Member of Technical Staff Intern

Summary

Touchdown Labs is focused on building useful AI systems and conducting research across various technical domains. The Member of Technical Staff Intern will engage in a technical internship with responsibilities including building a project in assigned technical areas, utilizing AI tools for development, and ensuring the quality and correctness of the work.

Responsibilities

• Build one bounded project in an assigned lane across agent development, product software, backend systems, inference, developer tooling, kernels, FPGA or drivers, hardware systems, materials or manufacturing data, research tooling, or education
• Start from a written problem, user, expected result, interfaces, constraints, and acceptance test
• Use AI tools to learn, prototype, debug, and move faster while keeping responsibility for testing and correctness
• Read real repositories, documentation, traces, papers, specifications, experiment records, or process data instead of stopping at generated summaries
• Trace code and system behavior through evidence and label source-backed, fixture-backed, simulated, and live results correctly
• Write a clear handoff that explains what changed, how to run it, what the evidence proves, and what remains unproven
• A written project brief naming the user, problem, technical lane, interfaces, constraints, acceptance test, evidence state, reviewer, and delivery date
• A working artifact such as code, tests, a benchmark, simulator, data pipeline, experiment tool, verification harness, visualization, or interactive lesson
• A review packet with source links, commands or methods, results, failures, limitations, and the evidence that supports each claim
• A final demonstration and handoff that another engineer, researcher, or educator can reproduce and extend
• You ship one useful artifact that passes its defined acceptance test
• You can explain the full path from user problem to code, system behavior, evidence, and limitation
• Your tests and documentation let another person reproduce and extend the work
• You leave with deeper technical judgment, not only familiarity with one tool or model

Skills

• 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 finish a bounded project
• Code, research, experiments, technical writing, or projects that demonstrate curiosity and ownership
• Comfort reading existing code and documentation before proposing a rewrite
• Basic testing and debugging discipline appropriate to your area
• Clear written communication and willingness to receive direct technical review
• Agent systems, evaluation, or developer tools
• Full-stack, backend, data, or infrastructure engineering
• ML serving, routing, caching, profiling, or inference infrastructure
• Compilers, GPU kernels, FPGA, drivers, architecture, or verification
• Materials science, semiconductor processing, packaging, metrology, reliability, DOE, or manufacturing data
• Research communication or technical education

Qualifications

Must Haves

• 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 finish a bounded project
• Code, research, experiments, technical writing, or projects that demonstrate curiosity and ownership
• Comfort reading existing code and documentation before proposing a rewrite
• Basic testing and debugging discipline appropriate to your area
• Clear written communication and willingness to receive direct technical review

Nice to Haves

• Agent systems, evaluation, or developer tools
• Full-stack, backend, data, or infrastructure engineering
• ML serving, routing, caching, profiling, or inference infrastructure
• Compilers, GPU kernels, FPGA, drivers, architecture, or verification
• Materials science, semiconductor processing, packaging, metrology, reliability, DOE, or manufacturing data
• Research communication or technical education