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Climate Research Scientist Machine Learning Jobs in Minnesota

Senior Machine Learning Engineer

Minneapolis, MN ยท On-site

$109K - $149K/yr

... researchers, software engineers, systems teams, and field operators to translate mission ... Required : โ€ข Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a ...

Machine Learning Tutor

Minneapolis, MN ยท Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Collaborate closely with product managers, data scientists, and backend engineers to deeply ... machine learning, operations research or equivalent self study and experience * Have strong ...

Collaborate closely with product managers, data scientists, and backend engineers to deeply ... machine learning, operations research or equivalent self study and experience * Have strong ...

Collaborate closely with product managers, data scientists, and backend engineers to deeply ... machine learning, operations research or equivalent self study and experience * Have strong ...

Collaborate closely with product managers, data scientists, and backend engineers to deeply ... machine learning, operations research or equivalent self study and experience * Have strong ...

Lead Research Engineer

Eagan, MN ยท On-site

$104K - $137K/yr

The science and engineering of AI are rapidly evolving. We are looking for a lead who drives ... Experience integrating Machine Learning solutions into production-grade software with a sound ...

Collaborate closely with product managers, data scientists, and backend engineers to deeply ... machine learning, operations research or equivalent self study and experience * Have strong ...

Showing results 41-60

Climate Research Scientist Machine Learning information

What are the key skills and qualifications needed to thrive as a climate research scientist specializing in machine learning?

To thrive as a Climate Research Scientist specializing in Machine Learning, you need a solid background in climate science, statistical analysis, and advanced machine learning techniques, typically supported by a graduate degree in a related field. Experience with programming languages like Python or R, familiarity with climate modeling software, and proficiency in machine learning frameworks such as TensorFlow or PyTorch are highly valuable. Strong analytical thinking, problem-solving abilities, and effective communication skills help you explain complex findings to diverse audiences and collaborate across disciplines. These skills and qualities are crucial for advancing climate research, developing innovative solutions, and informing policy decisions based on robust data analysis.

What does a climate research scientist specializing in machine learning do?

A Climate Research Scientist who specializes in Machine Learning uses advanced algorithms and computational models to analyze climate data and improve predictions about climate change. They work with large datasets from satellites, weather stations, and simulations to identify patterns, make forecasts, and assess environmental impacts. Their work helps inform policy decisions, guide mitigation strategies, and advance our scientific understanding of the Earth's climate system. Collaboration with other scientists, governments, and organizations is often a key part of the role.

How do climate research scientists specializing in machine learning typically collaborate with multidisciplinary teams?

Climate Research Scientists with expertise in Machine Learning often work closely with meteorologists, data engineers, environmental scientists, and policy experts. They contribute by developing and refining predictive models using large climate datasets, while also translating complex outputs into actionable insights for decision-makers. Collaboration often involves regular team meetings, joint publications, and integrating domain expertise to ensure that the models are both scientifically robust and practically useful. Strong communication skills are valuable, as these scientists frequently explain technical concepts to colleagues from non-technical backgrounds.

What is the difference between Climate Research Scientist Machine Learning vs Climate Data Analyst?

AspectClimate Research Scientist Machine LearningClimate Data Analyst
Required CredentialsMaster's or PhD in Climate Science, Data Science, or related fields; knowledge of machine learningBachelor's or Master's in Environmental Science, Data Analysis, or related fields; proficiency in data tools
Work EnvironmentResearch labs, universities, environmental agencies, often collaborative and interdisciplinaryGovernment agencies, consulting firms, NGOs; focus on data processing and reporting
Employer & Industry UsageResearch institutions, academia, environmental organizations integrating machine learningPolicy organizations, environmental consultancies analyzing climate data

While both roles involve climate data, Climate Research Scientist Machine Learning focuses on developing predictive models using advanced algorithms, whereas Climate Data Analysts primarily process and interpret climate datasets to inform decisions. The former requires more specialized knowledge in machine learning techniques, while the latter emphasizes data management and reporting skills.

What are popular job titles related to Climate Research Scientist Machine Learning jobs in Minnesota? For Climate Research Scientist Machine Learning jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Climate Research Scientist Machine Learning jobs in Minnesota look for? The top searched job categories for Climate Research Scientist Machine Learning jobs in Minnesota are:
What cities in Minnesota are hiring for Climate Research Scientist Machine Learning jobs? Cities in Minnesota with the most Climate Research Scientist Machine Learning job openings:

Senior Machine Learning Engineer

Onsights

Minneapolis, MN โ€ข On-site

$109K - $149K/yr

Full-time

Re-posted 21 days ago


Job description

Job Summary:
Anno.ai is a mission-focused defense technology startup dedicated to accelerating the safe and effective development of next-generation autonomous systems. As a Senior Machine Learning Engineer, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled software to automate processes and streamline customer mission operations.
Responsibilities:
โ€ข Operationalize machine learning models by building and maintaining robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-prem, and edge compute environments
โ€ข Work closely with autonomy researchers, software engineers, systems teams, and field operators to translate mission requirements into deployable ML capabilities
โ€ข Implement automated CI/CD workflows tailored to ML systems, ensuring repeatable experiments, reliable packaging, and continuous delivery of both up to date models and associated data pipelines
โ€ข Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and incorporating model serving platforms (e.g., Seldon, KServe, BentoML)
โ€ข Develop monitoring systems to track model health, performance, data drift, system utilization, and mission relevance using tools such as Prometheus, Grafana, and ELK/EFK stacks
โ€ข Ensure ML deployments meet defense, customer, and platform security requirements, with emphasis on data integrity, traceability, and operational reliability
โ€ข Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to enhance reproducibility, extensibility, scalability, and deployment speed of ML systems
Qualifications:
Required:
โ€ข Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical field (Master's preferred)
โ€ข 5+ years of professional experience in software engineering, machine learning engineering, MLOps, or related roles
โ€ข Experience operationalizing ML systems at production scale, including model training, versioning, packaging, deployment, and monitoring
โ€ข Strong proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow)
โ€ข Hands-on experience with MLOps frameworks and workflow tooling (e.g., MLflow, Kubeflow, Airflow, DVC, BentoML)
โ€ข Experience deploying containerized ML services using Docker and orchestrating workloads using Kubernetes (including air-gapped or constrained deployments)
โ€ข Understanding of CI/CD workflows and DevOps practices applied to ML systems (e.g., Git, Code Review, Metrics Evaluation)
โ€ข Familiarity with monitoring, observability, and logging platforms (e.g., Prometheus, Grafana, ELK/EFK)
โ€ข Ability to obtain and maintain U.S. Government security clearance (U.S. Citizenship required)
โ€ข Ability to travel up to 20%
Preferred:
โ€ข Experience with deploying models and associated runtimes to Edged Devices
โ€ข Experience optimizing models for memory and CPU constrained systems (e.g., embedded systems, microcontrollers)
โ€ข Prior experience supporting U.S. Department of War programs, cUAS systems, or mission-critical autonomous platforms
โ€ข Experience working with diverse or atypical data sources (e.g., Audio/Acoustics, RF signals, EO/IR imagery)
โ€ข Experience deploying and optimizing ML inference on edge or resource-limited compute systems
โ€ข Experience with Explainable/Auditable AI/ML tools and interpretable model design
โ€ข Experience with AI Software Development Tools (e.g., GitHub CoPilot, Claude)
Company:
Bringing online retail metrics, insights, and visibility you care about into your brick and mortar locations. Founded in 2019, the company is headquartered in Minnetonka, USA, with a team of 11-50 employees. The company is currently Early Stage.