI-driven enhancements to the execution stack, including adaptive routing and predictive scheduling.

• Engage directly with institutional clients, prime brokerage relationships, and professional traders to surface execution needs and translate them into product requirements.