At Airspace, we know that failure is not an option. We ship anything from organs for transplant to aircraft parts that could cause delays across the globe. We focus on time-critical shipments because…
Job Overview
Application for Machine Learning Engineer (Defense) position at Air Space Intelligence. ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible.
Key Responsibilities
Design and deploy production-grade systems that integrate machine learning models into scalable software pipelines.
Develop and ship features that leverage ML to solve real-world optimization and prediction problems.
Work with modern infrastructure like Kubernetes, AWS, and MLOps tooling.
Approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale.
Required Qualifications
Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain.
Strong understanding of data structures, algorithms, and software engineering best practices.
Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.
A bias for simplicity and clarity in solving complex problems.
Intellectual curiosity and willingness to collaborate.
Clear communication and collaboration across cross-functional teams.
