• Lead the full AI product lifecycle: from identifying user pain points and defining KPIs to training foundation models and measuring real-world impact. • Design and execute rigorous experiments (A/B tests, causal inference) to validate new AI features and drive product optimizations. • Partner with Product Managers, Designers, and Engineers to translate business goals into a technical AI roadmap, ensuring our model development stays aligned with user needs and technical feasibility. • Build and maintain production-grade training pipelines and evaluation harnesses using reproducible, well-tested code. • Analyze production feedback and user interactions to iteratively refine models and maximize value. • Foster a collaborative culture: mentor teammates, simplify complex problems, and drive iterative, high-impact delivery.

• Design and implement scalable data architecture. • Develop ETL processes • Develop scripts in SQL and Python • Data modeling • Data analysis • Technical leadership • Project leadership • Completed higher education • Advanced / fluent English • Experience with Azure Data Factory, Databricks, and Azure Storage • Handling Parquet files • Spark • Senior-level Python • SQL • SQL Server and Oracle databases • Knowledge of DevOps: pipelines, branch creation, releases

Role Description 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). What You'll Do - 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. Qualifications - 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. Nice to Have - Experience working in early-stage startups. - Experience with C, Rust, or other performance-oriented languages. - Experience working on aerospace and defence applications. Benefits - 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. Interview Process - 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.

• Liderar a construção, implementação e validação de modelos de Machine Learning focados em gerar alto impacto e valor para o negócio. • Aplicar métodos estatísticos avançados para exploração, visualização e interpretação de dados, traduzindo complexidade em *insights* acionáveis. • Utilizar ferramentas de IA (como GitHub Copilot, Cursor, etc.) no dia a dia para ganhar velocidade na codificação, criação de testes e documentação de projetos. • Monitorar rigorosamente a performance das soluções desenvolvidas, interpretando métricas complexas e propondo melhorias contínuas. • Atuar junto à engenharia no suporte a pipelines e no *deploy* de modelos em ambientes Cloud, garantindo escalabilidade. • Apresentar resultados técnicos e de negócio de forma estruturada, traduzindo o "matematiquês" para *stakeholders* e áreas de negócio.