Veeda AI - Internship
Requirements
• You have a Bachelor's degree or equivalent hands-on experience in Computer Science, Engineering, or a related technical field, or you are currently working toward one. • You have real depth in machine learning fundamentals (optimization, generalization, architectures) or in systems fundamentals (operating systems, networking, parallel computing), plus the intuition to know when theory breaks down. • You program fluently in Python and have built and debugged a non-trivial system end to end, on your own. • You can name which of our seven areas you want to work in and sketch, concretely, what you would try to build or measure there. • You have built or maintained scalable data pipelines, training infrastructure, or simulation tooling. • You have published or contributed to research on generative models for image, video, or 3D content, or on robot learning. • You have hands-on experience with GPU kernels (CUDA, Triton), a physics engine (MuJoCo, Newton), or real robot hardware. • You have contributed to an open-source project that other people use. • You have self-directed projects, research competitions, or competitive programming results that show how quickly you pick things up. • You can work hybrid from one of our offices — Toronto, Mountain View, Zürich, or Singapore — for the duration of the internship.
Responsibilities
• Own one scoped project in a single area — ML Performance, AI Infrastructure, ML Operations, Data, Robotics, Simulation, or World Models — with a mentor and a deliverable agreed in week one. • Representative Projects: Profile a step-time regression in a Megatron-Core run, add a task to an Isaac Lab environment suite, build a deduplication pass in Ray Data, or measure drift in a world-model rollout. • Real Systems, Not a Sandbox: Work in the same repositories, on the same clusters, and against the same data as the rest of the technical staff, not a parallel toy version. • A Deliverable That Outlives You: Ship one artifact the team keeps using — a dataset, a benchmark, an evaluation, or a tool — and write up what you found and what to try next.
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