Machine Learning Scientist

1 week ago

Canada Perceptive Space Full time

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. 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 2+ years of industry experience following a Master’s or Ph.D. in Physics, Aerospace, Electrical Engineering, Applied Math, or a related field. Experience in fast‑paced, high‑ownership ML roles within a startup or 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. Additional Requirements The role is fully remote, but you are expected to be available during Eastern Time working hours. 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. #J-18808-Ljbffr

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