• Migrate Informatica PowerCenter ETL workflows to Informatica Cloud (IICS) on Azure • Redesign and optimize ETL/ELT pipelines for cloud scalability and performance • Perform end-to-end development, testing, and validation of migrated data pipelines • Analyze and clean up Snowflake metadata and unused objects • Optimize Snowflake performance and cost using metadata (ACCOUNT_USAGE, INFORMATION_SCHEMA) • Monitor and fine-tune query performance and warehouse utilization • Collaborate with architects, operations, and cross-functional teams • Support deployment planning and ensure smooth handoff to support teams • Prepare technical documentation and knowledge transfer artifacts

• Collaborating with cross-functional teams to develop manufacturing strategies for optical products. • Overseeing the production of optical components and ensuring adherence to industry standards and specifications. • Conducting research and feasibility studies to optimize manufacturing processes and improve product quality. • Developing and maintaining documentation, including manufacturing instructions and standard operating procedures. • Working alongside the Operations, R&D, and NPI teams on projects & mass production. • Implementing quality control measures to ensure consistent and reliable output. • Analyzing data and performance metrics to identify areas for improvement and cost reduction. • Evaluating and selecting appropriate equipment and tools for manufacturing optical products. • Assisting in the testing and validation of new optical technologies and products. • Travel as needed for the business.

• Decomposing and modularizing build processes for reusablity across multiple projects • Debugging CMake, pip, and conda issues encountered in CI and local builds • Working on scripting and infrastructure to manage dependencies across various environments and build systems • Bringing up builds and CI across platforms (x86_64/arm64) and OSes (Linux/Windows/Mac) and other unreleased hardware and software • Working with engineering leadership to identify the support matrix and manage the scope of the build matrix • Automating scheduled work for all of the above

• Design and build learned evaluation pipelines that assess driving behavior using LLMs, VLMs, and multimodal models • Develop agentic workflows that chain model inference, retrieval, and structured reasoning to evaluate complex driving scenarios • Define evaluation-of-evaluation methodology — how do we know our learned evaluators are correct? • Build golden-set frameworks and calibration loops for learned metrics • Partner with AML (Alpamayo Logos) teams on model-specific eval needs (e.g., COT prediction quality, AML regression coverage) • Instrument evaluation systems with robust experiment tracking, A/B comparison tooling, and model versioning • Contribute to the team's transition from rule-based to learned evaluation: identify metrics and analyzers that are candidates for ML replacement and build the alternatives