Senior Computer Vision Engineer

Pakket • New York City, NY

2d • Onsite

About The Position

Pakket builds remote control systems for warehouses. Specifically, we track customer’s forklifts, pallet jacks, people, and trucks across their facility IP cameras, project them onto a shared bird's-eye-view floor map, and stream live positions to a customer-facing backend. Our stack is a real-time Python pipeline (YOLO/Ultralytics detection, multi-object tracking, homography-based BEV projection), deployed on an on-site GPU edge server. The system runs live at customer sites today. We are looking for a hands-on builder who is interested in owning the perception and the core of the multi-camera tracking systems.

Requirements

• 4+ years of hands on experience building Computer Vision systems
• Real-time multi-camera video: RTSP capture, FFmpeg/PyAV decode, GPU (NVDEC) decode, frame pacing, backpressure.
• Object detection with YOLO/Ultralytics end-to-end: dataset prep, fine-tuning, and deployment; inference optimization (batching, ONNX/TensorRT, CUDA).
• Camera geometry and calibration: intrinsics + lens distortion, homography estimation/application, projection to a shared ground plane; strong NumPy/OpenCV/SciPy.
• Linux + remote GPU operations: SSH to an edge machine and a cloud VM, run systemd/Docker services.
• Experience building a system that ran in production on real video, and can speak to common failure modes.

Nice To Haves

• Warehouse/logistics or industrial CV domain experience
• Tracking objects across multiple cameras
• Small-model classification (MobileNet-style) and sequence smoothing (e.g. HMM).
• Video encoding/distribution: NVENC, RTMP/HLS, MediaMTX.
• Familiarity with a startup environment.

Responsibilities

• Build and improve the real-time pipeline: multi-camera RTSP capture, GPU decode, detection, fusion, and tracking.
• Improve tracking quality across cameras: association, track lifecycle, ID switches, occlusions, and track revival.
• Fine-tune, evaluate, and deploy detection models end-to-end — dataset prep through TensorRT/ONNX deployment.
• Optimize the compute budget: batching, quantization, latency/throughput on the edge GPU.
• Build the evaluation tooling that proves improvements: offline replay, ground-truth scoring (MOTA/IDF1/HOTA-tier metrics), and controlled A/B ablations.
• Own adjacent systems work: backend/API, deployment, observability, and internal admin tooling.
• Diagnose hard production failures from logs, replays, and captured data.

Benefits

• Meaningful equity
• Ownership/control of the core technical system
• Opportunity to work on any part of the stack
• Competitive salary
• Opportunity to help build the company from the ground up

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed