Machine Learning Specialist, SSA

Jobtailor; Montreal (administrative region); On-site; CAD 110,000 - 170,000; Full time

Job summary

Jobtailor is seeking a highly skilled Machine Learning Scientist to design, build, and validate ML and DL models for astrodynamics data. You will extract insights from constrained hardware imagery, identify dynamics in time series, and cluster complex patterns.

You will deploy solutions to cloud or edge devices and contribute robust software components. The role requires a PhD or Master’s, 5+ years of experience, strong ML fundamentals, and fluency in French and English.

Qualifications

• Master's degree or PhD in ML, 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.
• Good software engineering practices: Git, testing, CI/CD

Responsibilities

• Design, implement, and validate machine learning and 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 and cluster time series data.
• Infer astrodynamics parameters from partial observations and analyze long-term orbital trends.
• Deploy models to cloud infrastructures or embedded (edge) devices; develop robust, reusable software components.
• Collaborate with a multidisciplinary team and translate findings into actionable recommendations.

Skills

Machine Learning

Deep Learning

Python Programming

PyTorch

Time Series Analysis

Data Clustering

Astrodynamics

Cloud Infrastructure

Embedded Systems

Git

CI/CD

Probability

Statistics

Optimization

Production-Ready

Modularity

Education

Master's degree

PhD

Tools

Git

CI/CD

SciPy/NumPy

Job description

• 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
• 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