Platform Engineer

Harmon Eyes is the world's only open, AI-powered eye-tracking platform that identifies and predicts a person's cognitive, emotional, and physical state based on eye movements alone. Compatible with any camera-based device, Harmon Eyes provides a one-stop shop to analyze eye-tracking data, develop models, and deploy solutions to applications at scale with the Theia™ SDK. Backed by 60+ years of eye-tracking experience and born from the largest validated eye-tracking database (14 million unique records), Harmon Eyes is used by the world's leading organizations in technology, aviation/automotive, healthcare, and elite performance in military and sports.

As a Platform Engineer, you'll build and own the software that takes our ML models from research to production: the Theia SDK that runs real-time inference at the edge, the APIs and services our customers and partners integrate with, and the cloud infrastructure behind our data and model pipelines. You'll work closely with our ML engineers and researchers, so you'll need to understand how models are trained, evaluated, and deployed, but your core focus is building reliable, well-designed systems around them.

• SDK Development:
  Design, build, and maintain the Theia SDK in Python and C++, delivering real-time eye-tracking inference across desktop, mobile, embedded, and XR platforms. Own packaging, performance, versioning, and the developer experience.
• API & Backend Development:
  Design and implement APIs and backend services for data ingestion, model serving, licensing, and customer integrations, with a focus on clean interfaces, reliability, and security.
• Cloud Engineering:
  Build and operate infrastructure on AWS, including containerized services on Kubernetes, infrastructure-as-code, CI/CD, and monitoring.
• Full Stack:
  Build internal tools, dashboards, and customer-facing web interfaces that make our data and models accessible.
• Data Science Engineering:
  Build and scale the pipelines that process millions of eye-tracking records for training, evaluation, and analytics. Clean, transform, validate, and analyze large tabular datasets, and build the tooling that lets our ML team explore and work with data efficiently.
• ML Productionization:
  Partner with ML engineers to deploy, optimize, and monitor models in production and at the edge, including model conversion, latency and memory optimization, and inference pipelines.

• 2–4 years of professional software engineering experience building production systems
• Strong Python skills, including packaging and distributing libraries
• Working familiarity with C++ (reading, modifying, and building C++ code in a cross-platform codebase)
• Experience designing and building APIs (REST, gRPC, or similar) and backend services
• Hands‑on experience with AWS, containers, and CI/CD
• Experience working with large tabular datasets in pandas (data cleaning, transformation, aggregation, and performance at scale)
• Working familiarity with machine learning: you understand the model lifecycle and have helped deploy or integrate ML models in a product
• Strong communication skills and comfort working across engineering and research teams

• Experience building and shipping SDKs or libraries used by external developers
• SQL and database engineering (schema design, query optimization, PostgreSQL or similar)
• Kubernetes and infrastructure-as-code (Terraform or similar)
• Frontend experience (React or similar)
• Edge or on-device inference (ONNX Runtime, TensorRT, TFLite, Core ML, or similar)
• Python compilation or distribution tooling (Nuitka, Cython, pybind
  11, or similar)
• Exposure to time‑series, sensor, or signal-processing data
• Interest in VR/AR, eye tracking, or human‑computer interaction
• 1 year experience in a research setting (developing or working with ML research domains)

• Remote role with room to grow into automation, tooling, and AI‑engineering work—not a pure manual‑QA seat
• Direct exposure to a novel technical stack: real-time eye‑tracking, ML pipelines, SDKs, and VR/AR integrations
• Tight feedback loop with senior engineering and leadership—the work you ship is felt across the company