Software Engineer Applied AI Research

Our client is a premier global investment bank and institutional powerhouse that sits at the very epicenter of worldwide financial markets. Renowned for its elite engineering culture and intellectual rigor the firm designs and operates the technology platforms driving billions of dollars in daily capital transactions. With a deep commitment to cutting-edge research they invest heavily in artificial intelligence and machine learning to redefine quantitative trading and market infrastructure. Joining their Warsaw technology hub means partnering directly with world-class quantitative researchers data scientists and engineers on industry-shaping systems.

Are you energized by bridging the gap between cutting-edge AI research and battle-tested production systems

We are seeking a senior-level Applied AI / Machine Learning Software Engineer to spearhead the engineering foundation for next-generation quantitative investment workflows. Operating at the dynamic intersection of AI/ML quantitative finance and high-scale systems architecture you will take innovative research concepts out of the lab and turn them into resilient high-throughput engines powering live trading and portfolio decision-making.

• From Hypothesis to Production: Conceptualize prototype and rigorously evaluate state-of-the-art AI/ML models engineering them into scalable low-latency production applications.

• Architect Core Infrastructure: Design and implement modular libraries testing frameworks and scalable infrastructure that streamline the quantitative research and deployment lifecycle.

• Power Global Investment Strategies: Embed AI solutions directly into high-impact workflows across Equities Fixed Income and Multi-Asset strategies.

• Drive Technical Excellence: Provide hands-on technical leadership set code quality standards and advocate for best-in-class software engineering and system reliability.

• Cross-Disciplinary Collaboration: Partner closely with quantitative researchers portfolio managers and global engineering teams to solve complex non-linear market challenges.

• Industry Engagement: Represent the organization at top-tier industry conferences and contribute to open-source initiatives where appropriate.

• Academic Foundation: Masters or PhD in Computer Science Machine Learning Mathematics Statistics Physics Quantitative Finance or a related quantitative discipline (or equivalent demonstrably deep industry experience).

• Proven Track Record: 5 years of software engineering experience building robust production-grade applications within finance quantitative technology or related data-intensive domains.

• Core Engineering Chops: Exceptional programming skills in Python C Java or modern object-oriented/functional languages backed by solid computer science fundamentals (data structures algorithms concurrency).

• Applied AI/ML Expertise: Practical experience designing training evaluating and deploying modern machine learning or deep learning systems.

• Market Familiarity: Hands-on experience developing software supporting quantitative investment workflows and a solid grasp of asset classes (Equities Fixed Income or Multi-Asset).

• Engineering Rigor: Deep commitment to clean architecture automated testing continuous integration scalability and maintainability.

• Demonstrated success taking novel ML/AI models from pure experimentation into high-availability production environments.

• Hands-on experience with financial time-series data massive structured datasets and portfolio analytics.

• Exposure to distributed computing cloud architectures or high-performance computing (HPC).

• A public footprint in the engineering/research community (open-source contributions technical blog posts or conference papers).