Member of Technical Staff Intern

Role overview Complete one bounded technical project in an assigned area, with a defined user, deliverable, acceptance test, reviewer, and handoff. Possible areas span software, AI systems, inference infrastructure, hardware and materials tooling, manufacturing data, and technical education. The project is selected to fit current work, demonstrated ability, supervision, and access and safety constraints.

Responsibilities - Start from a written brief that defines the problem, expected result, interfaces, constraints, and acceptance criteria. - Build a useful artifact in your assigned lane, such as software, tests, a benchmark, simulator, data pipeline, experiment tool, verification harness, visualization, or lesson. - Use AI tools to support learning and development while remaining accountable for testing and correctness. - Inspect relevant code, documentation, traces, papers, specifications, experiment records, or process data. - Trace system behavior and clearly distinguish source-backed, fixture-backed, simulated, and live results. - Document methods, results, failures, limitations, and evidence, then demonstrate and hand off work others can reproduce.

Requirements - Current bachelor’s, master’s, or PhD student, or recent graduate, with strong fundamentals and evidence of building, experimenting, or researching. - Ability to learn unfamiliar tools, ask precise questions, and complete a scoped project. - Code, research, experiments, writing, or other projects that show curiosity and ownership. - Comfort reading existing code and documentation before proposing changes. - Testing and debugging habits suited to the project area. - Clear written communication and willingness to receive technical review.

Nice to have - Experience with agent systems, evaluation, developer tools, full-stack or backend development, data, or infrastructure. - Familiarity with model serving, routing, caching, profiling, inference systems, compilers, GPU kernels, FPGA, drivers, architecture, or verification. - Background in materials science, semiconductor processing, packaging, metrology, reliability, DOE, or manufacturing data. - Experience with research communication or technical education.