Voxel

Machine Learning Engineer

• Set the technical direction for ML infrastructure at Voxel
• Architect and build the training infrastructure that lets the applied ML team run multiple experiments concurrently
• Own the train-to-deploy handoff
• Pick and roll out the experiment tracking and lifecycle stack
• Establish DevOps-for-ML best practices
• Mentor engineers across Vision & AI on ML infrastructure best practices
• Anticipate where the infrastructure needs to be in 12 to 18 months

Staff Software Engineer, ML Infrastructure

Full-stack Engineer

• Set technical direction for Voxel's vision and ML capabilities: what we build, how we build it, how we know it works.
• Hire and lead a small team of applied ML engineers. Set the bar, set the goals, set the culture.
• Stay in the code. Architecture decisions, model reviews, the occasional 2am debugging session.
• Make hard calls on tradeoffs: classical CV vs. end-to-end models, on-device vs. cloud, accuracy vs. latency vs. cost.
• Work directly with product, sales and customers. The loop from customer problem to shipped model runs through you.

Tech Lead Manager, Vision & AI

• Drive the roadmap for the data infrastructure that powers Voxel's vision and ML capabilities.
• Build petabyte-scale pipelines for ingestion, search, labeling, versioning and storage of camera data.
• Develop methods for data mining and automated data collection.
• Partner with applied ML engineers on dataset quality for training and evaluation.
• Collaborate with the Data Ops team (HITL) to design processes to measure and improve data quality.
• Understand the data needs of Vision and AI engineers and design scalable infra solutions that support model improvement and vision capabilities.
• Collaborate with the Platform team to store and retrieve data efficiently on the cloud.

Senior Software Engineer, ML Data

• Own data & labeling pipelines – architect scalable labeling services (storage, query, retrieval), design ontologies, automate annotation workflows, and build quality-tiered datasets that stay within cost constraints.
• Build and operate training infrastructure – create multi-GPU / multi-node training frameworks (Ray, Spark, Kubernetes), optimize distributed jobs, and integrate accelerators (TensorRT, CUDA-graph, FP8, etc.).
• Manage the full model lifecycle – stand up model registries, version control, evaluation suites, and continuous-learning loops that push updates from dev → staging → prod with zero-downtime rollbacks.
• Provide technical leadership, mentorship, and lightweight project management to a small infra + research squad.
• Establish DevOps-for-ML best practices (IaC, CI/CD, observability, cost monitoring) so researchers can iterate quickly and safely.
• Partner with ML engineers on architecture decisions, from data schemas to inference optimizations, ensuring infra and research road-maps stay tightly aligned.

Senior/Staff Software Engineer – ML Infrastructure

Product Designer

• work directly with our Head of Product Design and a small product team
• visit customer sites, build prototypes, and design experiences
• maintain regular customer conversations and bring back insights that shape the product
• enhance collaboration with engineers to ease their work

Engineer

• Ship for impact. Architect and write the code that makes Voxel work in the real world. You will build the end-to-end systems (frontend, backend, infrastructure) that unlock massive operational efficiency for enterprise customers.
• Integrate our computer vision models with messy, existing facility infrastructure and legacy data streams.
• Lead the end-to-end design of technical solutions, working directly with the Field CTO and enterprise customers.
• Rapidly prototype and iterate on product capabilities based on real customer feedback.
• Create the internal tooling, reusable components, and automated workflows that will allow the forward-deployed engineering team to scale rapidly.
• Document systems, tooling, and architecture to scale with the forward-deployed engineering function.

Founding Forward Deployed Engineer

• Contribute to the development and scaling of Voxel’s perception and computer vision systems, powering real-time understanding of industrial environments
• Design and deploy distributed systems for video ingestion, processing, and inference across both edge devices and cloud infrastructure
• Build and secure cloud APIs, access control systems, and backend services that support the video platform and data pipelines
• Develop scalable and resilient services that enable low-latency event detection, real-time alerts, and historical video analytics
• Collaborate with cross-functional teams—including ML engineers, product managers, and researchers—to bring computer vision models into production
• Influence the team’s technical direction and uphold high standards in code quality, system design, and operational excellence