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 Good 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 Positive 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 #J-18808-Ljbffr

📌 Borealis Ai Machine Learning Specialist (Montreal)
🏢 Borealis AI
📍 Montreal