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Intern Computer Vision Deep Learning Engineer Jobs in Missouri

Jobgether

$80K - $110K/yr

The role combines hands-on engineering with technical ownership, giving you the opportunity to lead ... Proven experience developing computer vision or deep learning models using satellite or aerial ...

Whiterabbit.ai

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... applications of computer vision Must Have Experience * Experience with deep learning and ...

Whiterabbit.ai

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... applications of computer vision Must Have Experience * Experience with deep learning and ...

Whiterabbit.ai

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... applications of computer vision Must Have Experience * Experience with deep learning and ...

Whiterabbit.ai

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... applications of computer vision Must Have Experience * Experience with deep learning and ...

Whiterabbit.ai

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... applications of computer vision Must Have Experience * Experience with deep learning and ...

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All JobsIntern Computer Vision Deep Learning Engineer JobsIntern Computer Vision Deep Learning Engineer Jobs in Missouri

Intern Computer Vision Deep Learning Engineer information

What does an intern computer vision deep learning engineer do?

An Intern Computer Vision Deep Learning Engineer assists in developing and improving algorithms that enable computers to interpret and understand visual information from the world, such as images and videos. They often work on tasks like image classification, object detection, and facial recognition using deep learning frameworks like TensorFlow or PyTorch. Interns typically help with data collection, model training, evaluation, and sometimes deployment, all under the guidance of experienced team members. This role is a great opportunity to gain hands-on experience in machine learning and computer vision while contributing to real-world projects.

What are the key skills and qualifications needed to thrive as an intern computer vision deep learning engineer?

To thrive as an Intern Computer Vision Deep Learning Engineer, you need a solid understanding of machine learning fundamentals, computer vision concepts, and proficiency in programming languages like Python, often supported by coursework or personal projects. Familiarity with deep learning frameworks such as TensorFlow or PyTorch and experience with image processing libraries like OpenCV are typically expected. Strong problem-solving abilities, curiosity, and effective teamwork skills help interns excel in fast-paced research and development environments. These skills are essential for contributing to innovative projects and adapting to the rapidly evolving field of computer vision.

What types of projects or tasks can I expect to work on as an intern computer vision deep learning engineer?

As an Intern Computer Vision Deep Learning Engineer, you can expect to contribute to projects involving image or video analysis, such as object detection, image classification, or facial recognition. Your daily tasks might include data preprocessing, annotating datasets, training and evaluating deep learning models, and assisting with model optimization for deployment. You’ll often work closely with senior engineers and researchers, gaining hands-on experience with real-world datasets and cutting-edge frameworks. Collaboration with cross-functional teams, such as software developers and product managers, is common to ensure your models address practical business needs.

What is the difference between Intern Computer Vision Deep Learning Engineer vs Intern Machine Learning Engineer?

AspectIntern Computer Vision Deep Learning EngineerIntern Machine Learning Engineer
Required SkillsComputer vision, deep learning, CNNs, Python, TensorFlow/PyTorchMachine learning, algorithms, Python, scikit-learn, TensorFlow/PyTorch
Work EnvironmentResearch labs, tech companies, startups focusing on image/video analysisTech companies, research labs, startups working on diverse ML applications
Industry UsagePrimarily in computer vision projects like object detection, image segmentationBroader ML projects including predictive modeling, NLP, recommendation systems

Intern Computer Vision Deep Learning Engineers focus on image and video analysis using deep learning techniques, while Intern Machine Learning Engineers work on a wider range of ML applications. Both roles require strong Python skills and familiarity with deep learning frameworks, but their project focus and industry applications differ.

What are the most commonly searched types of Computer Vision Deep Learning Engineer jobs in Missouri?

The most popular types of Computer Vision Deep Learning Engineer jobs in Missouri are:

What are popular job titles related to Intern Computer Vision Deep Learning Engineer jobs in Missouri?

For Intern Computer Vision Deep Learning Engineer jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Intern Computer Vision Deep Learning Engineer jobs in Missouri look for?

The top searched job categories for Intern Computer Vision Deep Learning Engineer jobs in Missouri are:

What cities in Missouri are hiring for Intern Computer Vision Deep Learning Engineer jobs?

Cities in Missouri with the most Intern Computer Vision Deep Learning Engineer job openings:

Intern Computer Vision Deep Learning Engineer jobs near you
Infographic showing various Intern Computer Vision Deep Learning Engineer job openings in Missouri as of July 2026, with employment types broken down into 1% As Needed, 76% Full Time, 18% Part Time, and 5% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.

Senior Geospatial Machine Learning Engineer

Jobgether

On-site, Remote

$80K - $110K/yr

Full-time

Posted 12 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Geospatial Machine Learning Engineer based in Netherlands.

Join a fully remote, mission-driven climate technology environment where machine learning and satellite imagery are used to address critical infrastructure challenges.
As part of the Vegetation Modeling team, you will build advanced ML solutions that identify vegetation-related risks before they contribute to wildfires or power outages.
You will work with large-scale geospatial datasets, satellite and aerial imagery, computer vision, and deep learning to create production-ready intelligence products.
The role combines hands-on engineering with technical ownership, giving you the opportunity to lead projects from initial planning through delivery.
You will collaborate with teams across Europe and the Americas, influencing data pipelines, platform architecture, model evaluation, and product delivery.
Your work will directly contribute to improving grid resilience while applying technology to complex environmental and climate challenges.
This is an opportunity for a senior ML professional who wants meaningful technical challenges and measurable real-world impact.

Accountabilities
  • Develop and deploy new vegetation intelligence products using machine learning, deep learning, computer vision, geospatial Python libraries, and large-scale satellite or aerial imagery.
  • Explore geospatial datasets, identify opportunities for model improvement, optimize existing ML solutions, and troubleshoot production issues.
  • Maintain and enhance existing vegetation modeling products to improve accuracy, reliability, scalability, and overall impact.
  • Lead technical projects end-to-end, from defining objectives and planning implementation through execution, delivery, and evaluation.
  • Develop measurement frameworks, evaluation tooling, and performance metrics that enable data-driven decisions about model quality and impact.
  • Monitor production models and investigate performance issues using appropriate observability, monitoring, and debugging tools.
  • Work closely with upstream data ingestion teams to influence data pipelines, processing workflows, and platform architecture.
  • Partner with downstream product and delivery teams to ensure geospatial ML outputs can be effectively integrated into customer-facing solutions.
  • Use tools such as QGIS, Dagster, Sentry, Grafana, or equivalent platforms to analyze data, manage workflows, monitor systems, and diagnose issues.
  • Communicate technical findings, project progress, model performance, and business impact clearly to technical and non-technical stakeholders.
  • Contribute to engineering and ML best practices across a distributed team working across Europe and the Americas.
  • Help translate complex environmental and geospatial problems into scalable machine learning solutions that support climate resilience and critical infrastructure.
Requirements:
  • 5+ years of professional experience as a Machine Learning Engineer, Data Scientist, or in a closely related role, with demonstrated experience building and deploying production machine learning or deep learning models.
  • Proven experience developing computer vision or deep learning models using satellite or aerial imagery.
  • Strong proficiency in Python and geospatial Python libraries such as rasterio, geopandas, shapely, GDAL, or equivalent technologies.
  • Solid understanding of geospatial data structures, formats, processing workflows, and analysis techniques.
  • Professional experience with ML and deep learning frameworks such as PyTorch, TensorFlow, scikit-learn, or comparable tools.
  • Experience designing, implementing, or maintaining data pipelines using orchestration and workflow tools such as Dagster, Airflow, dbt, or equivalent systems.
  • Experience with QGIS or comparable geospatial visualization and analysis software.
  • Strong understanding of model evaluation, performance measurement, monitoring, and debugging in production environments.
  • Ability to work effectively with large-scale, complex datasets and translate technical findings into practical product or business decisions.
  • Strong project ownership skills, with the ability to independently drive initiatives from planning through execution and delivery.
  • Excellent communication and collaboration skills, particularly in distributed and cross-functional environments.
  • Experience with multispectral or hyperspectral satellite imagery is a strong advantage.
  • Background in vegetation analysis, forestry, agriculture, environmental monitoring, or related geospatial applications is highly valued.
  • Familiarity with observability and monitoring tools such as Grafana, Sentry, Prometheus, or similar platforms is a plus.
  • A genuine interest in climate technology, environmental applications, and using advanced technology to solve complex real-world problems is highly desirable.
  • Candidates should be comfortable working in a fully remote environment and collaborating across multiple time zones.
Benefits:
  • Fully remote working environment.
  • Opportunity to work on technology with direct applications in climate action, wildfire prevention, and electrical grid resilience.
  • Meaningful ownership of machine learning products and the opportunity to lead projects from concept through production.
  • Collaboration with a geographically distributed team spanning Europe and the Americas.
  • Exposure to advanced satellite imagery, geospatial data, computer vision, and large-scale machine learning systems.
  • High-impact technical challenges involving real-world environmental and infrastructure problems.
  • Competitive senior-level compensation package expected, commensurate with experience.
  • Opportunity to contribute to the development of production ML systems rather than purely experimental or research-focused models.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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