Entry Level Computer Science Environmental Jobs in Toronto, ON

Perceptive Space is an AI and aerospace company building space environment intelligence for commercial, civil, and defence operators.

We are building AI-powered space weather forecasting and decision intelligence systems for satellite operators, launch providers, defence and national security customers, and other space infrastructure teams. Our goal is to help customers respond to space environment conditions that affect satellite operations, mission design, launch planning, orbital performance, and mission resilience.

• This is an early-stage startup role. Priorities will change, we move quickly, and urgency matters. We are looking for someone who is energized by high ownership, high expectations, and the pace required to be part of an ambitious company.
• This role sits at the intersection of research, engineering, and product. One day, you may be reading papers and helping develop foundational AI models for the space environment. The next day, you may be optimizing a production ML pipeline to ensure customers receive accurate, reliable, and operationally useful forecasts.
• The position is fully remote (in Canada only).

• Build, improve, and ship AI models for space environment forecasting and operational decision support.
• Improve existing models, develop new ones where needed, and move promising experiments into production.
• Apply modern deep learning techniques to sequential, temporal, scientific, and sensor datasets.
• Use AI-assisted coding tools, automation, and modern development workflows to move faster, debug faster, test better, and accelerate experimentation.
• Turn new research ideas into working systems quickly.
• Improve ML pipelines so models are easier to train, evaluate, deploy, monitor, and maintain.
• Define practical evaluation strategies, validation workflows, and performance metrics for operational AI systems.
• Communicate decisions, results, tradeoffs, risks, and next steps clearly and often.

• A startup mindset with high ownership and high-velocity, hands-on execution..
• High leverage with AI-assisted coding tools: you know how to use LLMs and modern development tools to ship faster, debug faster, write better tests, and accelerate experimentation without compromising code quality.
• Minimum 2+ years of industry experience with modern deep learning architectures for sequential, temporal, or multimodal data, including attention-based models, transformers, representation learning, and model pretraining or fine-tuning workflows.
• Experience with ML infrastructure tools such as cloud platforms, Airflow, Docker, and related production systems.
• Advanced degree in engineering, applied mathematics, computer science, or a related technical discipline.
• Strong written and verbal communication skills, including the ability to clearly explain technical decisions, experimental results, tradeoffs, and risks.
• Ability to operate with ownership, urgency, and independence required in a startup environment.

• Experience working in early-stage startups.
• Experience with C, Rust, or other performance-oriented languages.
• Experience working on aerospace and defence applications.

• You will have meaningful ownership in an early-stage company building critical infrastructure for the space economy.
• You will help build a category-defining product for the operators and missions that depend on space, from commercial aerospace to defence.
• Your work will directly shape how satellites, launch vehicles, and other space infrastructure are designed, operated, and protected, with real impact for life on Earth and humanity's progress in space.
• We are a fully remote team solving technically deep problems with urgent, real-world relevance
• Ample opportunity for growth

Our interview process is designed to help both sides assess fit for the role, the team, and the way we work.

• Initial Interview
  A conversation with someone from our team to discuss your work experience, working style, interest in the role, and to share more about Perceptive Space.
• Technical Panel
  A discussion with a few members of our team to understand the breadth and depth of your ML, engineering, and problem-solving experience, especially in production and deployment settings.
• Founder Interview
  A conversation with our Founder about the company, role expectations, and what success looks like in this position.
• Paid Working Session
  Later-stage candidates may be invited to spend a day with us completing a working session on a realistic problem related to the role. You will be compensated for your time. This session is designed to help both sides assess mutual fit.
• Offer Call
  A final conversation to discuss compensation, benefits, stock options, start date, and next steps.
• Compensation Base salary will be determined based on seniority, primary work location, and relevant work experience. This role is also eligible for dental, vision and healthcare benefits and stock options.