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Machine Learning Defense Jobs in Seattle, WA (NOW HIRING)

MORSE Corp

Machine Learning Engineer

Seattle, WA · On-site

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... Understanding of Department of Defense requirements and standards is a plus

Axon

Principal Machine Learning Engineer

Seattle, WA · Hybrid

... new machine learning models and AI capabilities, leading to better and more responsible AI ... Defense domains. What You'll Do Location: Hybrid from Seattle, WA Reports to: VP, Dedrone ...

Axon

Principal Machine Learning Engineer

Seattle, WA · On-site

$153.60K - $206K/yr

... new machine learning models and AI capabilities, leading to better and more responsible AI ... Defense domains. What You'll Do Location: Hybrid from Seattle, WA Reports to: VP, Dedrone ...

Axon

Principal Machine Learning Engineer

Seattle, WA · Hybrid

$153.70K - $206.10K/yr

... new machine learning models and AI capabilities, leading to better and more responsible AI ... Defense domains. What You'll Do Location: Hybrid from Seattle, WA Reports to: VP, Dedrone ...

Raft

Associate AI/ML Engineer

Tacoma, WA · On-site

Raft is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S ... develop machine learning models that are integral to delivering value to end customers.

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$29K

$48.5K

$100.1K

How much do machine learning defense jobs pay per year?

As of Jun 3, 2026, the average yearly pay for machine learning defense in Seattle, WA is $48,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $37,000.00 and $52,300.00 per year, depending on experience, location, and employer.

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 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 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.
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Machine Learning Defense jobs near you
Machine Learning Engineer

Machine Learning Engineer

MORSE Corp

Seattle, WA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role in designing, implementing, and managing complex ML algorithms and systems, with a focus on computer vision (CV) and other types of data. You will be responsible for acquiring truth data, integrating algorithms, testing algorithms, combining algorithms, reviewing literature to stay on top of the latest-and-greatest methods, analyzing data from field tests, and developing advanced algorithms. MORSE's AI & ML work crosses modalities, and experience or interest in the fields of Large Language Models (LLM), audio analysis, computer vision, and advanced reasoning is a plus. You will work with MORSE's current team of engineers to transition algorithms to production, which may run on on-prem servers, on the cloud, or on a real-time embedded system. You will be part of our team working to accelerate our US National Security customers abilities to use natural language processing capabilities in mission-critical environments. 

Responsibilities: 
  • Develop, fine-tune, train, and optimize Computer Vision algorithms processing tasks such as object detection and tracking.  
  • Use MLOps tools for efficient experiment tracking, data management, and reproducibility 
  • Write robust, efficient, and maintainable code 
  • Track the latest advancements with machine learning research to bring new techniques and methodologies to MORSE 
  • Conduct experiments and perform rigorous evaluations to assess the effectiveness and efficiency of CV models 

Skills and Requirements: 
  • US CITIZENSHIP REQUIRED and the ability to obtain a U.S. Security Clearance 
  • Masters or Ph.D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or related field 
  • Proven experience in applying CV models, techniques, frameworks, and libraries to implement and fine-tune models 
  • Proven experience testing and validating the performance of AI technologies in real-world applications 
  • Proficiency in Python 
  • Experience with cloud platforms (AWS and Azure) 
  • Experience with Docker 
  • Experience with MLOps tools such as Airflow, MLFlow, AimStack, etc. 
  • Exceptional communication skills and the ability to work well with customers 
  • Understanding of Department of Defense requirements and standards is a plus 

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