Machine Learning Engineer
| Company | Airspace Intelligence |
|---|---|
| Location | Boston, MA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Junior, Mid Level |
Requirements
- 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
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
Preferred Qualifications
-
No preferred qualifications provided.