Helix AI Engineer, Backend Infrastructure (AI)

TL;DR

Helix AI Engineer, Backend Infrastructure (Backend): Architecting and scaling cloud backend systems for real-time streaming of media and sensor data with an accent on low-latency pipelines and high-throughput infrastructure. Focus on integrating ML model serving into real-time pipelines and ensuring system reliability for robot fleet operations.

Location: Onsite in San Jose, CA (5 days/week in-office collaboration required)

Salary: $150,000 – $400,000

Company

hirify.global is an AI Robotics company developing a general-purpose humanoid designed for commercial and home tasks.

What you will do

• Architect and scale cloud backend infrastructure for high-concurrency, real-time streaming of media and sensor data.
• Design low-latency data pipelines to ingest, route, and process high-bandwidth streams into the AI stack.
• Own reliability, latency, and throughput SLAs for streaming and data infrastructure.
• Collaborate with AI and robotics teams to integrate ML model serving into real-time pipelines.
• Build observability, alerting, and tooling for live robot traffic.
• Drive architectural decisions and mentor engineers across the team.

Requirements

• Deep experience scaling cloud backend systems handling high-concurrency, real-time data streams (media, sensor, telemetry).
• Strong fundamentals in distributed systems, stream processing, and low-latency architecture.
• Proficiency in one or more backend languages: Go, C++, Python, or Rust.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerized infrastructure.
• Strong communication and cross-functional collaboration skills.
• Must be based in or be able to work onsite in San Jose, CA 5 days per week.

Nice to have

• Experience integrating AI inference serving (Triton, TensorRT, SageMaker) into real-time pipelines.
• Background in robotics, autonomous vehicles, or latency-critical streaming domains.
• Familiarity with WebRTC, RTSP, gRPC, or Kafka for real-time data transport.
• Experience with on-device or edge inference and cloud vs. edge processing tradeoffs.