AI/ML Manager

Why LeoLabs?

• Lead by doing – Design, build, and deploy production-grade ML models and pipelines while mentoring a small team of engineers and data scientists.
• Architect scalable AI systems –Build end-to-end AI solutions from data ingestion through inference, ensuring reliability, observability, and performance across distributed environments.
• Collaborate cross-functionally – Work closely with product, data, and platform engineering teams to translate complex business challenges into ML-driven solutions.
• Own the ML lifecycle – Oversee model training, validation, deployment, and continuous monitoring using modern MLOps practices and tooling.
• Experiment and innovate – Apply state-of-the-art machine learning and generative AI techniques to improve product capabilities and customer outcomes.
• Establish best practices – Set standards for reproducible research, model explainability, and responsible AI implementation.
• Optimize performance – Tune models and infrastructure for accuracy, latency, and cost.
• Provide technical leadership and mentorship –Guide architectural decisions, design reviews, and technical standards.
• Communicate Impact – Present insights to technical and non-technical stakeholders, influencing product strategy through data-driven recommendations.
• Stay ahead of the curve – Evaluating emerging ML frameworks, architectures, and tools to accelerate innovation

• Bachelor’s degree in computer science, Applied Mathematics, Physics, or a related technical field.
• 6+ years in software engineering, data science, or applied machine learning.
• 2+ years managing technical teams.
• Proven experience building and deploying ML models using Python, PyTorch, TensorFlow, or similar frameworks.
• Experience with data pipelines and orchestration tools (e.g., Databricks).
• Strong background in sensor data processing, time-series analysis, and computer vision or tracking algorithms.
• Experience deploying ML models in production environments, including cloud or containerized deployments (AWS GovCloud, Azure Gov, or on-prem).
• Demonstrated leadership, mentorship, and project execution skills.
• Excellent communication and technical documentation skills.
• Eligibility for US security clearance.

• Advanced degree (MS or PhD) in Machine Learning, Aerospace Engineering, or related field.
• Familiarity with orbital mechanics concepts (e.g., TLEs, ephemerides, conjunction analysis, or space object classification).
• Experience integrating LLMs, transformers, or generative AI solutions into production systems.
• Familiarity with vector databases, retrieval-augmented generation (RAG), or AI agent frameworks.
• Experience with real-time inference, model optimization, and edge deployment.
• Active US security clearance.

• Gain a deep understanding of the company’s ML infrastructure, datasets, and product goals.
• Review existing models, pipelines, and codebases.
• Establish trust and cadence with the engineering and data teams.
• Deliver a proposal outlining quick wins for improving model performance or process efficiency.

• Take over day to day management of the Insights AI/ML team.
• Lead implementation of at least one model improvement or new ML feature from design through deployment.
• Define standards for model reproducibility, code quality, and experiment tracking.
• Partner with data engineering to ensure robust, high-quality data pipelines.

• Own the end-to-end lifecycle of multiple ML systems and features.
• Demonstrate measurable impact (e.g., improved model accuracy, reduced latency, or new product capability).
• Mentor team members and contribute to hiring and growth of the AI/ML capability.

• Lead the roadmap for AI/ML initiatives across the product line.
• Establish best practices for experimentation, model governance, and continuous improvement.
• Deliver multiple ML-powered features in production with clear business ROI.
• Serve as the technical authority and go-to expert for all AI-related initiatives within the organization.

Perks and Benefits