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Semantic Segmentation Jobs (NOW HIRING)

Senior, ML Engineer - VLM

Ann Arbor, MI · On-site +1

$102K - $140K/yr

Computer Vision & Deep Learning - model training and at least two of: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, BEV, Depth Estimation. * Multimodal / VLM experience ...

Senior, ML Engineer - VLM

Ann Arbor, MI · On-site +1

$102K - $140K/yr

Computer Vision & Deep Learning - model training and at least two of: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, BEV, Depth Estimation. * Multimodal / VLM experience ...

$18 - $24/hr

Hands-on expertise in and in-depth understanding of one or more of the following areas: multi-view depth estimation, semantic segmentation, Gaussian splatting, generative modeling, BEV-style models ...

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 ...

... semantic segmentation, object tracking, etc. (both single and multi-frame) in frameworks such as PyTorch, TensorRT, and ONNX * Experience fine-tuning, implementing, and deploying vision-language ...

Showing results 21-40

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.

Graduate Intern - Focused Ion Beam, Electron Microscopy, and Autonomous Characterization

Golden, CO • On-site

The National Renewable Energy Laboratory (NREL)
Scientific Research and Development Services • 1 - 5K employees

Full-time

Medical, Dental, Vision, Retirement

Re-posted 13 days ago


Job description

Posting Title
Graduate Intern - Focused Ion Beam, Electron Microscopy, and Autonomous Characterization
Location
CO - Golden
Position Type
Intern (Fixed Term)
Hours Per Week
40
Working at NLR
NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.
Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
Job Description
The MICRO Research Group at the National Laboratory of the Rockies (NLR) has an opening for a B.S. - graduate intern to support cutting-edge work in focused ion beam (FIB) and plasma FIB (PFIB) instrumentation, with particular emphasis on our one-of-a-kind autonomous laser-integrated PFIB platform. This instrument represents a unique national capability - combining laser-assisted sample preparation with AI-driven automation in a configuration unavailable elsewhere in the U.S. research community.
The intern will contribute to both the operation and advancement of this platform, assisting with instrument maintenance, calibration, and scripting workflows that extend its autonomous capabilities. Work will draw on principles from materials science, physics, and chemistry to support a range of characterization and sample preparation tasks - including time-sensitive customer assignments requiring clear documentation and short-turnaround reporting.
Working alongside staff scientists and engineers in a multidisciplinary national laboratory environment, the intern will develop and validate computer vision pipelines - including semantic segmentation models - for automated site identification, sample quality assessment, and adaptive ion beam milling strategies. Integration of foundation models and large language models (LLMs) to interpret instrument feedback and guide autonomous decision-making is an area of active development and a valued area of expertise.
Responsibilities include:
  • Assist with maintenance, calibration, and performance validation of FIB, PFIB, and laser PFIB instruments, following established procedures and safety protocols.
  • Develop and test Python-based instrument scripting and automation routines for ion beam control, image acquisition, and adaptive workflow execution.
  • Build and validate computer vision pipelines - including semantic segmentation models - for real-time site identification, feature detection, and sample quality assessment during FIB/PFIB operations.
  • Support time-sensitive customer characterization assignments, including sample preparation, data collection, and preparation of concise technical reports on short turnaround.
  • Explore integration of foundation models and large language models (LLMs) for natural language instrument interfacing, automated reporting, and AI-guided experimental decision-making.
  • Collaborate with multidisciplinary staff across materials science, physics, chemistry, and engineering; document methods and contribute to internal reports and peer-reviewed publications as appropriate.

We're looking for an intern to work onsite 30-40 hours per week for at least 6 months, with the opportunity to extend.
Basic Qualifications
Minimum of a 3.0 cumulative grade point average.
Undergraduate: Must be enrolled as a full-time student in a bachelor's degree program from an accredited institution.
Post Undergraduate: Earned a bachelor's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate: Must be enrolled as a full-time student in a master's degree program from an accredited institution.
Post Graduate: Earned a master's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate + PhD: Completed master's degree and enrolled as PhD student from an accredited institution.
Please Note:
• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.
* Must meet educational requirements prior to employment start date.
Additional Required Qualifications
  • Strong background in materials science, physics, chemistry, engineering, or a closely related discipline at the master's level.
  • Excellent written and verbal communication skills; demonstrated ability to produce clear technical documentation and meet reporting deadlines. Ability to work effectively in diverse, multidisciplinary research teams.

Preferred Qualifications
  • Prior experience with FIB, PFIB, SEM, TEM, or related electron/ion beam instrumentation, including operation, maintenance, or scripting.
  • Proficiency in Python, including scripting for instrument control, data acquisition, or image processing.
  • Experience with computer vision methods applied to scientific or engineering image data (e.g., segmentation, object detection, feature extraction).
  • Familiarity with Thermo Fisher Scientific (or FEI) instruments (e.g., Helios platform) or equivalent Tescan, JEOL systems.
  • Prior experience in a national laboratory, industrial R&D, or similarly structured research environment.
  • Exposure to foundation models or large language models (LLMs) in a research or applied automation context.
  • Experience with semantic segmentation frameworks (e.g., Segment Anything Model, U-Net, Mask R-CNN) or related deep learning tools applied to scientific imaging.

Job Application Submission Window
The anticipated closing window for application submission is up to 30 days and may be extended as needed.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: / Annual Salary Range: $44,500 - $71,200
NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.
* Based on eligibility rules
Badging Requirement
NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.
Drug Free Workplace
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Submission Guidelines
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Reasonable Accommodations
E-Verifywww.dhs.gov/E-VerifyFor information about right to work, click here for English or here for Spanish.
E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.