LeoLabs

LeoLabs

LeoLabs is transforming low Earth orbit with LeoLabs Vertex™, offering unmatched coverage and real-time tracking for safe and efficient space operations worldwide.

Aerospace & Defense
51-250
Founded 2016
$82M raised

Description

  • Design, build, and operate AI- and machine learning-powered systems for real-time space domain awareness.
  • Develop scalable data, feature, and training pipelines that support production AI workflows.
  • Deploy machine learning models into operational systems and maintain them in production.
  • Integrate AI capabilities into customer-facing and mission-critical software systems.
  • Transform large-scale sensor and orbital datasets into systems that detect patterns, identify anomalies, and generate predictive insights.
  • Own the full lifecycle of AI solutions, from problem framing through monitoring and continuous improvement.
  • Partner with data engineering to improve data quality, scalability, and pipeline reliability.
  • Lead the development of new models and enhancements to existing systems.
  • Monitor model performance and drive optimization through feature engineering, tuning, and experimentation.
  • Mentor junior team members and help establish team best practices for applied AI.

Requirements

  • B.S. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Physics, or equivalent experience.
  • 5-7 years of experience in software engineering, machine learning engineering, or applied AI roles.
  • Strong proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
  • Advanced experience with SQL and large-scale data processing.
  • Proven experience developing and deploying production-grade machine learning models.
  • Experience with large-scale distributed data platforms such as Databricks or Spark.
  • Strong understanding of statistical modeling, machine learning algorithms, and experimental design.
  • Experience designing and implementing feature engineering pipelines and training workflows.
  • Familiarity with MLOps practices, including model versioning, monitoring, and lifecycle management.
  • Up-to-date familiarity with the latest developments in Agentic AI.
  • Experience building Agentic AI systems for time-series, anomaly detection, or predictive modeling, preferred.
  • Familiarity with Databricks ML, MLflow, or similar ML lifecycle platforms, preferred.
  • Experience deploying models in real-time or near-real-time production systems, preferred.
  • Background working with sensor, telemetry, or geospatial/orbital datasets, preferred.
  • Experience mentoring junior data scientists or leading technical initiatives, preferred.
  • Familiarity with streaming data systems such as Kafka or Spark Structured Streaming, preferred.
  • Background in orbital mechanics, aerospace, physics, or applied mathematics, preferred.
  • Active U.S. security clearance or ability to obtain one, preferred.

Benefits

  • Flexible remote/hybrid work opportunities for a global workforce.
  • Unlimited paid time off for most roles.
  • Competitive salary and equity packages.
  • Comprehensive health, dental, and vision coverage.
  • Opportunity to work on complex, meaningful missions with real-world impact.
  • Access to the forefront of commercial space operations and defense innovation.

Interested in this position?

Apply directly on the company website

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