• Design, build, maintain and extend products, features, and functionality that solve real customer problems • Partner with Product, Design, and Engineering to discover and validate customer needs and technical approaches • Prototype quickly, and as necessary, to de-risk projects, test assumptions, and iterate ideas into production-ready solutions • Consistently deliver incremental value by anticipating dependencies, breaking down work, and regularly demoing progress • Communicate technical trade-offs, present system design proposals clearly, and document architectural decisions • Apply modern software engineering practices to deliver robust, maintainable, and extensible systems • Uplevel teammates through code reviews, pairing, and strong collaboration • Take ownership of your code and product domain, engaging in retrospectives and continuously improving how the team works

• Design, develop, and optimize cloud-based software solutions • Help drive the underlying technology stack and implementation methodology • Work closely with cross-functional teams to deliver high-quality cloud solutions • Mentor junior engineers, providing guidance on best practices and technical development • Identify and implement new technologies and methodologies to improve our cloud infrastructure and software development processes • Maintain a customer-centric approach by supporting, maintaining, and detailing software functionality

• Contribute to hardening a chemistry-to-quantum resource estimation pipeline: robustness, scalability, reproducibility, and “push-button” usability. • Implement and validate stable interfaces/data contracts between core modules (inputs/outputs/metadata/schema checks). • Help automate execution of a predefined benchmark suite across both cloud GPU environments and internal HPC clusters (job submission, retries, artifact collection, deterministic configs). • Support expansion of an end-to-end scientific computation workflow by implementing integration layers, validation, and test coverage for new upstream inputs, additional computational backends, and alternative algorithmic paths within a single, consistent execution framework. • Implement a representative application workflow that exercises the pipeline across a structured set of related inputs (parameter sweeps), automates repeated executions, aggregates outputs into analysis-ready artifacts, and documents key assumptions, approximations, and major sources of error/uncertainty. • Implement secure, automated sharing of HPC outputs to cloud object storage (e.g., S3) for downstream analysis and collaboration.

• Implement governance patterns for datasets and run outputs (naming, lineage, access boundaries, catalog/volume organization). • Improve dataset upload validation and guardrails to prevent accidental modification of unrelated storage paths and to enforce consistent metadata and file structure • Build monitoring and reporting for compute usage and cost drivers (job frequency, runtime, GPU utilization proxies, storage growth, auto-termination effectiveness). • Deliver dashboards that make platform health and spend understandable to both engineers and researchers. • Refactor existing job setup / submission scripts into a maintainable, testable OOP design (clear interfaces, configuration objects, reusable clients). • Improve workflow parameter handling for the two-stage pipeline (tensor factorization stage and QRE stage) and standardize outputs for downstream analysis. • Reduce onboarding friction by abstracting authentication and setup into a single, ergonomic path (e.g., a CLI/Python entry point that validates auth, environment, and required dependencies). • Replace “follow the guide manually” with automation: preflight checks, actionable errors, and self-serve setup validation.