Job Posting - Principal Distributed Systems Developer for ML
Principal Distributed Systems Developer for ML
Job ID: 10052
Location: Downtown Montreal (Hybrid Option Available)
About T-RIZE:
T-RIZE Group is a leader in Real World Asset (RWA) tokenization and Federated Learning applications, bridging traditional finance and decentralized innovation. Our platform, projected to exceed $2 billion in TVL, provides institutional-grade tokenization for real estate, private credit, and other illiquid assets.
By leveraging smart contracts and digital workflows, we connect global asset owners and investors through compliant networks and decentralized liquidity channels, unlocking scalable investment opportunities and enhancing liquidity.
Our onchain intelligence integrates privacy-preserving AI (Federated Learning) to enable secure training on private data while ensuring compliance and confidentiality. Our T-RIZE Labs, an industrial research chair, provides access to expert research, advanced computing resources, and specialized researchers in decentralized machine learning, cryptography, and quantum computing through our partnership with ETS University. You will collaborate with Ph.D. researchers, postdocs, and highly trained industry leaders. We also partner with top-tier institutional entities engaged in a variety of complex and cutting-edge projects.
Join a team committed to democratizing access to high-quality assets and shaping the future of AI-driven decentralized finance.
Role Overview:
We’re seeking a Principal Distributed Systems Developer to develop our privacy-preserving decentralized AI infrastructure based on Federated Learning. Reporting to the Head of AI, you'll play a pivotal role in integrating AI with blockchain systems, designing secure, scalable AI models, and advancing onchain intelligence.
This role is ideal for someone who thrives at the intersection of AI and distributed computing, with a passion for privacy-first AI and real-world blockchain applications.
Expectations
Develop state of the art infrastructure for self-improving models, leveraging Lifelong Learning and adaptive architecture to stay accurate in dynamic real-world environments.
Architect and scale distributed AI systems, optimizing for federated learning in adversarial environments
Ensure AI security, privacy, and robustness, integrating adversarial defense mechanisms, homomorphic encryption, secure multiparty computation (SMPC), and differential privacy to build resilient training infrastructure.
Handle complex high throughput distributed systems to provide data for AI models leveraging Graph Neural Networks (GNNs), attention-based mechanisms, memory-augmented networks, and reinforcement learning with human feedback (RLHF) to enhance agent capabilities.
Key Responsibilities:
Design scalable infrastructure to support aggregation of locally trained models
Leverage decentralized computing to deliver robust and reliable infrastructure
Develop solutions leveraging smart contracts and zero-knowledge proof to improve verifiability and accountability
Develop an extensive suite of tests to ensure code functionalities are working as expected.
Participate in code reviews to ensure maintainable code and knowledge sharing.
Create technical documentation
Contribute to R&D efforts in decentralized AI by collaborating with researchers from the T-RIZE Labs.
Qualifications:
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PhD in Computer Science, AI, Machine Learning, or a related field. A Master’s degree in Computer Science, Engineering, or a relevant field with equivalent industry experience will also be considered.
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5+ years of experience in AI/ML development, knowledge of federated learning, decentralized AI, and privacy-preserving machine learning.
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High proficiency in Python, TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas.
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Experience with Solidity to write smart contracts
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Experience with distributed AI frameworks (e.g., Flower, Ray ).
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Familiarity with blockchain data analytics, smart contract integration, and EVM-compatible networks.
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Experience with MLOps. (i.e: MLFlow, Grafana)
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Experience with data visualization tools (e.g., Matplotlib, Seaborn, BI platforms).
Preferred Qualification:
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Thrives in a fast-paced environment, excels at multitasking, and remains focused under pressure and tight deadlines.
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Experience in DeFi, RWA tokenization, or AI-driven financial applications.
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Contributions to open-source federated learning frameworks.
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Familiarity with AI-driven governance models and decentralized identity (DID) solutions.
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Proficiency in French is a plus.
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Why Choose T-Rize:
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Make an impact in reshaping the financial system with innovative solutions leveraging distributed technologies.
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Pioneer a new evolution of AI systems which ends the data centralization to bring back privacy and data sovereignty.
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Competitive compensation with performance-based growth opportunities.
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Comprehensive health benefits, including medical, dental, and vision coverage.
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Flexible work arrangements, with hybrid and remote options.
Application Process:
**PLEASE MAKE SURE YOU QUALIFY FOR THIS POSITION BEFORE APPLYING**
Apply directly on LinkedIn or Submit your resume and cover letter to career@t-rize.io with the subject line:
"Principal Distributed Systems Developer for ML Application – [Your Name]"
Application Deadline: March 31, 2025
We sincerely appreciate all applications. However, due to the high volume of submissions, only candidates selected for further consideration will be contacted.