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Remote Google Machine Learning Engineer Jobs in Missouri

Jobgether

$80K - $110K/yr

... Engineer based in Netherlands ... Join a fully remote, mission-driven climate technology environment where machine learning and ...

Kohl's

$95K - $131K/yr

... a Machine Learning Engineer with a proven track record of successful project delivery * In-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI ...

Jobgether

This is a remote opportunity for an experienced Machine Learning Specialist to design and deliver ... The role combines hands-on technical delivery with collaboration across data, engineering ...

Catawiki

Working with cross-disciplinary teams involving product owners, developers, UX designers, and ... PyTorch/Tensorflow, LightFM, Git, Docker, Kubernetes, Google BigQuery, MySQL, Spark, Airflow, Kafka ...

Third-Party Job Posts

Location - Remote (Europe) How You'll Make an Impact: As a Staff Machine Learning Engineer , you will play a key role in building and implementing features that empower lodging customers to make data ...

Varsity Tutors

Machine Learning Tutor

Kansas City, MO · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Saint Louis, MO · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Columbia, MO · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Koantek

Senior AI Engineer

Chesterfield, MO · Remote

$54.75 - $70.50/hr

Sr AI Engineer / Data Scientist / MLOps Consultant Location: United States - Remote Employment Type ... This remote role requires a blend of advanced Machine Learning (ML) expertise, deep knowledge of ...

Jobgether

Develop and evolve reliable platform infrastructure supporting data science and machine learning ... Fully remote position within Europe, with the flexibility to work from your home country.

Jobgether

You'll work closely with experienced engineers in a remote, collaborative environment where ... Demonstrated experience taking a machine learning model from raw data through experimentation and ...

Jobgether

$159K - $215K/yr

This is a high-impact engineering leadership role at the intersection of machine learning, data ... Fully remote work environment with a globally distributed team. * Opportunity to lead a ...

Jobgether

$79K - $104K/yr

The role spans managed AWS machine learning services, open-source ML tooling, model serving ... This is a fully remote independent contractor opportunity within a globally distributed team, with ...

New

Jobgether

Design, develop, train, and evaluate machine learning models capable of interpreting complete CT ... Fully remote position open to candidates worldwide. * Location-flexible compensation with a cash ...

Bunge North America

Data Scientist

Chesterfield, MO · On-site +1

... engineers, and business leaders, to translate complex business challenges into solvable data ... Translate complex business problems into data-driven analytics and machine learning tasks, then ...

Bunge

Data Scientist

Chesterfield, MO · On-site +1

... engineers, and business leaders, to translate complex business challenges into solvable data ... Translate complex business problems into data-driven analytics and machine learning tasks, then ...

Third-Party Job Posts

Remote (Europe) How You'll Make an Impact: As a Staff Software Engineer in Revenue Intelligence ... processing, machine-learning workflows, and production infrastructure. We value pragmatic ...

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Remote Google Machine Learning Engineer information

What is a remote Google machine learning engineer?

A Remote Google Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models and artificial intelligence solutions, often using Google Cloud technologies, while working from a remote location. These engineers collaborate with cross-functional teams to solve complex business problems, optimize data pipelines, and improve model performance. Their responsibilities typically include data preprocessing, model selection, training, evaluation, and deployment, all while ensuring scalability and security. Working remotely allows them to contribute to projects from anywhere, leveraging cloud-based tools and collaboration platforms.

What are the key skills and qualifications needed to thrive as a remote Google machine learning engineer?

To thrive as a Remote Google Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning algorithms, typically supported by a relevant degree and experience in building scalable models. Proficiency with tools such as TensorFlow, Python, Google Cloud Platform (GCP), and familiarity with distributed systems is essential. Excellent problem-solving, communication, and self-management skills are crucial for effective remote collaboration and innovation. These capabilities enable engineers to deliver impactful machine learning solutions while seamlessly integrating with global Google teams.

How do remote Google machine learning engineers typically collaborate with cross-functional teams while working from different locations?

Remote Google Machine Learning Engineers often use a combination of video conferencing, cloud-based collaboration tools, and shared code repositories to work closely with data scientists, product managers, and software engineers. Regular stand-up meetings, sprint planning sessions, and detailed documentation help ensure everyone is aligned and project milestones are met. Despite being remote, engineers are encouraged to proactively communicate progress, share insights, and participate in code reviews to maintain a strong team dynamic and drive successful project outcomes.

What are the most commonly searched types of Google Machine Learning Engineer jobs in Missouri?

The most popular types of Google Machine Learning Engineer jobs in Missouri are:

What are popular job titles related to Remote Google Machine Learning Engineer jobs in Missouri?

For Remote Google Machine Learning Engineer jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Remote Google Machine Learning Engineer jobs in Missouri look for?

The top searched job categories for Remote Google Machine Learning Engineer jobs in Missouri are:

What cities in Missouri are hiring for Remote Google Machine Learning Engineer jobs?

Cities in Missouri with the most Remote Google Machine Learning Engineer job openings:

Remote Google Machine Learning Engineer jobs near you

Senior Geospatial Machine Learning Engineer

Jobgether

On-site, Remote

$80K - $110K/yr

Full-time

Posted 19 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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