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We are looking for a Machine Learning & Computer Vision Engineer to develop perception and learning systems for robotic applications using image, video, sensor, and time-series data. You will work across the full ML life-cycle: defining data requirements, building datasets and evaluation pipelines, training models, analyzing failure cases, and integrating validated models into production robotics software. The work spans robot perception, human and object understanding, temporal reasoning, and learning-based behavior. What you will do Develop computer-vision and machine-learning systems for robot perception and decision-making. Build and evaluate models for detection, segmentation, pose estimation, tracking, video understanding, and sensor-based learning. Define data collection, labelling, dataset versioning, and evaluation workflows for real-world robotics problems. Diagnose model failures and improve robustness across lighting, viewpoints, environments, hardware, and operational conditions. Work with robotics engineers to integrate models into ROS 2-based systems, simulation workflows, and real robot deployments. Design clear experiments, compare approaches rigorously, and communicate recommendations backed by evidence. Help establish reproducible training, validation, and deployment practices. What we are looking for Strong foundations in machine learning, computer vision, or deep learning. Experience building and evaluating models with PyTorch or an equivalent framework. Good understanding of data collection, labelling, training, validation, metrics, and model integration. Ability to independently own an experimentation trackfrom problem framing through evaluation and recommendation. Strong engineering judgement, clear written communication, and comfort working with ambiguous real-world data. Good to have YOLO, RT-DETR, or Mask R-CNN for detection and segmentation. ViTPose, Keypoint R-CNN, or ByteTrack for pose estimation and tracking. VideoMAE, Video Swin Transformer, or MS-TCN for video and temporal understanding. Behaviour Cloning, Action Chunking with Transformers, or Diffusion Policy for learning-based control. Experience with multimodal learning, synthetic data, domain adaptation, uncertainty estimation, or robotics-related ML. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.