Senior Software Engineer, Voxel

• 5+ years building and shipping production ML or computer vision systems, with a track record of moving techniques from research to reliable product
• Strong Python; working knowledge of C++ or Go is a plus
• Experience with model serving and inference optimization (Triton, TensorRT, or similar)
• Experience with streaming or distributed data processing (Flink, Kafka, or similar)
• Solid grounding in evaluation: building datasets, defining metrics, and detecting regressions before customers do
• Familiarity with Kubernetes, Docker, and infrastructure as code (Terraform, ArgoCD); AWS experience
• Technical leadership without authority: you drive alignment across engineering, platform, GTM, and data ops through clear thinking and communication
• Ownership: you follow a feature past "the model works" to "customers trust it"
• Pragmatism: you know when a research result is ready for production and when it needs more work
• Mentorship: you raise the bar for teammates through design reviews, code review, and sharing context
• Comfort with ambiguity: you help shape an architecture in flux and can validate it with a fast proof of concept

Desirable

• Our perception team turns raw video into reliable, customer-facing incidents
• We're re-architecting our perception system around stream-based processing, and we need an engineer who can take research-grade computer vision techniques and make them work in production: scalable, observable, and integrated cleanly into the wider product
• You'll be a technical leader on the team, setting direction through design and execution rather than people management
• Turn research ideas (transformers, ensemble models, new detection and classification approaches) into production features, from prototype through evaluation, rollout, and iteration
• Design and build perception pipeline components on our stream-processing architecture (Flink/PyFlink), working with the platform team on model serving, configuration management, and orchestration
• Own and improve production CV models, including training, evaluation, and inference optimization (Triton, TensorRT, multi-backend serving)
• Design the logic that converts model outputs into trustworthy incidents: tracking, filtering, probabilistic reasoning, and state machines
• Build observability into everything you ship: metrics, dashboards, and evaluation loops that show whether a model or pipeline is working in the field
• Reduce end-to-end latency and cost while keeping detection quality high
• Partner with GTM, customer success, and data operations to plan customer rollouts, tune deployments, and improve labeling and review quality
• Evaluate and integrate third-party ML tooling (orchestration, labeling, dataset curation) and drive adoption across the team
• Write clear design docs and requirements, and de-risk major changes with proofs of concept before full buildouts
• You'll have real influence over the architecture of a system that's being rebuilt, with a direct line from your work to customer outcomes