Job Title: MLOPs Internship
Company: Deca Defense, LLC
Location: Remote
Employment Type: Unpaid Internship (20-25 hours per week)
ITAR Restriction: In order to comply with the International Traffic in Arms Regulations (ITAR), we can only accept applications from U.S. citizens. Applicants who do not meet this requirement cannot be considered for this position.
About Us:
Deca Defense exclusively serves Defense OEMs and the DoD, specializing in Edge AI engineering. Our mission is clear: eliminate warfighter pain points by developing custom AI models that directly resolve critical field challenges. We integrate embedded AI with advanced deep learning to deliver precise, mission-critical solutions at the tactical edge.
What sets Deca Defense apart is our AI engineering team, comprised of veterans who have transitioned from the battlefield to a critical capacity behind the scenes. These individuals bring irreplaceable real-world insights from varied deployments directly to our models. We deliver AI solutions tailored to the harsh realities of the tactical edge.
It’s that straightforward—while others build “one-size-fits-all, vendor-locked black-box solutions” that dazzle in demos and boost their stock prices, we’re busy engineering AI that excels where it truly matters: in the field, where mission success isn’t just a target, it’s a mandate.
Position Overview:
We’re offering an unpaid internship for a driven MLOps intern with a preference for candidates with a veteran background to join our team. This role is designed to provide valuable hands-on experience in deploying, managing, and scaling machine learning models in production environments. While contributing to real-world projects that address critical warfighter challenges, you’ll gain in-depth knowledge of MLOps best practices and the tactical application of AI at the edge.
As an intern, your growth is our priority. You’ll work closely with experienced professionals who are committed to mentoring and guiding you as you develop your skills.
Key Responsibilities:
- Gain hands-on experience in building and streamlining the deployment, monitoring, and management of AI models in production environments under the guidance of experienced professionals.
- Work alongside AI research and AI system engineers, in addition to AI firmware engineering and other team members to understand project requirements and contribute to the development of scalable solutions, enhancing your ability to collaborate effectively in a multidisciplinary environment.
- Develop your skills by assisting in the optimization and maintenance of CI/CD pipelines for machine learning model deployment, learning industry best practices along the way.
- Acquire practical experience in monitoring and troubleshooting machine learning systems in production to ensure high availability and performance with support and mentorship from our team.
- Conduct research to stay updated with the latest advancements in MLOps, AI, and defense-related applications, applying what you learn to real-world projects and broadening your knowledge base.
- Learn how to document processes, models, and results effectively for future reference and knowledge sharing, contributing to team knowledge and honing your technical writing skills.
Qualifications:
- Preference for candidates with a military background, which will be beneficial in understanding the context of our projects.
- Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Engineering, or a related field with opportunities to apply and expand your academic knowledge in a practical setting.
- Familiarity with MLOps tools and frameworks such as Kubeflow, MLflow, or TensorFlow Extended (TFX), with a focus on deepening your understanding through guided practical application.
- Basic knowledge of containerization and orchestration tools like Docker and Kubernetes, with opportunities to gain more experience in their use and best practices.
- Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or Keras, with an emphasis on developing your skills through practical experience in real-world projects.
- Proficiency in programming languages such as Python, Bash, or YAML, with support to enhance your coding skills in the context of MLOps.
- Basic understanding of cloud platforms (AWS, Google Cloud, Azure) and distributed computing, with opportunities to learn more about their applications in MLOps.
- Strong problem-solving skills and attention to detail, with the chance to apply and refine these abilities in a supportive environment.
- Strong communication and collaboration skills, which you’ll have the opportunity to develop further while working with a multidisciplinary team.
- A plus if you have an understanding of military systems and warfighter needs with opportunities to learn how these insights can be applied in AI and MLOps contexts.
Note: If you don't meet all the qualifications listed above, don't hesitate to apply. We are committed to training and developing our team members to fill in any gaps.
Why Join Us:
- Join a team of seasoned veterans who have successfully transitioned from the field to the forefront of AI development. You’ll learn how deep learning solutions are engineered to directly impact mission success at the tactical edge.
- This internship offers you the chance to work on projects that address genuine challenges faced by warfighters, providing you with practical, impactful experience that goes beyond theoretical knowledge.
- Develop your skills in a dynamic environment, guided by professionals who’ve faced the challenges you’re helping to solve. Your growth and learning are prioritized, ensuring you gain valuable insights and expertise.
- We understand the demands of working at the tactical edge and offer the flexibility needed to support your learning journey. Our mission is to help you develop the skills and knowledge to excel in your career.
How to Apply:
If you are a veteran with a passion for MLOps and AI, and you meet the qualifications listed above, we would like to hear from you. Please note that this is an unpaid internship. Please submit your resume and a cover letter detailing your relevant experience and why you are interested in this position.