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Machine Learning Defense Jobs in Florida (NOW HIRING)

SOSi

Data Science Lead

Doral, FL · On-site

... defense and government services industry. We deliver tailored solutions, tested leadership, and ... Proficient in advanced machine learning techniques, statistical modeling, and data science ...

SOSi

Data Science Lead

Doral, FL

... defense and government services industry. We deliver tailored solutions, tested leadership, and ... Proficient in advanced machine learning techniques, statistical modeling, and data science ...

SOSi

Data Science Lead

Doral, FL

... defense and government services industry. We deliver tailored solutions, tested leadership, and ... Proficient in advanced machine learning techniques, statistical modeling, and data science ...

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Machine Learning Defense information

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

To thrive as a Machine Learning Defense professional, you need a strong background in computer science, cybersecurity, and machine learning, often supported by degrees in these fields or related certifications. Familiarity with frameworks like TensorFlow or PyTorch, experience with adversarial machine learning techniques, and knowledge of security protocols are typically required. Critical thinking, problem-solving, and strong communication skills are essential for anticipating threats and collaborating with interdisciplinary teams. These skills ensure that AI systems remain robust and secure against evolving cyber threats, protecting sensitive data and organizational integrity.

What is machine learning defense?

Machine learning defense refers to techniques and strategies designed to protect machine learning models from various security threats, such as adversarial attacks, data poisoning, and model theft. These defenses can include methods like adversarial training, input sanitization, and robust model architectures. The goal is to ensure that machine learning systems remain accurate, reliable, and safe even when faced with malicious attempts to manipulate or exploit them. As machine learning becomes more widely adopted, the importance of effective defenses continues to grow.

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

Professionals in Machine Learning Defense often encounter challenges such as staying ahead of adversarial attacks, managing model robustness, and keeping up with rapidly evolving threat landscapes. Addressing these challenges typically requires continuous learning, collaboration with cybersecurity and data science teams, and implementing rigorous testing and monitoring frameworks for deployed models. Proactively participating in industry forums and staying updated on the latest research also help in identifying emerging threats and mitigation strategies.
What are popular job titles related to Machine Learning Defense jobs in Florida? For Machine Learning Defense jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Machine Learning Defense jobs in Florida look for? The top searched job categories for Machine Learning Defense jobs in Florida are:
What cities in Florida are hiring for Machine Learning Defense jobs? Cities in Florida with the most Machine Learning Defense job openings:
Machine Learning Defense jobs near you

AI Software Engineer

Cooperidge Consulting Firm

Sarasota, FL • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago

Job description

Cooperidge Consulting Firm is seeking an AI/ML Software Engineer for a premier Defense & Intelligence Technology leader in Sarasota, FL.

This role is designed for a high-drive engineer with a deep foundation in artificial intelligence and its practical application to complex signal processing. You will be responsible for designing and deploying AI/ML solutions that address critical SIGINT (Signals Intelligence) processing needs, including time-series data analysis and autonomous decision-making. Working within a high-stakes environment, you will build adaptive systems for the Department of Defense (DoD), ensuring that machine learning architectures can operate with elite accuracy, security, and low-latency inference in dynamic mission environments.
Job Responsibilities

  • SIGINT Processing: Design and implement AI/ML models for event characterization, pattern recognition, and anomaly detection within high-volume sensor data streams.
  • Autonomous Decision-Making: Enable intelligent systems that operate with minimal human intervention, focusing on adaptive processing and system-state inference.
  • Enterprise Integration: Partner with team leads to integrate AI/ML capabilities into large-scale enterprise architectures, prioritizing performant processing and maintainability.
  • Architecture Development: Utilize Transformers, attention mechanisms, and deep learning architectures to discover features within time-series and RF data.
  • MLOps Oversight: Own the deployment and processing pipelines, ensuring rigorous testing, validation, and security of containerized models.
  • Real-World Impact: Develop tailored models to deliver intelligent insights in support of high-priority missions for the Intelligence Community.
  • Feature Discovery: Infer system states from underlying data streams to support critical analysis of sensor systems.

Requirements

Education & Legal
  • BS degree or higher in Computer Science, Electrical Engineering, Mathematics, or a related field.
  • ACTIVE TS/SCI Clearance is REQUIRED.
  • U.S. Citizenship is REQUIRED.
Experience
  • Minimum of 1 year of professional AI/ML experience (3–5 years preferred).
  • Proven expertise in model development and deployment using modern libraries (TensorFlow, PyTorch, scikit-learn).
  • Strong background in neural network architectures, including deep learning and transformers.
  • Solid programming skills with experience in statistical and signal analysis libraries.
  • Deep understanding of MLOps, deployment pipelines, and testing/validation protocols.
Nice-to-Have (Bonus Skills)
  • Experience with RFML (Radio Frequency Machine Learning) or Digital Signal Processing.
  • Familiarity with LLMs, RAG, Agentic systems, or Reinforcement Learning (RLHF).
  • Proficiency with Docker/Kubernetes and cloud platforms (AWS Bedrock, Azure OpenAI, Vertex AI).
  • Experience with real-time inference, low-latency model serving, or adversarial ML.

Benefits

  • Comprehensive health, vision, and dental insurance plans
  • Life insurance coverage
  • 401(k) retirement plan with company matching contributions
  • Paid time off including vacation, sick leave, and holidays
  • Opportunities for career growth and advancement