Virtual Computer Vision Engineer Positions

Virtual Computer Vision Engineer, full-time, fully remote, $140,000 a year, open to candidates anywhere.

Skills

• Python
• OpenCV
• PyTorch or TensorFlow
• Image processing
• Deep learning
• C++
• Model optimization
• Cloud deployment

C++ shows up more than you'd expect for a role that's mostly Python day to day. Real-time vision systems often need a performance layer that Python can't provide on its own, and the ability to read and modify C++ code, even without writing large amounts of it, matters when a model needs to meet a hard latency target. OpenCV comes up in ways that go beyond preprocessing images before they reach a model. A surprising amount of practical vision work involves classical techniques, edge detection, contour analysis, basic tracking, layered around the deep learning pieces rather than replaced by them entirely.

Responsibilities

• Design and train models for image recognition, object detection, and video analysis
• Optimize models for real-time performance, not just accuracy on a benchmark
• Integrate vision systems into production applications that run reliably outside a lab environment

An object detection model on one project hit 95 percent accuracy in testing and looked ready to ship. Once it reached the actual camera hardware, though, frame rate dropped low enough that objects were slipping through the gaps between processed frames. The model itself hadn't gotten worse. Running it on constrained edge hardware exposed a completely different bottleneck than the one accuracy metrics had been measuring, and solving it took real optimization work, not a bigger training dataset. Integration work is where many theoretically sound models actually fail. A model that works well on a static test set can behave unpredictably when pulling frames from a live camera feed with variable lighting, network jitter, or hardware that occasionally drops a frame. Building for that variability, rather than assuming a clean input stream, is a key part of successfully getting a vision system into production.

Experience and education

This role requires 30 months of demonstrated experience building image or video processing models. On the education side, a bachelor's degree is the baseline requirement, typically in computer science, electrical engineering, or a closely related field. A number of candidates at this level also hold a master's degree, though it isn't required, and applied deployment experience carries real weight compared to an advanced degree alone. Proficiency in Python and at least one major deep learning framework is expected coming in. Candidates who've mostly worked with pre-trained models through high-level APIs, without much exposure to training or optimizing models directly, will find the technical bar here higher than that kind of experience typically prepares someone for.

Pay and benefits

This role pays $140,000 a year. It comes with retirement plan matching, time off, and health coverage, as well as remote-work flexibility and a stipend for hardware or GPU compute access. That last one matters more here than in many remote roles, since testing a model against real camera hardware or with heavier compute than a laptop can handle is a routine part of the job, not an occasional exception. Naukri Mitra is handling recruitment for this opening, and the hardware stipend has previously covered dedicated GPU access for training runs and specialized cameras for testing edge-deployment scenarios. Raises are reviewed annually and tend to reflect the difficulty of what someone's shipped, rather than tenure alone. Getting a model that struggled with real-time constraints into a reliably performing production system tends to matter more in that conversation than incremental accuracy gains on something already stable. For anyone comparing computer vision engineer remote salary figures at the two-and-a-half-year mark, this offer sits above the median, reflecting the role's real-time and edge-deployment focus over a purely research-oriented computer vision position.

How the work is structured

This team splits its attention between developing new vision models and getting existing ones to run reliably on the hardware they'll actually ship, which is a heavier deployment focus than many computer vision engineering jobs worldwide, which stay closer to research. A model that performs beautifully in a Jupyter notebook and fails once it hits real camera input under real lighting conditions is treated as unfinished work, not a success that just needs a small follow-up. Testing against real-world footage matters more than testing against curated datasets alone. Lighting changes, motion blur, and unusual camera angles show up constantly in production and rarely in a clean benchmark set, and building that kind of messy, realistic testing into the normal workflow is part of what keeps deployed models from breaking the first time conditions shift from what training data assumed. Optimization work is ongoing rather than a final step before launch. A model that meets its latency target on launch day can drift as hardware ages, as camera resolution changes, or as new features are layered onto an existing pipeline, and periodic performance reviews are expected rather than treated as a sign that something went wrong originally.

Applying

Send a resume along with a short description of a computer vision model you took from development to production, including one performance or deployment issue that only appeared once it was running on real hardware. Interviews include a technical round on model design and optimization, followed by a system design conversation focused on real-time deployment constraints.