AI Researcher

Smart Bricks; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Smart Bricks, a frontier AI lab in San Francisco, is seeking an AI Researcher to advance our model and intelligence layer, taking ideas to production and focusing on scalable architectures and domain reasoning.

You will own a research agenda, collaborate with engineering to deploy research, and contribute to the vision of intelligent autonomous systems across assets and markets.

Qualifications

• Strong fundamentals in deep learning with practical experience in PyTorch and transformer models.
• Experience with graph neural networks and related frameworks.
• Proven ability to own and execute long-running research projects autonomously.

Responsibilities

• Research model architectures for prediction, classification, and decision-support on structured, unstructured, and visual data.
• Advance graph neural network architectures for large-scale relational knowledge graphs.
• Improve LLM layers with fine-tuning, RLHF, and retrieval-augmented generation for domain reasoning.
• Investigate cross-domain model transfer to improve cold-start performance.
• Publish and present findings to drive model improvements across the team.

Skills

Deep learning

PyTorch

Transformers

Graph neural networks

Education

PhD in Computer Science, Mathematics, Statistics, or related field

Job description

Smart Bricks is a frontier AI lab building autonomous reasoning systems that allow capital to discover, evaluate, and transact assets end-to-end. We sit at the intersection of frontier AI research and one of the world's largest and most data-rich industries - and we are building infrastructure that compounds in capability with every decision the system processes. Research here is not academic - it goes directly into production and has real consequences at scale.

About The Role

As an AI Researcher at Smart Bricks, you will advance the research agenda of our model and intelligence layer - discovering approaches that work at scale and taking them from idea to production. We are looking for people who want to work on problems that are both technically interesting and commercially consequential. You will own a research agenda, collaborate closely with engineering to take your work to production, and contribute to a broader vision of what intelligent autonomous systems can do.

What You Will Work On

Researching and developing new model architectures for prediction, classification, and decision-support tasks operating on proprietary structured, unstructured, and visual data

Exploring and advancing graph neural network architectures for large-scale relational knowledge graphs - modelling entities, relationships, and temporal dynamics at scale

Advancing our LLM layer - investigating fine-tuning approaches, RLHF methodologies, and retrieval-augmented generation for domain-specific reasoning and explainability

Researching cross-domain model transfer - understanding how models trained in one context generalise to another, and designing architectures that improve cold-start performance

Exploring multimodal learning - integrating image, text, and structured data signals into unified intelligence representations

Publishing and presenting findings internally to drive model improvement and shape research direction across the team

We Expect You To

Have a track record of coming up with new ideas or improving upon existing ideas in machine learning - demonstrated through publications, projects, or production implementations

Be able to own and pursue a research agenda - choosing impactful problems and driving them to completion autonomously over long-running projects

Have strong fundamentals in deep learning with comfort in PyTorch, transformers, and graph neural network frameworks

Be excited about research that ships - not just research that publishes

Communicate research clearly to audiences with different backgrounds - from fellow researchers to engineers to non-technical stakeholders

Nice To Have

PhD in Computer Science, Mathematics, Statistics, or a related field

First-authored publications at peer-reviewed ML conferences or journals such as NeurIPS, ICML, ICLR, or ACL

Experience with graph neural networks, geospatial ML, or multimodal learning

Background in applied AI research in a production or commercial setting

Experience with reinforcement learning from human feedback (RLHF) or preference optimisation

Why Smart Bricks

Solve Hard Problems:Work on AI systems that are live in production - agentic orchestration, real-time inference, cross-market model transfer, and retrieval systems operating at scale on proprietary data that doesn't exist anywhere else.

Build What's Next:The infrastructure we are building sits at the frontier of applied AI. The models, agents, and reasoning systems you work on here will define how one of the world's largest asset classes operates for decades.

Ownership and Impact:Small team, no bureaucracy, high trust. Your work ships, your decisions matter, and your fingerprints are on everything we build.

Learn from the Best:Collaborate with world-class engineers, researchers, and operators who left careers at leading AI labs and financial institutions to build something genuinely new.