Senior Machine Learning Operations Engineer
Designs and deploys end-to-end ML pipelines and MLOps practices for AI-driven counter-drone systems in defense applications. Requires 5+ years ML engineering experience, Python/C++ proficiency, cloud/containerization, and computer vision expertise.
About the job
What You'll Do
- Design, develop, and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
- Collaborate with the general software engineering team to integrate ML models into existing software systems and ensure scalability and maintainability.
- Work in conjunction with computer vision specialists to apply and optimize ML techniques for image and video analysis, object detection, tracking, and recognition in defense contexts.
- Research and evaluate new machine learning algorithms, tools, and technologies to enhance our capabilities and solve challenging problems.
- Perform rigorous model testing, validation, and performance tuning to ensure robustness and accuracy in real-world scenarios.
- Contribute to the development of best practices for ML engineering, including MLOps, version control, and reproducible research.
- Mentor junior engineers and contribute to a culture of continuous learning and knowledge sharing.
- Communicate technical concepts effectively to both technical and non-technical stakeholders.
What You'll Need
Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
Experience: 5+ years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments.
Technical Skills:
- Strong proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Solid understanding of core machine learning concepts, including supervised, unsupervised, and reinforcement learning.
- Experience with various machine learning model architectures and their application (e.g., CNNs, RNNs, Transformers, decision trees, support vector machines).
- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
- Experience with MLOps tools and practices.
- Experience deploying a variety of edge systems.
- Experience with TensorRT and other similar technologies.
- Deep knowledge of C++ and Python.
Domain Knowledge:
- Experience or strong interest in defense, aerospace, or related industries is highly desirable.
- Understanding of the unique challenges and considerations for deploying ML in defense applications (e.g., adversarial robustness, real-time constraints, data security).
Collaboration & Communication:
- Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.
- Ability to translate complex technical concepts into clear and concise language.
Problem-Solving:
- Strong analytical and problem-solving skills, with a proactive and innovative approach.
- Ability to work independently and manage multiple priorities in a fast-paced environment.
Bonus Points
- Experience with specific computer vision tasks such as object detection, segmentation, or tracking.
- Familiarity with real-time ML systems and embedded systems.
- Contributions to open-source projects or publications in relevant fields.
What We Offer
- Competitive salary, equity, and benefits package.
- Opportunity to work on cutting-edge technology with a significant impact on national security.
- A collaborative work environment that values innovation.
- Professional development opportunities and career growth.
Skills
Python, TensorFlow, PyTorch, scikit-learn, Kubernetes, Docker, AWS, Azure, GCP, MLOps, TensorRT, C++, Cnns, Rnns, Transformers
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