AI Engineer
HighlightTA
AWSAzureCloudGoogle Cloud PlatformPythonPyTorchTensorflow
Machine Learning Scientist
Perceptive Space
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇨🇦 Canada
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
AWSAzureCloudGoogle Cloud PlatformJavaPythonPyTorchRayTensorflow
About the role
- 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, Aerospace, 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 start ups 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
Benefits
- Opportunity to work at the frontier of AI and aerospace, building first-of-its-kind products.
- Competitive stock option compensation
- Top-tier health and benefits coverage
- Fully remote team
- Opportunities to lead technical efforts as the team scales.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningtime series forecastingspatio-temporal dynamicsfeature engineeringPythondeep learningmodeling temporal dataC/C++Javauncertainty quantification
Soft skills
communicationcollaborationexplanation of model behaviorcross-disciplinary teamworkproblem-solvingadaptabilityleadershiporganizational skillsstakeholder engagementcritical thinking
