Senior Machine Learning Engineer

About the role

About us

Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production. Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more.

We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.

The role

We're looking for a Senior ML Systems Engineer to join Encord's in-house ML team. This is a software-engineering-first role for someone with real machine learning deployment experience: you'll design, build and run the infrastructure that serves our computer vision and multimodal models in production.

Our platform is moving from manual, GUI-heavy annotation towards ML-driven workflows, where models do more of the work and learn from annotator feedback. Your job is to make that possible by turning our models into fast, dependable product features.

Our ML team covers both research and engineering, and this role sits firmly on the engineering side. You'll own architecture, implementation, deployment and production operations, while teammates on the research side focus on longer-horizon experiments and prototypes. Day-to-day, you'll work closely with them, as well as with our Product Engineering and Human Data teams.

Problems you'll work on

- Interactive inference in the annotation editor. Serving heavy vision and multimodal models fast enough that annotators get model suggestions in real time, with their corrections flowing back as feedback.

- Inference at very large scale. Pre-computing model outputs across millions of hours of egocentric video for robotics customers, where throughput, GPU cost and reliability all matter.

- Taking agentic QC to production. Turning quality-control agents that check multimodal outputs against a customer's ontology from a research prototype into a dependable platform feature.

- One integration, many workflows. Building model integrations that hold up across customers' custom, fast-changing ontologies and task templates, instead of one-off implementations.

What you'll do

- Design, build and operate model-serving and inference infrastructure for transformer-based vision and multimodal models

- Own the serving architecture behind our ML features, justifying trade-offs in latency, throughput, reliability and GPU cost

- Optimise models and pipelines for production through batching, KV caching, GPU utilisation and orchestration

- Integrate models into Encord's product and annotation workflows together with Product Engineering

- Build reusable abstractions that work across models, data types, customers and changing task templates

- Set up monitoring and observability, and own performance and reliability once features are live