Computer Vision Engineer

GenLogs is a transportation-technology company building the next generation of truck intelligence. Through a nationwide network of sensors and proprietary data, we deliver real-time, high-fidelity insights into freight movement for commercial supply-chain customers and public-sector agencies. Our mission is to strengthen America’s logistics backbone, combat freight fraud and cargo theft, and provide near-instantaneous visibility into commercial motor vehicle activity across major freight corridors. By operating at the intersection of edge sensing, computer vision, AI-driven analytics, and large-scale field deployment, GenLogs is transforming how transportation data is captured, secured, and commercialized.

ABOUT THE DATA TEAM

The Data Science team at GenLogs transforms raw observational data from the our sensor network into high-value intelligence used by law-enforcement agencies, regulators, ports, and private-sector freight operators. We build 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. Our work sits at the center of our platform, shaping how billions of roadside observations become actionable information. We partner closely with Engineering and Product to deploy algorithms at scale and with Go-to-Market teams to define customer-facing analyses that drive real operational outcomes. The team blends statistical rigor, ML capability, and domain expertise to create a new standard for freight intelligence in the United States.

ABOUT THE JOB

You will own computer-vision problems end to end from the photons entering a roadside camera to the structured vehicle intelligence delivered by our platform.

This is not a role where you train a model, publish an evaluation, and hand it to another team to productionize. You will be responsible for understanding the entire system: camera placement and configuration, image quality, training data, model architecture, edge inference, production deployment, monitoring, and downstream outcomes.

Our 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 our customers receive.

You will have the 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.

We’re looking for someone who doesn’t stop at “the model works.” You own the outcome.

WHAT YOU’LL DO

• Own computer-vision capabilities from initial problem definition through field deployment and sustained production performance.
• Build systems for vehicle detection, tracking, OCR, attribute extraction, embedding generation, and re-identification.
• Establish evaluation frameworks that connect model performance to successful vehicle identification and customer outcomes, not merely offline accuracy.
• Diagnose failures across the entire imaging pipeline, including camera placement, optics, exposure, lighting, encoding, training data, model behavior, edge compute, and downstream processing.
• Develop models and write the production software required to run them reliably at scale.
• Optimize multi-model pipelines for latency, throughput, memory utilization, and accuracy on constrained CPU and GPU edge hardware.
• Design experiments, analyze failure modes, and make clear decisions about what to improve next.
• Build monitoring and feedback loops that reveal model degradation, environmental shifts, hardware issues, and new failure modes.
• Work directly with field operations to test camera configurations and validate improvements under real roadside conditions.
• Partner with platform and data engineers while remaining accountable for getting your work into production.
• Improve annotation strategy, training datasets, and hard-example selection based on observed production failures.
• Evaluate new research, determine what is genuinely useful, and translate promising ideas into dependable systems.
• Raise the technical standard for computer vision across GenLogs through strong engineering, documentation, and mentorship.

QUALIFICATIONS

• 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
• Experience or interest in geospatial data analysis (e.g., GIS, GeoPandas)
• Experience with version control systems like Git
• Experience with linux systems
• Experience:
• Minimum 4 years of professional experience in computer vision, data science, or research
• Hands-on experience with applied computer vision techniques, especially on edge hardware
• Soft Skills:
• Strong problem-solving skills and attention to detail
• Excellent written and verbal communication skills
• Ability to communicate technical concepts to both technical and non-technical stakeholders

WHAT OWNERSHIP MEANS HERE

At GenLogs, ownership does not end when a pull request is merged or a model artifact is produced.

If a model performs well offline but fails in the