Machine Learning Scientist

Perceptive Space; Canada; Remote; CAD 110,000 - 140,000; Full time

Job summary

Perceptive Space is seeking a Machine Learning Researcher to join their innovative team. You will develop foundational technology for satellite operations in a challenging environment, focusing on improving models and working collaboratively with engineers. This fully remote role offers competitive compensation and is ideal for proactive candidates with a strong educational background in related fields.

Benefits

Generous stock option compensation

Top-tier health and benefits coverage

Fully remote team

Opportunities to lead technical efforts

Qualifications

• 4+ years of industry experience in ML roles required.
• Experience in Python and deep learning frameworks essential.
• Strong background in modeling temporal data necessary.

Responsibilities

• Build and evaluate machine learning models for forecasting.
• Collaborate across disciplines to deploy ML systems.
• Design experiments to assess model generalization.

Skills

Machine learning

Python

Deep learning

Modeling temporal data

Software engineering

Data integration

Education

Master's or PhD in Physics, Electrical Engineering, Applied Math

Tools

PyTorch

TensorFlow

MLflow

Ray

Dask

Numba

Job description

Perceptive Space Systems is building a decision intelligence platform to help satellite and launch operators navigate the growing risks posed by space weather and the space environment. We work at the intersection of aerospace, AI, and real-time systems, combining cutting-edge modeling, sensor fusion, and autonomy to improve operational resilience in orbit. Read more here.

Join us at the frontier of space technology and AI

You will build the foundational technology required for satellites, launch vehicles, and human missions to operate safely and efficiently in the harsh space environment

As part of our small, high-velocity team, you'll work at the intersection of aerospace, autonomy, and applied AI, solving real-world challenges with immediate mission impact.

This role is ideal for entrepreneurial engineers who want to build from first principles, move fast, and own core systems end-to-end and who take initiative, thrive in ambiguity, and be part of a demanding startup environment.

What You'll Do

• Build and evaluate machine learning models for time series forecasting and spatio-temporal dynamics
• Design experiments to assess model generalization, uncertainty, and relevance to physical systems
• Integrate domain knowledge, external signals, or prior constraints to improve model performance
• Optimize model performance through feature engineering, architecture tuning, and validation strategies
• Collaborate with aerospace engineers, software engineers, and domain experts to deploy ML systems in production
• Stay up to date with developments in ML for dynamic systems, forecasting, and scientific ML
  
  
  

Requirements

• 4+ years of industry experience following a Master's or PhD in Physics, Electrical Engineering, Applied Math, or a related field
• Experience in fast-paced, high-ownership ML roles within a startup or a fast-moving, demanding startup-like environment
• Proficient in Python and experienced with deep learning frameworks such as PyTorch or TensorFlow
• Experienced with tools and frameworks like MLflow, Ray, Dask, and Numba
• Strong background in modeling temporal or sequential data (e.g., time series forecasting, state-space models, signal processing)
• Comfortable working with multidimensional datasets and integrating domain context into modeling
• Strong general foundations in software engineering, including coding standards, code reviews, source control (e.g., Git), build processes, and testing
• Experience deploying ML solutions onto cloud platforms (e.g., AWS, GCP, Azure)
• Track record of contributing to the successful delivery of production-ready ML models
• Able to explain model behavior, assumptions, and limitations clearly to both technical and non-technical stakeholders
• Excellent communication and collaboration skills; able to work effectively across disciplines
  
  
  

Bonus If You Have

• Experience working in early-stage or cross-disciplinary R&D teams
• Experience working on scientific modeling, simulation data, or systems governed by physics or control principles
• Familiarity with techniques for uncertainty quantification and physics-informed ML
• A track record of publications or contributions to open-source ML libraries
• Proficient in C/C++ and Java
  
  
  

Additional Requirements

• Due to contract requirements, applicants must be Canadian citizens or permanent residents
• The role is fully remote, BUT you are expected to be available during Eastern Time working hours.
  
  
  

Benefits

• Generous stock option compensation
• Top-tier health and benefits coverage
• Fully remote team
• Opportunities to lead technical efforts as the team scales

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Other

• Industries

  IT Services and IT Consulting

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