Senior Software Engineer, ML Systems

Voxel•San Francisco, CA

1d•$200,000 - $240,000

About The Position

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.

Requirements

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

Nice To Haves

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
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
Strong analytical instincts — you make decisions from data, can build a pipeline model in a spreadsheet, and communicate performance clearly to senior stakeholders.
AI-forward mindset — active interest in applying AI tools to accelerate campaign development, audience targeting, content personalization, and channel optimization.

Responsibilities

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