Software Engineer, Infrastructure & Data

Mecka•New York, NY

1d•$170,000 - $200,000

About The Position

We're hiring a Software Engineer, Infrastructure & Data to design and build the backend systems and data pipelines that power Mecka's platform. This is a high-performance systems role focused on end-to-end data pipelines, distributed compute, and the tooling that enables continuous model evaluation at scale. You'll work across the stack of infrastructure — from storage and transport to compute orchestration and inference — building systems that are fast, reliable, and built to handle serious throughput. This role is ideal for someone who obsesses over performance, understands distributed systems deeply, and wants to work on infrastructure that directly enables cutting-edge robotics and AI research.

Requirements

4+ years of backend or infrastructure engineering experience
Proficiency in one or more of: Rust, TypeScript, or Go — with a track record of building high-performance systems
Experience designing and operating distributed compute and data pipeline systems
Hands-on experience with orchestration tooling (Temporal, Kafka, or equivalent)
Strong systems thinking — you reason about performance, reliability, and scale from first principles

Nice To Haves

Experience with CUDA kernels or GPU-accelerated compute
Understanding of AI inference optimization — model serving, quantization, batching, hardware utilization
Experience building model evaluation pipelines for computer vision, robotics, or ML teams
Familiarity with video codecs, compression pipelines, or object storage optimization
Experience in research-adjacent or AI infrastructure environments

Responsibilities

Design and optimize end-to-end data pipelines for large-scale data ingestion, transformation, and delivery
Build for high throughput and low latency across the full data path — from capture to storage to consumption
Own reliability and observability across pipeline infrastructure
Architect and operate distributed compute systems for large-scale data processing workloads
Implement and maintain orchestration using tools like Temporal, Kafka, and similar systems
Build fault-tolerant, scalable pipelines that handle real-world operational load
Build continuous and large-scale model evaluation tooling for robotics, computer vision, and machine learning teams
Optimize systems for AI inference — latency, throughput, and resource efficiency
Work closely with ML and robotics teams to understand evaluation requirements and build the infrastructure to support them
Optimize systems at every level — compute, memory, I/O, and network
Contribute to CUDA kernel development and GPU-accelerated workloads where needed
Identify and eliminate bottlenecks across the data and compute stack