About Us
At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI, robotics, distributed sensing, and resilient data systems, we unlock capabilities that were only recently impossible. Our open-ocean systems enable vessels and coastal infrastructure to sense, compute, and communicate—enhancing maritime domain awareness for those who need it most.
Role Summary
Quartermaster AI is hiring across Applied Machine Learning, Machine Perception, RF Machine Learning, Data Science, and Data Engineering to build the intelligence layer of our maritime sensing platform.
These roles span the full AI and data lifecycle—from sensor data ingestion and dataset creation to model development, edge deployment, analytics, and scalable data infrastructure. Candidates will focus in one of the areas below based on experience and interest.
Focus Areas:
Applied Machine Learning
- Design, train, evaluate, and deploy models for detection, classification, anomaly detection, and sensor-based inference
- Build lightweight inference pipelines for edge and cloud environments
- Support experimentation across computer vision, signal processing, and multi-modal fusion
- Develop tools for benchmarking, debugging, visualization, and model performance analysis
Best fit: Applied ML Engineer
Machine Perception
- Lead development of computer vision and perception models for vessel detection, classification, tracking, and scene understanding
- Optimize real-time inference pipelines for edge devices under compute and bandwidth constraints
- Apply active learning, domain adaptation, synthetic data, and sensor fusion strategies
- Own model evaluation, benchmarking, validation, versioning, and traceability across field conditions
Best fit: Machine Perception Engineer
RF Machine Learning
- Build ML systems for RF signal detection, classification, tagging, and vessel activity tracking
- Develop RF dataset curation pipelines, including AIS-correlated labeling, synthetic RF data, and augmentation strategies
- Define the interface between DSP outputs and ML model inputs
- Optimize RF models and inference workflows for edge compute hardware
Best fit: Senior RF Machine Learning Engineer
Data Science & Analytics
- Analyze large-scale field deployment data to identify trends, anomalies, performance gaps, and operational insights
- Build analytics pipelines, dashboards, reports, and intelligence products
- Partner with perception and ML teams to support feedback loops, retraining, edge-case detection, and model monitoring
- Develop metrics for evaluating detection, perception, fusion, and system performance over time and space
Best fit: Staff Data Scientist
Data Engineering & Infrastructure
- Design, build, and maintain secure, scalable batch and real-time data pipelines
- Architect cloud infrastructure across AWS and Azure for mission-critical data systems
- Support high-throughput ingestion and processing of sensor, radar, SDR, geospatial, and maritime data
- Implement database, replication, failover, disaster recovery, and secure data-separation strategies
- Develop infrastructure-as-code, automated deployments, monitoring, and cost optimization practices
Best fit: Senior Data Engineer
Cross-Functional Collaboration (All Roles)
- Work closely with hardware, RF, DSP, software, product, and operations teams
- Translate research, field data, and customer needs into production-ready systems
- Contribute to code reviews, technical documentation, model validation, and system design decisions
- Support continuous improvement of real-world AI systems operating in challenging maritime environments
What We’re Looking For
- Experience in machine learning, perception, RF ML, data science, or data engineering
- Strong programming skills, especially Python; additional experience with C/C++, TypeScript, SQL, or cloud tooling is valuable depending on role
- Experience working with production systems, real-world data, and technical ambiguity
- Strong debugging, experimentation, communication, and problem-solving skills
- Ability to collaborate across technical and non-technical teams
Preferred:
- Experience with PyTorch, TensorFlow, scikit-learn, pandas, SQL, or related ML/data tools
- Experience with edge/embedded ML, model compression, quantization, pruning, or TensorRT
- Familiarity with RF data, IQ/spectral representations, GIS, geospatial data, time-series data, radar, SDR, or sensor fusion
- Experience with AWS, Azure, MongoDB, PostgreSQL, Kafka, Kinesis, Spark, Flink, Docker, Kubernetes, Terraform, or related infrastructure tools
- Background in maritime, aerospace, defense, remote sensing, robotics, or regulated environments
Additional Requirements
- Some roles require eligibility to obtain and maintain a security clearance
Work Environment
- Full-time, Boston-based, hybrid
- Flexible working hours with occasional high-intensity deadlines