We are seeking a talented Machine Learning Engineer to join our team and help develop ML models that turn complex sensor data into actionable insights, driving the next generation of space-tech applications.

Key Responsibilities:

• Design and deploy machine learning models to analyze and interpret physics-based data, especially in the areas of space, missile defense, PNT, and communications.
• Implement numerical modeling techniques to simulate physical systems, integrating these with ML approaches for enhanced predictive accuracy.
• Collaborate with cross-functional teams to understand project requirements and to translate complex, physics-based processes into ML solutions.
• Optimize model performance and scalability for deployment on cloud platforms (AWS).
• Implement data preprocessing, feature engineering, and data augmentation techniques to improve model accuracy.
• Build, maintain, and improve data pipelines, ensuring the seamless flow of data from ingestion to deployment.
• Monitor and evaluate model performance post-deployment, making updates as needed for continuous improvement.
• Ensure models adhere to security, privacy, and regulatory standards.

Required Qualifications:

• 2+ years of experience in ML Engineering role.
• Proven experience in developing and deploying machine learning models using Keras, TensorFlow, PyTorch, Jax, or similar modern frameworks.
• Experience building numerical and ML models of physics-based systems with exposure to large datasets or distributed systems.
• Strong background in data science, including experience with data preprocessing, feature engineering, and model evaluation.
• Proficiency in cloud platforms (AWS) for deploying and scaling machine learning models.
• Familiarity with containerization tools like Docker for model deployment.
• Experience putting ML models into production. Knowledge of MLOps practices, including CI/CD for ML, model versioning, and automated monitoring.
• Experience building ML models of time-series data including sensor and imaging data
• Strong problem-solving and communication skills, and the ability to work collaboratively in a fast-paced environment.

Preferred Qualifications:

• Background in electrical engineering
• Experience in Aerospace and Defense sector
• Experience in signal processing
• Experience building Physics-Informed ML models (PINN, DeepOnet, FNO)
• Relevant certifications in cloud platforms or machine learning frameworks
• Experience with real-time data processing (Spark, Flink, Dataflow, Kafka, Pulsar, etc.)
• Experience building with AWS Sagemaker
• Experience debugging and maintaining live production systems on Kubernetes