Manufacturing and logistics are our strongest areas: defect detection, barcode reading, and dimensional checks on live lines. We’ve also delivered document and text recognition in the legal sector, including AI-powered legal document recognition for the Supreme Court, and we take on security and video analytics work. For medical imaging, we work as the engineering team alongside your clinical and regulatory specialists.
Hire computer vision engineers
Pre-vetted computer vision developers for industrial quality inspection, real-time video analytics, and object detection at line speed. C++, OpenCV, YOLO, PyTorch, deployed on the hardware you run.
Microsoft Partner · ISO 27001 / C++ + Python CV experts / Onboard in 2 weeks
Hire computer vision engineers
Most companies that set out to hire computer vision developers are looking for two different people at once. One trains models and knows PyTorch, augmentation, and how to read a confusion matrix, while the other knows what focal length and shutter speed do to a moving conveyor, why the reject actuator fires a few frames late, and how to get inference under 17 milliseconds on an industrial PC with no GPU. Job boards are full of the first. The second is what ships the project.
We supply engineers who cover both sides and who have done it on running equipment. You can hire computer vision team members one role at a time or take a full setup with a .NET integrator alongside. They work in your sprints and report directly to you. We handle sourcing, technical vetting, contracts, and replacement if a fit doesn’t work out.
Why hiring computer vision engineers is difficult
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Good engineers are hard to find
A computer vision engineer needs to think beyond the model. Camera position, optics, lighting, edge hardware, and inference speed can all determine whether the system works in production. An ML generalist may be able to fine-tune an object detector on a public dataset, but that does not necessarily help when the model starts producing false detections on the factory floor.
An experienced vision engineer can trace the problem back to something as simple as glare from shrink wrap and recognize that changing the lighting or adding a polarizing filter may solve it more effectively than collecting thousands of additional images.
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Lab accuracy is not the same as production accuracy
A successfully tested model can still perform poorly on the production line. The test images may have been captured in stable lighting, on a single product variant, through a clean lens. The real line runs shifts, daylight changes throughout the day, and packaging gets redesigned.
A held-out dataset tells you how the model performs on the data it was tested against. It does not tell you how well the system will handle changing conditions. A computer vision engineer must account for them to make the system ready for the factory floor.
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Speed requirements rule out generic solutions
At 60 frames per second, grabbing the frame, preparing it, running the model, and signaling the hardware has to finish in under 17 milliseconds. A Python prototype usually misses that window, because each library in the chain hands the frame to the next by copying it, and the copies eat the budget.
Hitting real time means removing them. The model is served through ONNX Runtime or TensorRT, quantized to run faster per pass, with the pipeline in C++ so one frame stays in one place from camera to decision.
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Isolated vision remains a pilot
A model that scores defects and writes the results to a local log does not change the operation. The result has to move into the systems that already run the process: SCADA, MES, ERP, or the .NET service responsible for production logic. From there, the system needs to act: reject a part, stop a line, trigger an alarm, or adjust a process parameter.
The prediction must be delivered in the right format, through an interface the existing system can use, and within the time available for the PLC or other control system to respond. A model can be highly accurate and still be useless on the production line if its output arrives too late, can’t be consumed by the existing software, or requires a manual job.
What our computer vision engineers build
Computer vision developers for hire
Our computer vision developers for hire build production-grade vision systems end to end: camera and lighting selection, dataset and annotation pipeline, model training, edge optimization, and the integration that puts results into your enterprise systems. Companies hire computer vision developers from us for the following kinds of work.
Industrial quality inspection
Defect detection, barcode and label reading, and dimensional verification for manufacturing and packaging lines. Our expertise comes from years of running PackTrack in live production, where vision systems must deliver reliable results at production speed.
Object detection and tracking
Real-time object detection and tracking using YOLO (v8 and v11) and custom CNN models. We build systems that detect, count, and track objects across frames for applications ranging from parcel and pallet handling to security and movement analysis.
Medical and document CV
Computer vision for medical imaging, OCR, document parsing, and form recognition. We build pipelines that can extract reliable information even from difficult source material, including skewed scans, stamps, mixed layouts, and handwritten fields.
Video analytics and surveillance
Continuous analysis of live and recorded video for motion, behavior, and anomalies. Our systems monitor multiple camera feeds, filter out irrelevant events, and alert people only when something requires attention.
Why hire computer vision engineers from Blackthorn Vision
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C++ and Python depth in the same team
Our engineers train models in PyTorch and deploy them in C++. We work across the full CV stack, from OpenCV and ONNX Runtime to TensorRT and embedded targets where memory, threading, and latency can directly impact performance. That means moving beyond a PoC to vision systems that can keep up with actual production workloads.
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Industrial CV proof
PackTrack is a machine vision quality control system we delivered. It runs barcode reading and visual inspection in industrial automation. It has been in continuous production use for years, adapting to hardware changes, framework upgrades, and product variants beyond the original training data. Our engineers bring that production experience to every industrial CV project.
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Vision output that lands in your enterprise systems
Blackthorn Vision is a Microsoft-partnered .NET and AI development company, so computer vision is not isolated from the rest of your software stack. The same team can train the model, build the C# service around it, and integrate its output with SCADA, ERP, MES, or your existing .NET backend.
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Edge AI deployment as a first-class skill
We optimize models for the hardware they actually need to run on. That includes ONNX Runtime, TensorRT, OpenVINO, INT8 quantization, and latency profiling on the target device rather than a workstation GPU. The goal is to deliver the required performance at the edge without adding infrastructure the system does not need.
Computer vision technology stack
All technologiesWe pick the stack from your constraints: frame rate, hardware budget, where inference has to run, and what the result has to talk to. A cloud API is a reasonable answer for document OCR and a poor one for a 60fps inspection station with no reliable uplink.
Model choice sets your ceiling on accuracy, and runtime choice sets whether you can use that model at all on the device in front of you.
Meet Our Computer Vision Engineering Team
“The hard part of industrial vision starts outside the lab. Lighting, packaging, and overall production conditions change all the time. We’d rather put you in front of engineers who have already dealt with those problems on a live production line.”
Mykhailo Terentiak, Founder & CEO
How we hire your computer vision engineers
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01
Technical Scoping
We go through the use case with you: what the camera sees, what hardware you have or can buy, the frame rate and latency the process demands, and which systems the output has to reach. Deliverable: a written scope with target latency, accuracy targets per defect class, hardware constraints, and integration points.
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02
Vetting
Candidates go through a CV system design session, a C++ or Python code review, and a production scenario where they have to reason out loud about a model that passed testing and failed on the floor. We ask every one of them how they’d hit a latency budget on given hardware. Deliverable: two or three vetted profiles with our written review notes.
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03
Client Interview
You interview the shortlist and choose. Technical interview, culture fit, whatever process you already run for permanent hires. Deliverable: your selected engineers with confirmed start dates.
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04
Onboarding
We bring them up to speed on your camera and hardware context, set up or plug into your annotation pipeline, and get them into your sprint rhythm. Deliverable: engineers in your standup from week two, with a first ticket closed inside the first sprint.
FAQ
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What industries do your computer vision engineers specialize in?
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Do you provide labelled training data or annotation services?
Yes, we set up the annotation pipeline in CVAT, Label Studio, or Roboflow, write the labelling guidelines, and can provide annotators for the bulk work. We also use synthetic data generation for defect classes that are too rare to collect sufficient real examples. Your own production images are still the most valuable input, so we usually start by helping you capture them properly.
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Can computer vision output integrate with our .NET or SCADA system?
Yes, our CV engineers work alongside .NET developers from the same company, so detections get into your SCADA, MES, ERP, or .NET backend as part of the same project. You avoid the standard handoff where a vision vendor delivers a model, and your internal team has to figure out the plumbing and timing.
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How do you handle accuracy requirements for industrial use cases?
We start by separating the error types. A missed defect that reaches a customer is usually far more expensive than a false alarm that costs an operator ten seconds, so we set the decision threshold against those costs instead of chasing a single accuracy number. Then we validate on data captured from your line across shifts and product variants.
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Can you build CV systems for edge deployment on embedded hardware?
Yes, we deploy to industrial PCs, embedded boards, and GPU edge devices using ONNX Runtime, TensorRT, and OpenVINO, with quantization and pruning where the accuracy budget allows.
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Do your CV engineers work with C++ or only Python?
We do model development and experimentation in Python with PyTorch. Anything with a hard latency budget, embedded deployment, or a tight memory ceiling gets written in C++ with the OpenCV C++ API and ONNX Runtime. A team that only writes Python will face problems with real-time industrial work, which is a large part of why we vet for both.
