Viam

Viam

Lead Software Engineer, Data Platform

New York, NY · Full-time

Sponsorship not specified$220k-$250kDetected 43 days ago
PythonGoSvelteDistributed SystemsBackend DevelopmentFull-Stack DevelopmentMongoDBGCPAzureRESTMachine LearningTensorFlowComputer VisionPerformance ManagementRoboticsMentoring

About the role

  • New York City (Hybrid 3+ days per week in office)
  • The team works in Go and Python with a Svelte frontend, running ML workloads on GKE using TensorFlow, TFLite, and ONNX, with MongoDB Atlas, GCP, and Azure for the data layer.
  • You will report to the VP of Engineering.

Responsibilities

  • Lead Engineers at Viam own problems from product design through production and play an active role in shaping how the platform evolves.
  • Lead and develop a team of 5+ engineers: set direction, run planning, ship reliably, and grow the team through coaching, feedback, and performance conversations
  • Drive the reliability and performance of ML training and inference infrastructure, from custom training workflows to the services that power auto-labeling and model evaluation
  • Own the architecture of the data pipeline end to end, from device to cloud, including storage, querying, and the APIs that power the rest of the platform

Requirements

  • Ideally you have:
  • Experience with robotics or IoT is not required.

Skills

  • training workflows, labeling pipelines, and inference in both cloud and at the edge.
  • What You'll Own

Compensation

  • The salary for this role is between $220,000 - 250,000 /year.

Benefits

  • Own cross-team problems through to resolution, working closely with Computer Vision, Fleet Management, Mobile, and Solutions Engineering

Company info

  • We are looking for a hands-on engineering manager to lead our Data/ML team. The role is responsible for developing engineers, driving execution, and making architectural decisions.
  • We are looking for a hands-on engineering manager to lead our Data/ML team.

This listing is sourced directly from Viam's careers page and normalized into a canonical job model.