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GenLogs is a transportation-technology company building next-generation truck intelligence through a nationwide network of sensors and proprietary data. The company delivers real-time, high-fidelity insights into freight movement for commercial supply-chain customers and public-sector agencies, operating at the intersection of edge sensing, computer vision, AI-driven analytics, and large-scale field deployment.
The Data Science team transforms raw observational data from the sensor network into high-value intelligence used by law-enforcement agencies, regulators, ports, and private-sector freight operators. The team builds models, analytics, and measurement frameworks that enable vehicle detection, entity resolution, behavioral insights, fraud and theft indicators, compliance signals, and network-wide operational performance metrics.
As a Computer Vision Engineer, you will own computer-vision problems end-to-end from photons entering a roadside camera to the structured vehicle intelligence delivered by the platform. This is not a role where you train a model and hand it off; you remain accountable for understanding the entire system: camera placement and configuration, image quality, training data, model architecture, edge inference, production deployment, monitoring, and downstream outcomes.
The operating environment is unforgiving. Trucks move at highway speeds through darkness, glare, rain, snow, occlusion, extreme perspectives, and inconsistent connectivity. Models must run reliably on constrained edge hardware across a geographically distributed sensor network. Improvements that look promising offline must survive actual roadside conditions and measurably improve the intelligence customers receive.
You will have autonomy to attack these problems wherever the evidence leads. One day that may mean designing a better OCR or detection model; the next may mean profiling a TensorRT pipeline, diagnosing video compression artifacts, redesigning an evaluation dataset, or working with field teams to change exposure, shutter speed, lighting, or camera positioning.
Key responsibilities include: owning computer-vision capabilities from problem definition through field deployment and sustained production performance; building systems for vehicle detection, tracking, OCR, attribute extraction, embedding generation, and re-identification; establishing evaluation frameworks that connect model performance to successful vehicle identification and customer outcomes; diagnosing failures across the entire imaging pipeline; developing models and production software to run them reliably at scale; optimizing multi-model pipelines for latency, throughput, memory utilization, and accuracy on constrained edge hardware; designing experiments and analyzing failure modes; building monitoring and feedback loops that reveal model degradation and new failure modes; working directly with field operations to test camera configurations and validate improvements under real roadside conditions; partnering with platform and data engineers while remaining accountable for production delivery; improving annotation strategy and training datasets based on observed production failures; evaluating new research and translating promising ideas into dependable systems; and raising the technical standard for computer vision across GenLogs through strong engineering, documentation, and mentorship.
**REQUIREMENTS:**
Technical Skills:
- Proficient in Python, SQL, OpenCV, and PyTorch programming
- Proficient in fundamental computer vision techniques including planar homography, keypoint detection, perspective projection, epipolar geometry, object tracking, camera calibration (intrinsic/extrinsic parameters), and re-identification
- Knowledgeable in GPU computing and inference optimization tools including CUDA, ONNX, OpenVINO, and TensorRT
- Knowledgeable in object detection, segmentation, optical character recognition, signal processing, linear algebra, and Vision Transformer models
Domain & Professional Qualities:
- Demonstrated ability to ship production systems, not just research prototypes
- Comfort with ambiguity and ability to define problems when requirements are unclear
- Ownership mentality: when something fails, you stay with it until the underlying problem is understood and resolved
