Computer Vision: market

Vacancy

an open job that cleared the quality gate and lists at least two technologies

Observed candidate

a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter

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Window

jobs open at the moment the snapshot was built

Numbers based on a small sample — for broader benchmarks see ML Engineer and Research Engineer / Scientist. Junior — typical entry: MS / PhD CV / Robotics + portfolio (Kaggle CV competitions, GitHub CV projects). The Junior → Middle jump — after the first production CV deployment (detection / segmentation / classification feature shipped). Middle → Senior — multi-task CV pipeline ownership + edge deployment expertise or generative AI specialisation. Senior → Staff / Principal — org-wide CV strategy + custom architecture design + research-paper publication track. Career flow: Backend Senior / ML Engineer Middle (2-3 years) + CV interest + portfolio → CV Engineer Junior (1-2 years) → Middle (2-3 years) → Senior → either 3D Vision Engineer, Edge CV Engineer, Generative AI Engineer (image/video), or ML Research CV (academic-track papers CVPR / ICCV / ECCV).

Top 5: python, pytorch, c++, tensorflow, opencv. Python deep (mostly — edge components sometimes C++/Rust). PyTorch + torchvision mastery — 90%+ of CV research on PyTorch in 2026 (TensorFlow legacy). OpenCV mastery: classical CV ops (cv2.findContours / cv2.HoughLines / Canny edge / morphology / homography / camera calibration / stereo matching — must for preprocessing + traditional algorithms). Pillow (PIL — image manipulation). Object detection / segmentation: Ultralytics YOLO (YOLOv8/v9/v10/v11 — industry standard 2026 — easy training + good docs), Detectron2 (Meta — research-grade, more flexible), MMDetection / MMSegmentation / MMPose (OpenMMLab — huge ecosystem), DETR family (RT-DETR rising 2026), SAM + SAM 2 (universal segmentation). Self-supervised + foundation models: DINOv2 (Meta — best vision backbone 2024 for feature extraction), CLIP + OpenCLIP + SigLIP (vision-language alignment), MAE (Masked Autoencoder). Classification backbones: ViT family, Swin Transformer, ConvNeXt, EfficientNet + MobileNet (efficient), timm (pretrained models, must-library). Data augmentation: albumentations (industry standard — 30+ augmentation types), torchvision.transforms.v2 (modern PyTorch native), RandAugment + AugMix + Mixup + CutMix. Multimodal LLM: GPT-4V + Claude 3.5 Sonnet vision + Gemini 1.5/2.0 Pro vision + open-source LLaVA + Qwen 2 VL + InternVL + Idefics2 + Molmo (best open multimodal 2024). OCR: PaddleOCR (Baidu — multilingual leader 2026 — 80+ languages), EasyOCR, Tesseract (legacy still huge), Docling (IBM 2024 — doc understanding), Surya, TrOCR (Microsoft transformer-based). Face recognition: InsightFace (open-source SOTA 2026 — ArcFace + RetinaFace + Buffalo models), DeepFace, face_recognition. Russian: NtechLab FaceNGN / VisionLabs Luna / Tevian / RecFaces. Video understanding + tracking: ByteTrack (fastest 2026), DeepSORT, BoT-SORT, StrongSORT, MOTRv2. Video models: VideoMAE, X-CLIP, InternVideo, MViT. 3D vision: NeRF (original 2020) + Instant-NGP (NVIDIA — 100× faster) + Gaussian Splatting (rising 2023–2026), Nerfstudio (unified framework), Open3D (point cloud), PyTorch3D (Meta), Kaolin (NVIDIA), COLMAP (structure-from-motion), Mitsuba 3 (differentiable rendering). Generative image / video: Stable Diffusion family (SDXL + SD3 + SD3.5), FLUX.1 (Black Forest Labs — open record-holder 2024+), DALL-E 3 / Imagen 3 / Midjourney v6 / Ideogram. Video: Runway Gen-3 / Pika 1.5 / Kling AI / Sora / AnimateDiff / SVD. UIs for generative: ComfyUI (node-based — research favourite), Automatic1111. Fine-tuning generative: LoRA + DreamBooth for Stable Diffusion / FLUX. Inference serving: Triton Inference Server + BentoML + TorchServe. Annotation: CVAT (OpenCV — industry standard), Label Studio + V7 Darwin (commercial) + Roboflow (rapid prototyping). Datasets: COCO (detection / segmentation benchmark) + ImageNet (classification) + OpenImages + LAION-5B (web-scale generative training) + Objects365 + Visual Genome + ADE20K (segmentation) + Cityscapes (autonomous driving). Hardware-aware optimisation: quantisation (INT8 / FP16), pruning, distillation, structured sparsity (for NVIDIA Ampere+ tensor cores).

All three roles overlap in 2026, but focus areas differ. ML Engineer — generalist, owns the production ML stack (recsys / fraud / ranking / classical ML + LLM). Can work with CV data but no deep specialisation. See ML Engineer. AI Engineer / LLM Engineer — focus on LLM integration (text-focused). See AI / LLM Engineer. NLP Engineer — focus on text + speech. See NLP Engineer. Computer Vision Engineer (this page) — focus on image / video / 3D data. Stack overlap with ML Engineer ~60% (PyTorch + cloud ML + deployment) + ~30% unique (OpenCV + Ultralytics + Detectron2 + SAM + albumentations + edge deployment specifics — TensorRT / CoreML). Distinctions: 1) Image-processing intuition — CV Engineer understands camera models / lens distortion / colour spaces / histogram analysis / morphological ops — classical CV foundation. AI Engineer usually knows none of this. 2) Computational constraints — CV models are heavy (gigabytes), inference latency critical (autonomous driving — 30+ FPS mandate), edge deployment (TensorRT / CoreML / OpenVINO mastery) — exclusive CV territory. 3) Generative image / video specialisation — Stable Diffusion / FLUX / ComfyUI workflow expertise — CV-niche specifically (overlaps with AI Engineer for text-driven generation). 4) 3D vision skills — NeRF / Gaussian Splatting / point cloud / SLAM / camera calibration — exclusive CV. 5) Domain expertise — CV roles often tied to a specific industry (autonomous vehicles / medical imaging / robotics / AR/VR / satellite imagery / manufacturing QC). Each domain has its own datasets + rules. Career pivots: ML Engineer Senior → CV Engineer — 4-8 months (need OpenCV + Ultralytics + Detectron2 + albumentations + one domain). CV Engineer Senior → ML Engineer — 2-4 months (easy lateral, add MLOps + LLM basics). CV Engineer Senior → AI Engineer (LLM track) — 3-6 months. Hot 2025–2026 sub-specialisations: 3D Vision (Gaussian Splatting) / Generative Image-Video (FLUX + Sora-style) / Multimodal (Vision-Language Models — LLaVA / Molmo).

Decision tree for object detection / segmentation 2026: 1) Ultralytics YOLO (YOLOv8 / v9 / v10 / v11) — default choice 2026 for object detection. Pros: easy training (3-5 lines of code), good docs + community, fast inference (real-time on CPU + edge), wide deployment support (ONNX + TensorRT + CoreML + TFLite native). Cons: Ultralytics license requires AGPL or commercial license ($$) for proprietary use, less academic-flexible than Detectron2 / MMDetection. Use case: 90% of production object detection use cases, prototypes, edge deployment. 2) Detectron2 (Meta) — research-grade. Pros: clean API, flexible (easy custom architectures), Apache 2.0 license (commercial-friendly), backed by Meta. Cons: slower training than Ultralytics, larger learning curve. Use case: complex custom architectures, research projects, when you need fine control over the training loop. 3) MMDetection / MMSegmentation / MMPose (OpenMMLab — Chinese consortium) — huge ecosystem. Pros: 100+ pre-implemented architectures (old and new), papers' reference implementations always there first, Apache 2.0. Cons: config-heavy (steep learning curve), Chinese-language community sometimes hard for English-only, dependencies can conflict. Use case: research, comparing many architectures, paper reproduction. 4) DETR family (transformer-based detection) — modern paradigm shift 2020+. RT-DETR (Real-Time DETR — Baidu, rising 2024+ — competitor to YOLO in real-time space), DINO-DETR, Co-DETR. Pros: end-to-end (no NMS post-processing), often better accuracy on complex scenes. Cons: slower inference than YOLO, more compute-hungry for training. Use case: highest-accuracy needs, complex scenes (crowded objects). 5) SAM (Segment Anything Model — Meta 2023) + SAM 2 (video version 2024) — universal segmentation. Use case: a) zero-shot segmentation (segment anything without training), b) interactive annotation tool (point / box prompt → mask), c) data-labelling acceleration (SAM-assisted annotation 10× faster). Combine with YOLO / Detectron2: detector → SAM for precise masks. 6) Classical detection (HOG + cascade classifiers — OpenCV) — only for very simple cases, edge devices without GPU, low-power microcontrollers (still relevant for embedded scenarios). Default 2026 recommendations: Production deployment + commercial → Ultralytics YOLO (if OK with AGPL or commercial license) OR Detectron2 (Apache 2.0 alternative). Research / paper reproduction → MMDetection. Highest accuracy / complex scenes → RT-DETR or Co-DETR. Universal / zero-shot segmentation → SAM 2. Annotation acceleration → SAM + interactive workflows (CVAT integration). Edge / mobile → YOLO with TensorRT (NVIDIA) / CoreML (Apple) / TFLite (Android). A Senior CV Engineer must know when to use which.

Yes, 24% of CV Engineer jobs are full-remote or hybrid. CV work is fully cloud-based (training in cloud GPUs — A100 / H100, datasets streaming from S3 / GCS, deployment in Kubernetes). Outsourcing shops — almost always remote on US CV projects. Russian — hybrid or remote after probation. Russian banks (Sber AI Banking CV — face recognition / document verification) — hybrid/office security compliance. Autonomous vehicle companies — special case: research roles often remote-friendly, but product deployment / on-vehicle testing requires on-site (Pittsburgh Cruise / Mountain View Waymo / Palo Alto Tesla / etc). Generative AI companies (Stability / Black Forest Labs / Runway / Pika / Kling) — full-remote standard. International voice-AI / NLP companies overlap (multimodal teams) — full-remote. Big Tech CV — hybrid-standard. Relocant hubs for CV: USA (Bay Area + Pittsburgh — autonomous vehicle clusters + Boston — Robotics MIT region), UK (London — Wayve), Canada (Toronto — Vector Institute), Germany (Berlin AI + Munich automotive — BMW / Mercedes AI), France (Paris — Hugging Face + Mistral for vision LLM), Japan (Tokyo — Sony AI + automotive). English for international CV remote — must (CVPR / ICCV / ECCV papers + community English-speaking).

3D Vision Engineer — sub-specialisation within CV focused on 3D understanding + neural rendering. Day-to-day: 1) NeRF / Gaussian Splatting reconstruction — input video → 3D scene representation. Tools: Instant-NGP (NVIDIA — fast NeRF) / Nerfstudio (unified framework) / gsplat (Gaussian Splatting library) / Polycam app for capture. 2) Point cloud processing — LiDAR data (autonomous vehicles) or depth cameras (Kinect / RealSense). Tools: Open3D / PCL (Point Cloud Library) / PyTorch3D. 3) Structure-from-motion (SfM) — multiple 2D images → 3D scene. Tools: COLMAP (industry-standard SfM). 4) SLAM (Simultaneous Localization and Mapping) — robotics / AR — track camera pose + build map. Tools: ORB-SLAM3 / OpenVSLAM / Kimera. 5) Differentiable rendering — learn 3D from 2D supervision. Tools: Mitsuba 3 / nvdiffrast / Kaolin. 6) 3D generation — text → 3D mesh (DreamFusion + Magic3D + Zero-1-to-3) or image → 3D (TripoSR / InstantMesh — open-source 2024+). 7) Mesh processing — texturing / retopology / UV unwrapping for 3D content pipelines. Stack-specific: PyTorch3D (Meta — 3D research), Kaolin (NVIDIA — 3D DL), Mitsuba 3 (differentiable rendering), Open3D / PCL (point cloud), COLMAP (SfM), Nerfstudio + gsplat (NeRF / 3DGS), Blender Python API (mesh manipulation). Career flow: CV Engineer Senior + computer graphics interest + NeRF / 3DGS hands-on portfolio → 3D Vision Engineer — 6-12 months.

Roadmap: 1) Math foundations — linear algebra + calculus + basics of projective geometry (transformations / homographies / camera models). "Multiple View Geometry in Computer Vision" Hartley / Zisserman — bible for CV math (can be used as a reference, no need to read end-to-end). 2) Python deep + ML basics — PyTorch + NumPy + Matplotlib. Build a simple ML classifier (MNIST). 3) OpenCV mastery — classical CV ops. Course: "Computer Vision Course" by PyImageSearch (Adrian Rosebrock — best entry for OpenCV), OpenCV official documentation + tutorials. Build pet projects: edge detection / homography panorama stitching / face detection with classical Haar cascades. 4) Deep Learning for Vision — Stanford CS231n "Convolutional Neural Networks for Visual Recognition" (Karpathy / Li — free YouTube + slides — must-do, canonical CV deep-learning course). 5) PyTorch + torchvision hands-on — train ResNet / EfficientNet on CIFAR-10, fine-tune a pretrained model on your own dataset. Learn to use timm library (pretrained models). 6) Object detection — Ultralytics YOLO hands-on (easiest entry — train YOLO on a custom dataset in a day). Then Detectron2 (more flexible). 7) Segmentation — Mask R-CNN via Detectron2, then SAM (universal segmentation) — try interactive segmentation. 8) Annotation tooling — CVAT mastery (industry standard for CV annotation). Annotate your own dataset (10-50 images), train detector on it. 9) Augmentation mastery — albumentations library (must for production CV). Understand training stability — strong augmentation improves robustness. 10) Modern transformers for CV — Vision Transformer (ViT) + Swin Transformer + DINOv2 (foundation model). Hugging Face Transformers vision support. 11) Multimodal LLM — try GPT-4V + Claude vision + open-source LLaVA / Qwen 2 VL for understanding. 12) Generative CV track (popular 2024–2026): Stable Diffusion + FLUX hands-on, ComfyUI workflows mastery, LoRA / DreamBooth fine-tuning. Course: "Generative AI with Diffusion Models" DeepLearning.AI. 13) 3D Vision track (rising 2024+): NeRF + Gaussian Splatting hands-on with Nerfstudio framework, capture your own scene with a phone (Polycam app), reconstruct as 3DGS. 14) Edge deployment hands-on — convert PyTorch model → ONNX → TensorRT (on NVIDIA GPU), benchmark inference latency. Try CoreML conversion for Apple Silicon. 15) Pet project portfolio: a) production-grade detection pipeline (e.g. fish-counting / vehicle-counting / cell-counting demo); b) custom Stable Diffusion / FLUX LoRA on own style; c) Gaussian Splatting reconstruction (cool 3D scene from phone video); d) mobile CV app (deploy via CoreML/TFLite). Document on GitHub + blog post + video demo. Russian courses: MIPT DLSchool (CV module — free YouTube), Karpov.Courses "Computer Vision" track, Otus "Computer Vision", SkillFactory CV, School21 (Sber) AI Computer Vision track. International (EN): Stanford CS231n (canonical — free YouTube), fast.ai Practical Deep Learning (CV included), Hugging Face Computer Vision Course (free), "Deep Learning for Computer Vision" book Mohamed Elgendy, PyImageSearch University (Adrian Rosebrock — applied focus). Must-read books: "Deep Learning for Vision Systems" Mohamed Elgendy (Manning), "Multiple View Geometry" Hartley / Zisserman (math reference), "Computer Vision: Algorithms and Applications" Richard Szeliski (free 2nd edition online — encyclopaedic). Conferences: CVPR (top — June), ICCV (October, alternating with ECCV), ECCV (October alternating), NeurIPS (December — broader ML with CV track). Backend Senior / ML Engineer Middle + CV interest + portfolio → CV Engineer Junior — 6-12 months. PhD CV / Robotics → Senior CV Engineer — direct entry.

29 active open CV Engineer positions with explicit CV specifics in our sample. The real pool is many times wider — many CV roles are classified as general ML Engineer / Robotics / AI Engineer (titles like "ML Engineer for autonomous driving" or "Senior Backend Engineer with CV focus"). True CV-focused jobs in CIS + Europe are estimated at 300-1,500 positions active at any moment in 2026 (counting fuzzily classified ones). Geography: Russia / Poland / remote. The real market is broader thanks to the international remote segment (generative AI companies + robotics startups full-remote-friendly). Time to close a Senior CV Engineer — 6-12 weeks (longer than general AI Engineer due to rare-skill — PyTorch deep + classical CV + one domain + edge deployment combination).

A Senior CV Engineer owns the full vision-engineering cycle + technical leadership. Math foundations: linear algebra + projective geometry (camera models / homographies / epipolar geometry) + calculus + optimisation theory — at the level of "can read CVPR papers without math blocks". Python deep + Backend Senior level: async / typing / FastAPI / pytest mastery. C++ basics (for edge deployment + OpenCV custom kernels — nice to have). PyTorch + torchvision mastery deep: custom Datasets / Samplers / Losses / training loops, distributed training (DDP for multi-GPU CV training), mixed-precision (FP16 / BF16 mandatory for production), gradient accumulation for large models. OpenCV mastery: classical CV ops (calibration / morphology / Hough transforms / homography / stereo matching / optical flow), C++ API basics for performance-critical paths. Modern transformers for CV: ViT / Swin / DINOv2 mastery, fine-tuning strategies, hybrid CNN-transformer architectures. Object detection / segmentation mastery: Ultralytics YOLO production deployment + Detectron2 custom architecture authoring + MMDetection / MMSegmentation when needed + RT-DETR + SAM 2 integration in pipelines. Data augmentation mastery: albumentations advanced (custom transforms + composition strategies), test-time augmentation (TTA), AutoAugment / RandAugment / CutMix / Mixup understanding. Foundation models: DINOv2 + CLIP + SigLIP + OpenCLIP — use cases for feature extraction + zero-shot classification + retrieval. Multimodal LLM (GPT-4V / Claude vision / LLaVA / Qwen 2 VL / Molmo) — when to use vs train custom model. Generative CV mastery (if track includes): Stable Diffusion + FLUX deep — ControlNet / LoRA / DreamBooth / IP-Adapter / textual inversion fine-tuning. ComfyUI workflow authoring. Video generation basics (AnimateDiff / SVD). 3D Vision mastery (if track includes): NeRF + Gaussian Splatting + Nerfstudio + COLMAP SfM + Open3D point clouds. Camera calibration + projective geometry deep. Edge deployment mastery: ONNX (cross-framework export + ONNX Runtime optimisation), TensorRT (NVIDIA — INT8 quantisation + plugin development if needed), CoreML (Apple Silicon — Conv2D ops support + Neural Engine specifics), TFLite (Android NNAPI delegate), MediaPipe pipelines, OpenVINO (Intel iGPU). Hardware-aware optimisation: quantisation (PTQ + QAT), pruning, distillation (teacher-student), structured sparsity (NVIDIA Ampere+ for 2× speedup). Production CV pipeline architecture: design end-to-end pipeline on a whiteboard — data ingestion (cameras / drives / cloud streaming) → preprocessing → batched inference → post-processing → tracking / aggregation → downstream actions. Multi-camera systems: synchronisation + calibration + fusion for autonomous / surveillance. Inference serving: Triton Inference Server (NVIDIA — best for multi-model + dynamic batching), DeepStream (NVIDIA video analytics), BentoML / TorchServe. Latency budgets: design for real-time constraints (autonomous: 30+ FPS, AR: 60+ FPS, video moderation: batch ok), profile-driven optimisation (NVIDIA Nsight + PyTorch Profiler). Domain expertise: deep understanding of one or two domains (autonomous driving / medical imaging / retail visual search / robotics perception / AR/VR / satellite imagery / manufacturing QC) — the main premium driver for Senior+. Annotation strategy: design large-scale annotation workflows (CVAT / Label Studio + SAM-assisted), active learning loops, label quality control. Evaluation methodology: COCO metrics (AP / AP50 / AP75 / mAP@[.5:.95]) deep understanding, segmentation metrics (mIoU / Dice / boundary F1), domain-specific metrics (NDS / mAP for autonomous detection), human-in-the-loop evaluation for generative. Soft: ADRs writing for CV architecture decisions, technical writing (CV feature design docs + paper drafts if research-track), cross-team collaboration (Product / Backend / Robotics / Hardware teams), mentoring Middle CV Engineers, paper-reading discipline (CVPR / ICCV / ECCV / NeurIPS must-follow). English for Senior+ MUST — CV community / docs / papers / conferences are English-speaking. Optional bonus: open-source contributions to Ultralytics / Detectron2 / MMDetection / timm / albumentations / Nerfstudio — sharply increase market value for Big Tech CV / autonomous vehicles / generative AI hiring. Papers at CVPR / ICCV / ECCV workshops — premium for Research Engineer CV track.