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Machine Learning Defense Jobs in Allen, TX (NOW HIRING)

Qorvo, Inc.

Data Engineer

Richardson, TX

$103K - $124K/yr

... and aerospace/defense. Visit www.qorvo.com to learn how our innovative team is helping connect ... Lead the architecture and implementation of production-grade data science and machine learning ...

Qorvo, Inc.

Data Engineer

Richardson, TX · On-site

$103K - $124K/yr

... and aerospace/defense. Visit www.qorvo.com to learn how our innovative team is helping connect ... Lead the architecture and implementation of production-grade data science and machine learning ...

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All JobsMachine Learning Defense JobsMachine Learning Defense Jobs in Allen, TX

Machine Learning Defense information

See Allen, TX salary details

$23.7K

$39.6K

$81.9K

How much do machine learning defense jobs pay per year?

As of Jul 31, 2026, the average yearly pay for machine learning defense in Allen, TX is $39,610.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,200.00 and $42,800.00 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 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.
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Machine Learning Defense jobs near you
Infographic showing various Machine Learning Defense job openings in Allen, TX as of July 2026, with employment types broken down into 87% Full Time, 10% Part Time, 2% Contract, and 1% Nights. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $39,610 per year, or $19 per hour.

Senior Staff Software Engineer, Perception (R4985)

Shield AI

Dallas, TX

$233K - $350K/yr

Full-time

Posted 3 days ago

New

Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

The Hivemind Solutions Perception team develops the next generation of perception capabilities for autonomous systems by combining state-of-the-art machine learning with the proven foundations of computer vision. The team advances how autonomous platforms understand and interpret the world by developing vision, vision-language (VLM), and vision-language-action (VLA) models that tackle core perception challenges such as object understanding, scene interpretation, and mission-relevant environmental awareness. Working at the intersection of research and production, our engineers build the data pipelines, supervised fine-tuning (SFT) workflows, evaluation frameworks, and deployment infrastructure needed to transform cutting-edge AI research into reliable, mission-ready perception capabilities.
 
In this role, you'll develop and deploy advanced machine learning models that enable autonomous systems to better understand, reason about, and interact with their environment. You'll partner closely with machine learning researchers, autonomy engineers, perception engineers, and platform teams to translate emerging AI capabilities into reliable, production-ready systems for U.S. and international defense customers. This is an ideal opportunity for engineers who enjoy building state-of-the-art AI systems while solving the practical challenges of deploying them on operational autonomous platforms. 
What You'll Do:

Model Development – Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems. 

Data Pipelines & Model Training – Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks. 

Model Deployment & Optimization – Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks. 

Perception & Autonomy Applications – Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments. 

Research-to-Production – Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability. 

Cross-functional Collaboration – Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems. 

Model Evaluation & Validation – Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements. 

Continuous Improvement – Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery. 

Required Qualifications:

  • Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.

  • Mastery of machine learning fundamentals. 

  • Experience training an deploying ML models for computer vision in a production setting. 

  • Strong understanding of 3D vision problems/algorithms. 

  • Experience with machine learning frameworks such as PyTorch and TensorFlow. 

  • Demonstrated expertise in deploying models using TensorRT and ONNX. 

  • Proficiency in C++ and Python. 

  • Strong analytical and problem-solving skills, with the ability to translate research into practical applications. 

  • Ability to obtain a SECRET clearance 
Preferred Qualifications:

  • Experience with developing autonomous systems for defense customers. 

  • Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.  

  • Contributions to open-source projects in machine learning or computer vision. 

  • Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).

#LI-DS-1
#LE

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
 
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
 
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
 
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.