Machine Learning Engineer

Spear AI is a growing defense contracting company dedicated to delivering cutting-edge solutions that support our nation's security. They are seeking a skilled Machine Learning Engineer to build and deploy production ML systems for maritime domain awareness, working on real-world projects that impact warfighter capabilities.

Responsibilities:

• Design, train, and optimize machine learning models using PyTorch
• Deploy models to production environments in the cloud and at the edge
• Build and maintain ML pipelines for training, evaluation, and inference
• Integrate machine learning models into real-time and batch processing systems
• Optimize model performance for accuracy, latency, and resource constraints
• Implement model monitoring, versioning, and deployment strategies
• Work with signal processing data and time-series analysis
• Improve local development and CI/CD for ML workflows using modern tooling and GitHub Actions

Requirements:

• Several years of experience with Python and machine learning frameworks
• Expertise in PyTorch for building and training neural networks
• Experience training and serving models in cloud environments (AWS, Azure, GCP)
• Proficiency with MLOps practices including experiment tracking, model versioning, and deployment
• Experience with model optimization for production performance and scale
• Knowledge of Docker and Kubernetes for containerized deployments
• Familiarity with REST APIs and model serving frameworks
• Understanding of CI/CD pipelines for ML systems
• Strong fundamentals in machine learning including model architecture design, training strategies, and evaluation
• You're fanatical about polish. Every detail matters. You love to make sure your code is linted, formatted, fully typed, and has comprehensive test coverage
• You care about correctness. You take pride in the fact that your models perform reliably and downstream consumers trust your predictions
• You obsess over performance. You daydream about model latency, throughput, and efficient inference pipelines
• You dive deep. It's important for you to really know how things work. You're always building prototypes and setting up experiments to reinforce your understanding
• You live on the bleeding edge. You've got a long list of upcoming ML techniques and frameworks you're excited about and can't wait to experiment with new approaches
• You're a great teacher. You know how to break down complex ML concepts for a specific audience and make it click with them in a way that gets them excited
• You must be willing to receive a Secret or Top Secret/SCI security clearance
• Experience with reinforcement learning algorithms and applications
• Digital signal processing experience
• Background in time-series analysis or sensor data processing
• Experience with edge deployment and model optimization for resource-constrained environments
• Familiarity with distributed training across multiple GPUs/nodes
• Experience with model compression techniques (quantization, pruning, distillation)
• Contributions to open-source ML projects or research publications
• Experience in defense, aerospace, or other regulated industries