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Scientific Machine Learning Jobs in Florida (NOW HIRING)

AAA Global

Machine Learning Researcher

Miami, FL · On-site

AI Scientist | West Palm Beach, FL About Our Client Our client develops and deploys systematic ... Conduct research in Machine Learning to improve multiple aspects of the investment pipeline

Northern Base

Machine Learning Engineer

Sunrise, FL · On-site

$90K - $110K/yr

Role - Machine Learning Engineer Experience Required -8+ Years We are seeking a Machine Learning ... Experience deploying ML/GenAI systems in production environments. * data scientist * Can do ML ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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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 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 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 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 Florida?

For Scientific Machine Learning jobs in Florida, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Florida look for?

The top searched job categories for Scientific Machine Learning jobs in Florida are:

What cities in Florida are hiring for Scientific Machine Learning jobs?

Cities in Florida with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Florida as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI & Machine Learning Engineer

V-Work Infotech Solutions INC

Cape Coral, FL • On-site

Other

Posted 3 days ago

New

Job description

Job Summary

We are seeking an experienced AI & Machine Learning Engineer with 10–15+ years of IT experience to design, develop, and deploy scalable AI and machine learning solutions. The ideal candidate will have expertise in Generative AI, Large Language Models (LLMs), deep learning, MLOps, cloud platforms, and production-grade AI systems. You will collaborate with data scientists, software engineers, and business stakeholders to deliver AI-driven products and intelligent automation solutions.

Key Responsibilities
  • Design, develop, and deploy machine learning and Generative AI solutions.
  • Build, fine-tune, and optimize Large Language Models (LLMs) and foundation models.
  • Develop Retrieval-Augmented Generation (RAG) applications.
  • Design AI architectures for enterprise-scale applications.
  • Build end-to-end ML pipelines from data ingestion to model deployment.
  • Develop AI-powered chatbots, virtual assistants, and recommendation systems.
  • Implement prompt engineering techniques to improve LLM performance.
  • Deploy models using MLOps best practices.
  • Optimize model accuracy, latency, scalability, and cost.
  • Work with structured, semi-structured, and unstructured datasets.
  • Collaborate with Data Engineering teams to build scalable AI platforms.
  • Monitor production AI systems and continuously improve model performance.
  • Ensure AI solutions follow security, governance, and responsible AI practices.
  • Mentor junior engineers and provide technical leadership.
Required Qualifications
  • Bachelor''s or Master''s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
  • 10–15+ years of IT experience, with significant experience in AI/ML engineering.
  • Strong understanding of machine learning algorithms, deep learning, and Generative AI.
  • Experience delivering enterprise AI solutions.
  • Excellent communication and problem-solving skills.

Apply