Software Engineer

About Viam 

Founded by Eliot Horowitz, co-founder and former CTO of MongoDB, Viam is a novel robotics engineering platform that lets you configure, control, and manage robots intuitively and quickly. We’re inspiring a generation of engineers to solve complicated automation problems with our uniquely powerful suite of software tools.

We’re a ~100-person company headquartered in New York City.

The Engineering Challenge

Building software for machines introduces challenges that traditional software systems rarely face. Devices operate in real-world environments, networks are unreliable, and software must interact with hardware, sensors, and real-time data. At Viam, engineers build the platform and tools that make those machines programmable, observable, and manageable at scale.

About the Role

New York City (Hybrid 3+ days per week in office)

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.

The Data/ML team owns the infrastructure that moves data from devices to the cloud and makes it usable across the Viam platform. On top of that foundation, the team also owns the ML infrastructure that turns that data into models: training workflows, labeling pipelines, and inference in both cloud and at the edge. 

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.

What You'll Own

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
• Write code and ship features alongside your team across the full stack, from backend infrastructure through UI
• 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
• 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
• Have opinions on what gets built and how users experience it, and bring those into product decisions early
• Own cross-team problems through to resolution, working closely with Computer Vision, Fleet Management, Mobile, and Solutions Engineering