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Scientific Machine Learning Jobs in Maine (NOW HIRING)

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Maine? For Scientific Machine Learning jobs in Maine, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Maine look for? The top searched job categories for Scientific Machine Learning jobs in Maine are:
Infographic showing various Scientific Machine Learning job openings in Maine as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Market Street Talent

Portland, ME • On-site

Other

Medical, Dental, Vision, Retirement

Posted 4 days ago


Job description

Could you be a good fit?

We are looking for a highly skilled Machine Learning Engineer to join the team of our exceptional client. This role focuses on building and operating the infrastructure that transforms machine learning and computer vision models into scalable production systems. You''ll work closely with Data Scientists and Software Engineers to develop robust training and inference pipelines, optimize model performance, and support cutting-edge AI solutions deployed in both cloud and edge environments.

Benefits:

• Hybrid work environment (Greater Portland Maine area preferred)
• Healthcare Medical, Dental, and Vision Insurance
• 401(k)

What will your day look like? As a Machine Learning Engineer, you will:

• Partner with Data Scientists to productionize machine learning and computer vision models and implement scalable, efficient training and inference pipelines
• Optimize models for performance and scalability, including GPU/CUDA-level tuning and edge/embedded inference optimization using technologies such as TensorRT and DLA
• Collaborate with Software Engineers and Data Scientists to integrate machine learning models into production systems in cloud and edge environments
• Analyze data requirements and support the development of data-driven solutions
• Design and implement data preprocessing and feature engineering pipelines
• Build and maintain infrastructure-as-code for machine learning platforms and deployments
• Develop, deploy, and support machine learning applications using modern MLOps tools and frameworks
• Stay current with emerging technologies and best practices in machine learning, computer vision, and MLOps
• Contribute to high-impact AI initiatives while helping drive operational excellence across machine learning projects

You will be a good fit for the Machine Learning Engineer role if you have:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience)
• Proven experience delivering machine learning solutions into production environments
• Strong software engineering skills with a test-driven development mindset
• Advanced programming experience with Python and experience working with Spark
• Hands-on experience with distributed computing frameworks such as Ray
• Experience with machine learning frameworks such as PyTorch and modern foundation and vision models
• Strong knowledge of MLOps technologies including Databricks, Databricks Asset Bundles, MLflow, and AWS services such as EC2, S3, and Lambda
• Experience with containerization technologies such as Docker
• Experience using infrastructure-as-code tools including Terraform and CloudFormation
• Expertise optimizing and deploying models for GPU and edge inference using technologies such as CUDA and TensorRT
• Experience creating and deploying applications to AWS Lambda
• Strong Linux administration and troubleshooting skills
• Experience writing and optimizing SQL queries
• Excellent problem-solving, analytical, communication, and collaboration skills

Nice-to-Haves:

• Experience with NVIDIA edge platforms such as Jetson Orin or Thor
• Familiarity with NVIDIA development and optimization tools including Nsight and DeepStream SDK
• Experience working with computer vision applications
• Familiarity with microscopy, scientific imaging, or other image-intensive data domains
• Experience supporting enterprise-scale machine learning operations and AI platforms

About Market Street Talent

We are a specialized staffing and consulting firm focused on IT and technology positions. Our deep industry knowledge allows us to match exceptional candidates with organizations where they can thrive and make an immediate impact.

Our Vision: To promote and foster the growth of information technology (IT) in our world—one candidate, one client, one community at a time.

Our Goal: To guide clients and candidates through the placement process and build long-term, successful relationships.

Our Culture: At MST, we value excellence, respect, and empathy in everything we do.

Sound like you? Ready for your next challenging IT opportunity? Click "Apply Now!"