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Semantic Segmentation Jobs (NOW HIRING)

As a Senior Machine Learning Engineer , you will build ML models for object detection, semantic segmentation, and tracking. You'll design algorithms capable of distinguishing vessels, land, shoreline ...

As a Senior Machine Learning Engineer , you will build ML models for object detection, semantic segmentation, and tracking. You'll design algorithms capable of distinguishing vessels, land, shoreline ...

As a Staff Machine Learning Engineer , you will be the lead architect of Radar (and secondary EO/IR) models for object detection, semantic segmentation, and tracking. You'll design algorithms capable ...

As a Staff Machine Learning Engineer , you will be the lead architect of Radar (and secondary EO/IR) models for object detection, semantic segmentation, and tracking. You'll design algorithms capable ...

Hands-on experience with deep learning and its applications to computer vision (e.g. object detection and tracking, semantic segmentation, vision language models). * Hands-on experience with ROS 2 ...

... Segmentation * - Semantic Segmentation / Instance Segmentation * -Various Region Detection on Images * - Saliency / Anomaly * - Shape matching / Blob from multi-modal data / Model Fitting ...

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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.

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 Machine Learning Engineer

Birmingham, AL • On-site

$130K - $162K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago


Job description

Job Description
Transform Maritime Intelligence with Cutting-Edge AI/ML
Are you an experienced machine learning researcher ready to push the limits of AI in one of the toughest domains-maritime autonomy? At Tocaro Blue, your expertise in designing, training, and deploying custom ML models will directly advance our foundational perception stack, ProteusCore radar tracking and ApolloCore radar/camera fusion.
As a Senior Machine Learning Engineer, you will build ML models for object detection, semantic segmentation, and tracking. You'll design algorithms capable of distinguishing vessels, land, shoreline constructions, wakes, and markers in dynamic maritime environments where off-the-shelf models fall short. You will be responsible for organizing dataset design, data collection in real and simulated environments, and synthetic augmentation of radar and camera datasets.
Your work will fuel products used by:
Defense customers developing USVs/ASVs for the U.S. Navy.
Commercial OEMs bringing advanced marine ADAS and autopilot features to market.
This role is an opportunity to define the ML foundations of maritime autonomy-where perception evolves from situational awareness, to navigation assistance, to full autonomy.
Core ML and Autonomy Innovation
• Invent and refine custom deep learning architectures for Radar and EO/IR imagery, with an emphasis on semantic segmentation and temporal tracking
• Develop multi-stage ML pipelines (context + characteristic models, segmentation + classification) tailored to low-SNR Radar returns
• Train models on proprietary large-scale datasets (millions of Radar samples and camera sequences) with design-of-experiment methods for data collection and annotation
• Optimize and deploy models to resource-constrained edge hardware (CPU-only and ARM64 platforms), including C++ inference layers
• Advance fusion-aware ML models that integrate Radar with EO/IR, AIS, and cartography for robust classification in GPS-denied or cluttered environments
• Collaborate with fusion and autonomy engineers to ensure ML outputs integrate seamlessly into multi-target tracking and SLAM pipelines
• Contribute to ML-Ops workflows: data management, large-scale training, continuous integration of new field data, and automated evaluation pipelines
What Sets You Apart
Essential Qualifications
• Advanced degree (MS/PhD) in Electrical Engineering, Computer Science, Robotics, or related field
• 7+ years applying machine learning and signal processing to real-world dynamic systems (graduate research counts if directly applicable)
• Demonstrated mastery of semantic segmentation and object classification models, ideally applied to non-vision sensor modalities
• Expert-level Python skills with ML frameworks (TensorFlow/Keras, PyTorch, or equivalent)
Preferred Expertise
• Track record of developing ML models beyond standard YOLO-style detectors, particularly for segmentation of noisy or sparse data (Radar, sonar, or medical imaging)
• Strong background in computer vision and temporal modeling (CNNs, transformers, RNNs for sequential sensor data)
• Experience deploying ML to embedded/edge platforms with optimized C++ inference
• Knowledge of marine, automotive, or aerial robotics systems
• Contributions to large-scale ML data pipelines: annotation strategies, dataset balancing, simulation-to-real transfer
• Passion for pushing the boundaries of AI in GPS-denied, cluttered, and low-visibility environments
Why Tocaro Blue?
Competitive Compensation & Growth
• Competitive base salary with potential equity in a rapidly growing company
• Comprehensive benefits: 401(k) with 4% company matching, full health/dental/vision, life & disability insurance, generous PTO
• Continuous learning via conferences, training, and professional growth
Innovation-First Culture
• Direct impact on defining the AI backbone of maritime autonomy
• Work on problems unsolved in automotive AI: Radar segmentation, maritime multi-object tracking, sensor fusion in GPS-denied waters
• Collaborative environment with elite engineers and researchers
Exciting Work Environment
• Full-time, in person Birmingham (AL)
• Hands-on field validation through semi-monthly data collection trips at our Pensacola test facility
• A culture that balances innovation with personal growth
Equal Opportunity & Eligibility
Tocaro Blue, LLC is an equal opportunity employer, and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Individual offers are based are made based on skill and experience, geographic location, as well as role, responsibility, and leadership within the company, and other due diligence. Our hiring team will try to determine whether each candidate fits the job description and may choose, at their discretion, to redirect a candidate to another job offering that is more appropriate.
All employees must be eligible to obtain a U.S. Department of Defense security clearance. With few exceptions, this is restricted to U.S. citizens and legal permanent residents (a.k.a. current Green Card Holders). Tocaro Blue LLC is not able to sponsor work visas nor permanent resident cards ("green cards") for this role.