Applied ML Engineer
Job Description: We are seeking a versatile and pragmatic Applied ML Engineer to contribute across a broad range of machine learning and perception tasks that power our edge-intelligent maritime systems. This role requires someone comfortable wearing many hats—from working with computer vision and sensor fusion models to building lightweight inference pipelines, designing experiments, and fine-tuning model behavior in production. You’ll work closely with a cross-functional team spanning hardware, software, and product to deliver real-world AI solutions that are robust, efficient, and reliable under challenging field conditions. This is an ideal position for someone who thrives on variety, rapidly shifting problem domains, and turning rough ideas into deployed systems.
Key Responsibilities:
Design, train, and evaluate models for tasks ranging from object detection and classification to anomaly detection and sensor-based inference
Optimize model architectures and inference pipelines for performance on embedded/edge hardware under compute and bandwidth constraints
Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation
Support prototyping and experimentation across a variety of AI subfields, including computer vision, signal processing, and multi-modal fusion
Implement real-time pipelines for processing sensor data on-device and in cloud environments
Develop tools and scripts for benchmarking, data visualization, and debugging ML model performance
Stay current with the latest research and tools in machine learning and evaluate their applicability to our product roadmap
Participate in code reviews, team knowledge sharing, and internal technical documentation
Qualifications (Preferred):
Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field. Bachelors candidates considered on a case by case basis.
4+ years of experience building and deploying machine learning models in
Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow
Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.)
Experience with edge or embedded ML deployments, including model compression and hardware-aware optimization
Familiarity with common ML practices including cross-validation, hyperparameter tuning, and model monitoring
Excellent debugging, experimentation, and problem-solving skills
Strong collaboration and communication skills with both technical and non-technical team members
Bonus: experience in maritime, aerospace, or other remote sensing domains
Work Environment:
This is a remote position with collaboration via online tools.
Flexible working hours with occasional deadlines requiring high availability.
Opportunity to work on innovative projects with a global impact.
Benefits:
Competitive salary
Flexible work hours and the option for remote work.
Opportunities for professional development and continued education.