Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.
Experiment Lifecycle Tracking and Tooling: Design, build and deploy tools that own how a run is defined, launched, resumed, and killed. Build and operate experiment databases for code version tracking, data version tracking, reproducibility, and checkpoint ancestry.
Inference Fleet Orchestration: Design, build, and operate the serving control plane that accepts large volumes of concurrent client requests and assigns them across inference clusters. Develop cache-aware admission, routing, batching, and scheduling policies that improve cache locality, balance workload, protect tail latency, and keep the fleet highly utilized and reliable. Partner with ML Performance on model runtime, kernel, and per-worker throughput optimization.
Model Evaluation in CI: Design, build, and operate automatic model checkpoint evaluation systems on seeded rollout and policy-success suites, run per-change and nightly.
Data Pipeline Operations: Design, build, and operate high-performance, fault-tolerant, distributed backend services and event-driven systems for our large scale data processing pipeline.
Visualization Platforms: Design, build, and deploy experiment observability and dataset visualization platform(s) that provides interactive data visualization, progress tracking, search, and comparison.
End-to-End Ownership: Lead projects through the complete software lifecycle, including technical specs, implementation, CI/CD, on-call support, and production observability.
You have run experiment tracking at scale, logging video, 3D, and trajectory artifacts rather than only scalars.
You have built evaluation harnesses for generative or embodied models, where quality is a distribution rather than a pass/fail.
Full stack development experience with web-based front-end.
You have orchestrated ML workflows with Argo Workflows, Flyte, or Ray, and know where each one breaks.
You have built GPU-hour attribution that maps cluster spend back to specific experiments and teams.
You have contributed to open-source ML tooling, or published on evaluation or reproducibility methodology.
