Senior Software Engineer, ML Systems

Industrial labor powers our world and protecting the people who do that work is both a moral imperative and a business advantage. At Voxel, we're transforming workplace safety and operational efficiency through AI-driven computer vision technology that helps organizations see unseen risks before incidents occur.

Voxel's platform enables Fortune 500 safety and operations leaders to uncover root causes, drive proactive risk reduction, and make smarter, data-driven decisions. Our Solutions Engineering team ensures customers successfully adopt, deploy, and scale Voxel across complex enterprise environments.

• 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

• 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

• Experience with the full CV data lifecycle: collection, sampling, labeling, and curation

• Experience with auto-labeling or embedding-based data search

• Exposure to customer deployments or field-facing ML systems