Objectways
Website:
objectways.com
Job details:
Job Description:
We are looking for a senior Applied AI/ML Engineer with strong hands-on experience in Computer Vision and video systems to help lead the development of production-grade AI solutions for video understanding, detection/segmentation, tracking, multimodal reasoning and model-assisted annotation. This is a practical research-and-engineering role: the person should be comfortable taking a problem from data and experimentation through model training, evaluation, optimization and deployment. Prior Physical AI or robotics experience is not mandatory, but the candidate must have enough depth in vision/video ML to grow into those areas.
What you will own
· Own or co-own the technical design of applied AI/ML solutions with a strong Computer Vision and video component, from problem definition and data strategy through deployment.
· Build, fine-tune and benchmark models for areas such as object detection, instance/semantic segmentation, tracking, video understanding, action recognition or other temporal vision tasks.
· Design reliable datasets, annotation schemas, gold sets and leakage-safe evaluation protocols, and perform systematic failure analysis across real-world video conditions.
· Evaluate modern vision, video and multimodal models and decide when to use a general-purpose model, a specialist model, or a hybrid pipeline.
· Optimize GPU inference and integrate versioned models into production services, working with backend/MLOps and annotation-platform teams.
Profile:
We are open to experienced Applied AI/ML candidates whose strongest work is in Computer Vision, video ML or multimodal systems. The exact previous job title is not important; demonstrated end-to-end ownership of real model-development work is.
· Relevant sourcing profiles / titles include: Senior Applied AI/ML Engineer; Senior AI/ML Engineer – Vision & Multimodal Systems; Senior Computer Vision Engineer; Machine Learning Engineer – Computer Vision; Video AI Engineer; Applied Scientist – Computer Vision; Multimodal AI Engineer; and Applied ML Research Engineer.
· Typically 4–8+ years of relevant hands-on experience in applied AI/ML, with a meaningful portion of that work involving Computer Vision or video. Strong candidates with fewer years but clear end-to-end model ownership may also be considered.
· B.Tech / M.Tech / MS / PhD in Computer Science, AI, Data Science, Electrical/ECE, Mathematics & Computing, Engineering Physics, Robotics, Mechatronics or a related technical discipline. Equivalent demonstrated industry or research depth is also acceptable.
Must Have:
· Strong Python and hands-on PyTorch experience, including model training, fine-tuning, debugging and experiment design.
· Solid practical experience in Computer Vision with real model-development work across at least two of the following: object detection, segmentation, tracking, pose estimation, video understanding, action recognition/localization or related temporal vision problems.
· Hands-on experience processing real image/video data using OpenCV, FFmpeg or equivalent tooling, including frame/clip preparation, augmentation and data-quality handling.
· Ability to design datasets and evaluation protocols correctly: train/validation/test construction, leakage prevention, class imbalance, hard negatives, gold-set creation and reproducible benchmarking.
· Strong model-evaluation and failure-analysis skills; able to reason about errors caused by motion blur, occlusion, domain shift, lighting, viewpoint changes, tracking drift or poor segmentation boundaries.
· Experience training or fine-tuning deep-learning models on GPUs, with practical understanding of VRAM, batching, mixed precision, throughput and inference trade-offs.
· Experience taking models beyond notebooks: Docker-based deployment and API/model serving using FastAPI or equivalent, with practical cloud/GPU deployment exposure.
· Ability to compare multiple technical approaches rather than simply integrate a single popular model, and to explain architecture decisions using measurable quality, latency and reliability trade-offs.
· Ability to provide technical guidance to junior engineers while remaining hands-on with code, experiments and debugging.
Good to Have:
· Multimodal/VLM or open-vocabulary vision experience, including vision-language grounding, video-language understanding or multimodal retrieval.
· Egocentric/first-person video, Physical/Embodied AI, hand-object interaction, manipulation data or annotation-automation experience.
· RGB-D/depth, 3D vision, camera calibration, SLAM/VIO, pose estimation, reconstruction or robotics perception.
· Experience with modern model families or equivalent approaches such as Grounding DINO, SAM-style models, Florence, Qwen-VL, InternVideo, ActionFormer, Mask2Former or contemporary detection/segmentation/video architectures.
· ONNX, TensorRT, Triton Inference Server, CUDA/performance profiling, distributed training or multi-GPU inference.
- · Experience integrating model outputs into human-in-the-loop annotation platforms such as TensorAct, Encord, CVAT, Label Studio or equivalent.
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