- Design, implement, and validate machine learning / deep learning models (supervised and unsupervised)
- Extract near-Earth astrodynamics data from noisy images under constrained hardware resources
- Identify time series representing an object's dynamics
- Cluster time series data
- Infer astrodynamics parameters from partial observations
- Analyze long-term trends and behavior of objects in orbit
- Deploy these models to cloud infrastructures or on embedded (edge) devices
- Develop robust, reusable software components (beyond research prototypes)
- Collaborate with a multidisciplinary team
- Maintain continuous scientific monitoring, synthesize key findings, and translate them into actionable recommendations for technical and non-technical audiences
- Evaluate solution performance using simulated and real data
- Clearly document algorithms, workflows, and results

Requirements

- Master's degree or PhD in Machine Learning, Physics, Electrical or Computer Engineering, Applied Mathematics, Aerospace, or a related field
- Minimum of 5 years of relevant experience
- Bilingual in French and English (written and spoken)
- Strong fundamentals in machine learning, probability, statistics, and optimization
- Hands-on experience with deep learning frameworks (e.g., PyTorch), Python and its scientific libraries, and developing and debugging custom models
- Positive command of deep learning architectures:



convolutional networks, attention-based architectures, adapting pre-trained models or designing models from scratch
- Ability to design proofs of concept with production constraints: modularity, scalability, and reproducibility
- Good software engineering practices: Git, testing, CI/CD

Core Competencies

Demonstrates expertise in designing and implementing machine learning and deep learning models, with a strong foundation in probability, statistics, and optimization. Proficient in deploying models to cloud infrastructures and embedded devices while adhering to software engineering best practices.

Highest-signal resume keywords

- Machine Learning Model Development
- Deep Learning Frameworks (PyTorch)
- Python Programming
- Astrodynamics Data Analysis
- Bilingual in French and English

ATS Optimization Keywords

Hard Skills

- Machine Learning
- Deep Learning
- Probability
- Statistics
- Optimization
- Time Series Analysis
- Data Clustering
- Algorithm Documentation
- Software Component Development
- Model Evaluation

Soft Skills

- Collaboration
- Communication

Certifications & Qualifications

- Master's Degree
- PhD

Industry Keywords

- Astrodynamics
- Embedded Systems
- Cloud Infrastructure
- Modularity
- Scalability

Tools & Technologies

- Git
- CI/CD
- Scientific Libraries

📌 Machine Learning Specialist, SSA (Montreal)
🏢 Jobtailor
📍 Montreal