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$50.5K

$130.1K

$174K

How much do climate research scientist machine learning jobs pay per year?

As of Sep 9, 2026, the average yearly pay for climate research scientist machine learning in the United States is $130,117.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $173,000.00 per year, depending on experience, location, and employer.

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.

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.

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.

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Climate Research Scientist Machine Learning jobs near you
Infographic showing various Climate Research Scientist Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $130,117 per year, or $62.6 per hour.

Machine Learning Research Scientist

Menlo Park, CA • On-site

Voltai
See more jobs at Voltaiexternal

Full-time

Re-posted 2 hours ago

Job description

Job Summary:
Voltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. The Machine Learning Research Scientist will conduct original research in machine learning and AI, develop and fine-tune large language models, and collaborate with cross-functional teams to translate research outcomes into practical applications.
Responsibilities:
• Conduct original research in machine learning and AI with a focus on models that integrate multiple modalities.
• Develop, train, and fine-tune large language models (LLMs) and multi-modal models (MMMs).
• Explore and scale trillion-parameter neural networks, along with smaller, specialized models.
• Design and implement models capable of solving discrete and continuous constraint reasoning tasks and graph-related challenges.
• Develop and evaluate interconnected systems comprising LLMs, NLP models, and retrieval algorithms.
• Publish research findings in top AI conferences and journals, contributing to the academic and industry community.
• Collaborate with cross-functional teams to translate research outcomes into practical applications.
• Innovate and create data synthetically and collect it from human interactions for diverse tasks.
• Lead efforts in optimizing both open-source and proprietary models, considering various constraints.
Qualifications:
Required:
• Strong background in machine learning, including sequence modeling, generative models, and model architecture.
• Expertise in Pytorch, Python, CUDA, and Triton.
• Proven research experience with a strong publication record in top AI conferences and journals.
• Deep understanding of pre-training and fine-tuning large multi-modal models.
• Experience implementing research papers into production code.
• Familiarity with the latest state-of-the-art techniques, including prompting and inference-time search methods.
• Experience in developing and managing large-scale machine learning systems.
• Proficiency in configuring and optimizing hardware and operating systems for maximum performance.
• Experience building distributed training systems for AI models in high-performance computing (HPC) clusters.
Company:
AI models for electronics Founded in , the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.

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