Software Engineer - Applied AI Research

Infotree Global Solutions; Warszawa; On-site; PLN 350,000 - 500,000; Full time

Job summary

Infotree Global Solutions Inc. in Warsaw seeks a senior Applied AI/ML Software Engineer to lead the engineering foundation for production-ready quantitative workflows. You will collaborate with researchers and traders to translate research into scalable, low-latency systems.

The role requires 5+ years in software engineering within finance or data-intensive domains, with strong Python/C++/Java skills and a focus on reliability and maintainability. Warsaw-based, full-time opportunity.

Qualifications

• Master’s or PhD in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Quantitative Finance, or related quantitative discipline (or equivalent deep industry experience).
• 5+ years of software engineering experience in finance, quantitative technology, or related data-intensive domains.
• Proven programming skills in Python, C++, Java and solid CS fundamentals (data structures, algorithms, concurrency).
• Applied AI/ML expertise: experience designing, training, evaluating, and deploying ML/DL systems.
• Hands-on experience with software supporting quantitative investment workflows and asset classes (Equities, Fixed Income, Multi-Asset).
• Engineering rigor: clean architecture, automated testing, CI, scalability, and maintainability.

Responsibilities

• From Hypothesis to Production: Conceptualize, prototype, and evaluate state-of-the-art AI/ML models for low-latency production apps.
• Architect Core Infrastructure: Design modular libraries, testing frameworks, and scalable infrastructure for research and deployment.
• Power Global Investment Strategies: Embed AI into high-impact workflows across Equities, Fixed Income, and Multi-Asset strategies.
• Drive Technical Excellence: Provide hands-on leadership, set code quality standards, and promote reliability.
• Cross-Disciplinary Collaboration: Partner with researchers, portfolio managers, and global engineering teams to solve complex market challenges.
• Industry Engagement: Represent the organization at top conferences and contribute to open-source initiatives.

Skills

Python

C++

Java

Education

Master’s or PhD in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Quantitative Finance, or related quantitative discipline

Job description

About Our Client

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.

The Challenge

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.

What You Will Do

• 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.

What You Bring

• Academic Foundation: Master’s 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.

Bonus Points

• 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).