• Evaluate and select the appropriate GPU computing technologies and frameworks (e.g., CUDA, Kokkos, or other modern GPU programming models) based on performance, portability, maintainability, and long-term architectural goals. • Design and implement the GPU computing layer within our desktop software stack, introducing GPU acceleration for computationally intensive workloads. • Integrate GPU development into the existing build and tooling ecosystem, including configuring the build system, dependency management, CI/CD workflows, and developer tooling to support GPU targets. • Port and optimize mesh processing algorithms and other performance-critical components from CPU implementations to GPU-accelerated implementations. • Analyze performance bottlenecks and apply GPU optimization techniques such as memory layout optimization, kernel design, and efficient data transfer between CPU and GPU. • Establish best practices, documentation, and architectural guidelines for GPU development to enable maintainable and scalable use of GPU acceleration across the codebase. • Collaborate with other engineers to identify additional opportunities for GPU acceleration and ensure seamless integration with the broader application architecture.

• Actively participate in the full lifecycle of solution conception and development, from the Discovery process, helping to identify the right problem to solve. • Contribute to technical refinements to structure solution designs. • Collaborate with interdependent squads to ensure alignment and integration of solutions. • Develop frontend (React) and backend (.NET Core) layers as needed, including unit tests to ensure quality and security. • Ensure proper configurations for DevOps processes, planning and monitoring production deployments. • Monitor the post-release environment to ensure stability and performance. • Apply advanced AI techniques using tools such as Python (LangChain, LangGraph), RAG, prompt engineering and agent evaluation (EVALs). • Work with AWS infrastructure (DocumentDB, Lambda, ECS, S3, Redis) and CI/CD tooling (GitHub Actions, Terraform, Docker). • Collaborate in the development and integration of intelligent agents; knowledge of MCP and Go is a plus.

• You’ll help design and operate the systems that power our financial platform. • Design and implement scalable distributed systems supporting financial transactions and real-time events • Architect services that handle real-world scale, failure scenarios, and performance constraints • Ensure strong data integrity and consistency across distributed workflows • Build secure infrastructure protecting sensitive financial data • Identify opportunities to leverage AI tools to accelerate development and improve engineering workflows • Contribute to AI-enabled product features and automation across the platform • Help evolve how our team integrates modern developer tools and AI-assisted engineering practices • Design systems with observability, monitoring, and failure recovery in mind • Debug complex production issues across distributed services • Improve reliability and performance of mission-critical systems • Participate in on-call rotations to maintain platform stability • Partner with product and business teams to translate customer needs into scalable technical solutions • Lead architectural decisions through hands-on implementation • Mentor engineers and help raise engineering standards across the team

• Contribute to and influence architecture decisions across our stack, helping establish patterns the team builds on • Own complex product challenges end-to-end—from technical design through shipping to end users • Support and grow other engineers through code reviews, design feedback, and pairing • Partner with Product, Design, and Clinical teams to translate clinical workflows into scalable solutions • Identify and help drive improvements to technical debt, reliability, and developer experience • Actively use AI tools in your daily workflow and contribute to team-wide best practices • Our tech stack: TypeScript, Node.js, React, Postgres, AWS, ECS