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Semantic Segmentation Jobs (NOW HIRING)

Blue River Technology

$77K - $105K/yr

Train and optimize computer vision models for object detection, semantic segmentation, and monocular/stereo depth estimation using supervised and self-supervised learning, including loss function ...

Imagry

Senior Deep Learning Engineer - Perception

San Jose, CA · On-site

$123K - $169K/yr

Experience and knowledge in computer vision and image processing algorithms, including classification, object detection, and/or semantic segmentation * In-depth, hands-on knowledge of deep learning ...

Slope

Software Engineer, Perception

Costa Mesa, CA · On-site

$191K - $253K/yr

Object Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic change Detection. * Experience in one or more of the following: * Visual Odometry, SLAM, Multi-view Geometry ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

Hands-on experience with state-of-the-art object detection (e.g., RetinaNet, Mask RCNN, CenterNet), semantic segmentation (e.g., U-Net, deeplab), and image classification models (e.g., ResNet ...

IntelliPro Group Inc.

Data Annotator

San Francisco, CA · On-site

$35 - $40/hr

Annotate visual 3D data (LiDAR/Point Cloud) and 2D camera imagery using bounding boxes, cuboids, polygons, and pixel-level semantic segmentation. * Identify, label, and classify objects and ...

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Semantic Segmentation information

What is semantic segmentation in the context of computer vision?

Semantic segmentation is a computer vision technique that involves classifying each pixel in an image into a predefined category or class, such as car, tree, road, etc. Unlike traditional image classification, which assigns a single label to an entire image, semantic segmentation provides a detailed understanding by labeling every pixel individually. This process is essential for applications like autonomous driving, medical imaging, and satellite image analysis, where precise object boundaries and locations are important. Semantic segmentation is typically achieved using deep learning models such as convolutional neural networks (CNNs) and more advanced architectures like U-Net and DeepLab.

What are the key skills and qualifications needed to thrive as a semantic segmentation specialist?

To thrive as a Semantic Segmentation Specialist, you need a strong background in computer vision, deep learning, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Proficiency with frameworks and tools such as TensorFlow, PyTorch, OpenCV, and experience using annotation tools and cloud platforms is essential. Critical thinking, attention to detail, and effective collaboration skills help professionals design accurate models and work within multidisciplinary teams. These skills and qualities are crucial for developing robust segmentation solutions that drive advancements in fields like autonomous vehicles, medical imaging, and robotics.

What are some common challenges faced by professionals working in semantic segmentation roles, and how can they be addressed?

Professionals in semantic segmentation often encounter challenges such as handling imbalanced datasets, ensuring precise annotation quality, and achieving high accuracy in complex or cluttered images. Overcoming these hurdles typically involves using data augmentation techniques, leveraging advanced neural network architectures, and collaborating closely with data labeling teams for consistent annotations. Additionally, regular collaboration with research scientists and continuous learning about evolving deep learning methods can greatly improve performance and career growth in this field.

What is the difference between Semantic Segmentation vs Computer Vision Engineer?

AspectSemantic SegmentationComputer Vision Engineer
Primary FocusPixel-level image classification to identify specific objects or regionsDeveloping algorithms for image and video analysis, including object detection, tracking, and recognition
Required SkillsDeep learning, CNNs, image processing, Python, TensorFlow/PyTorchMachine learning, computer vision techniques, programming, model deployment
Work EnvironmentResearch labs, AI development teams, autonomous vehicle companiesTech firms, robotics, surveillance, healthcare imaging

Semantic Segmentation specialists focus on detailed pixel-level image analysis, while Computer Vision Engineers develop broader image and video analysis algorithms. Both roles require deep learning expertise and often overlap in AI-driven industries, but their core responsibilities differ in scope and application.

What other helpful pages are available for Semantic Segmentation?

Other pages related to Semantic Segmentation:

Infographic showing various Semantic Segmentation job openings in the United States as of September 2026, with employment types broken down into 4% Internship, 88% Full Time, 4% Temporary, and 4% Contract. Highlights an 84% In-person, 4% Hybrid, and 12% Remote job distribution.

Senior AI/ML C++ software engineer

Lexington, SC • On-site

MLS Technologies
Recruiting and Staffing Services • 11 - 50 employees
Learn more about MLS Technologiesexternal

Full-time

Re-posted 21 days ago

Job description

Senior Embedded Controls Engineer: C++/Linux and Machine Learning exp.
As an AI Machine Learning Engineer focus will be on designing and developing scalable solutions using AI tools and machine learning models. Addressing various neural network-related challenges in transportation sector. This involves leveraging big data computation and storage tools to create prototypes and datasets, conducting model training and evaluations, integrating solutions, performing bench tests and onsite tests, tuning, and monitoring. Proficiency in languages such as C and C++ is required, along with software development for Linux platforms.
Your responsibilities
Design and develop real time AI ? Neural Network solutions for transportation industry maintenance equipment. Implementing appropriate ML algorithms.
Write clean, documented code following best practices.
Develop and implement communication protocols.
Work independently and collaboratively with a motivated team.
Generate requirements and design documentation.
Plan for, design, and deliver testing, and tested products into the QA process.
Apply communication and problem-solving skills to solve software issues related to the design, development, deployment, testing, and operation of systems.
Job Requirements
Minimum Security Clearance:
Bondable
Qualifications
Education
Master"s / Bachelor"s degree in Software Engineering or similar experience.
Experience
5+ years of experience in developing CNN, R-CNN type neural network for computer vision tasks.
5+ years of experience in Software development using C++ & Linux embedded.
Experience with Supervised and Semi-Supervised Learning, Deep Learning, Support Vector Machines, Linear and Logistic Regression.
Working knowledge of AI Framework such as TensorFlow, Caf?, PyTorch, Keras, Darknet and OpenCV.
Working knowledge of AI edge devices such as NVIDIA Jetson / Nano / Orin.
Knowledge of the Linux Operating System.
Preferred Experience
Experience using statistical computer languages (R, Python, SQL etc.) to manipulate data and draw insights from large data sets.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (semantic segmentation, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
Experience with edge computing & controlling devices (On-device deployment in C/C++ or similar) for real time application.
Experience with optimizing neural networks to perform well on low-power mobile platforms (e.g. pruning, distillation, quantization).

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