CNTXT is building the data infrastructure powering the next generation of AI and robotics. We’re looking for a Computer Vision Engineer — Egocentric & Robotics Data Pipelines to build and operate the data infrastructure behind large-scale robotics and egocentric AI datasets, transforming raw sensor data from GoPro cameras, ZED stereo rigs, Vision Pro headsets, and robotics platforms into clean, validated, richly annotated datasets.
About CNTXT – Data Infrastructure for Frontier AI
Mission: Scalable platforms transforming raw multimodal sensor data into high-quality AI training datasets
Technical Domains: Computer vision, robotics, distributed systems, and machine learning infrastructure
Data Sources: GoPro, ZED stereo rigs, PICO, Apple Vision Pro, mobile phones, and robotics platforms
Tech Stack: Python, Ray/Ray Data, Kubernetes (EKS), PyTorch, AWS, PostgreSQL
Standards Focus: Quality, reproducibility, and scalability across massive data pipelines
Career Growth & Cutting-Edge AI Exposure
Strategic Location: Abu Dhabi – an emerging hub for AI and robotics infrastructure development
Frontier Technology: Build systems that directly power next-generation foundation models
Cross-Disciplinary Exposure: Work across distributed systems, computer vision, and robotics
Career Growth: Competitive compensation with growth potential in a fast-scaling AI infrastructure company
Position Overview
This Computer Vision Engineer role sits at the intersection of distributed data processing, computer vision, robotics, and cloud infrastructure. You will design and maintain distributed data-processing pipelines using Ray/Ray Data, build dependency-aware workflows on Kubernetes, develop automated validation and quality-control systems for vendor datasets, productionize CV models including hand tracking and SLAM, standardize datasets into LeRobot, MCAP, and RLDS formats, and optimize throughput, partitioning, and fault tolerance across the pipeline.
Why This Role Matters: As Computer Vision Engineer at CNTXT, you build the data infrastructure that directly trains frontier AI and robotics foundation models, work hands-on with cutting-edge devices like Apple Vision Pro, ZED stereo rigs, and robotics platforms, productionize advanced CV techniques including SLAM, VIO, and hand tracking at scale, operate at the intersection of distributed systems, computer vision, and MLOps, and gain rare exposure to egocentric and robotics data infrastructure that few engineering roles offer.
Key Responsibilities
Distributed Data Pipeline Engineering
- Design, build, and maintain distributed data-processing pipelines using Ray/Ray Data
- Build dependency-aware workflows running on Kubernetes (EKS)
- Process large-scale video and multimodal sensor datasets
Data Validation & Quality Control
- Develop automated validation and quality-control systems for incoming vendor datasets
- Validate codecs, resolution, frame rate, FOV, IMU synchronization, camera calibration, metadata integrity, and provenance
Video & Sensor Data Processing
- Build pipelines for video transcoding, re-encoding, container parsing, and sensor synchronization
- Build dataset packaging and reliable ingestion and delivery endpoints
Computer Vision Model Productionization
- Productionize CV models including hand tracking, stereo triangulation, head pose estimation, VIO, and SLAM
- Standardize datasets into LeRobot, MCAP, Foxglove, and RLDS formats
Dataset Management & Optimization
- Manage dataset metadata, lineage, versioning, and reproducibility
- Optimize throughput, partitioning, fault tolerance, and scalability across pipelines
Qualifications & Requirements
Core Technical Requirements
- Strong Python programming skills
- Solid software engineering fundamentals (testing, Git, code reviews, modular code)
- Experience with distributed data pipelines (Ray, Spark, Dask, Beam, or similar)
Infrastructure & Systems Experience
- Understanding of throughput optimization, partitioning, and fault tolerance
- Hands-on Kubernetes experience
- Experience with video processing, multimedia containers, sensor/IMU data, or binary parsing
- Experience building scalable production data systems
Preferred Experience
- Production experience with computer vision models (hand tracking, pose estimation, depth, SLAM, VIO)
- PyTorch and ML model packaging experience
- Knowledge of robotics datasets, camera calibration, 3D geometry, and stereo triangulation
- Experience with MCAP, Foxglove, ROS, LeRobot, or RLDS
- MLOps/GitOps experience with ClearML, Argo CD, Helm, and CI/CD
About This CNTXT AI Infrastructure Opportunity
This Computer Vision Engineer role sits within CNTXT’s mission to build the data infrastructure powering the next generation of AI and robotics. The position combines distributed systems engineering, computer vision productionization, and data quality management, offering engineers the chance to work on cutting-edge egocentric and robotics datasets that directly train frontier AI models.
Career Excellence: Build the data infrastructure behind next-generation AI and robotics foundation models at a fast-scaling company in Abu Dhabi.
Who Should Apply?
- Computer Vision Engineers: Experienced in SLAM, VIO, pose estimation, or stereo triangulation
- Distributed Systems Engineers: With Ray, Spark, Dask, or Beam pipeline experience
- Robotics Data Engineers: Comfortable working with multimodal sensor and video data
- MLOps/Platform Engineers: Skilled in Kubernetes, ClearML, Argo CD, and CI/CD workflows
- AI Infrastructure Builders: Excited to work on data powering frontier foundation models
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