Data Scientist / ML Engineer

Company Introduction

Since our founding in 2008, Axelspace has been driven by the vision “Space within Your Reach.” We have continuously refined our unique micro-satellite technology with the goal of democratizing space technology to a wider population across the globe.

AxelGlobe started in 2019 to provide a new Earth observation infrastructure by launching multiple micro-satellites for its higher data frequency. In addition to a satellite launched in 2019, we have successfully launched Japan’s first four mass-produced satellites in 2021. We are currently operating a constellation of five Earth observation satellites, capturing images with a 55 km swath every 2 to 3 days, and providing data and solutions.

Building on our experience with our dedicated satellite business, we launched AxelLiner in 2022 to provide a one-stop service for satellite development and operation, helping customers quickly actualize their satellite business. In 2024, we launched our first demonstration satellite, “PYXIS,” bringing our total number of developed and operated satellites to 11.

Additionally, we received the highest award, the Minister of Economy, Trade and Industry Award at the 22nd JAPAN VENTURE AWARDS in 2022, and was selected as part of “Technology Fast 50 2023 Japan” in 2023.

As the world faces pivotal stages in democratizing satellite development and satellite imagery use, we are actively looking for talented individuals who can enjoy the challenges to make a global impact with micro-satellite technology. Hit that apply button and let’s make our vision come true together!

Job Description

About the Role

The successful candidate will be a member of the team responsible for developing end-to-end analytics solutions using Earth observation data, integrating them into the AxelGlobe platform and client-facing products. The role involves transforming complex satellite data into meaningful insights, with a strong emphasis on machine learning supported by data engineering, requiring expertise across both areas to deliver practical, high-impact solutions.

Prior experience in earth observation is not required; candidates with strong backgrounds in computer vision, machine learning, or data science are highly encouraged to apply.

Job Responsibilities

• Design end-to-end solutions in collaboration with the business team to address real-world problems.
• Research, prototype, and validate data processing and analysis algorithms using Earth observation data (e.g., optical imagery, weather, radar).
• Develop and productionize scalable data pipelines and full-stack analysis systems.
• Deliver and maintain deployed solutions, ensuring they meet business requirements and operational needs.

Mandatory Requirements

• 3+ years of hands-on experience in image processing within the computer vision domain, including tasks such as classification, regression, segmentation, object detection, and time series analysis.
• Implementing machine‑learning models/algorithms for real‑world problems (beyond basic library‑only use), and hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Building and operating image‑data pipelines/MLOps.
• Experience with standard tools and practices of collaborative software development: writing maintainable code, version control (git), code review, testing (e.g., pytest), containers (Docker), CI/CD tools.
• Strong problem-solving and analytical thinking skills.
• Ability to conduct technical discussions in English.

Favorable Skills / Experiences

• 2+ years of professional or research experience in earth observation data processing.
• Experience with the following GIS libraries/tools (or equivalents): GDAL, Rasterio, Geopandas, Shapely.
• Familiarity with AWS services and infrastructure-as-code tools such as Terraform.
• MSc or PhD in a relevant computational field (Computer Science, Remote Sensing, Physics, etc.)
• Deep knowledge of statistics.
• First‑author experience in peer‑reviewed publications.
• Business-level proficiency in Japanese.
• Interest in the space industry, curiosity about other aspects of satellite operations, and openness to cross-team collaboration.

Tech Stack

• ML: PyTorch, TensorFlow (CV tasks : segmentation, detection, time series, etc.)
• Geospatial : GDAL, Rasterio, GeoPandas, Shapely
• Data/MLOps : Data pipelines, AWS, Terraform
• Engineering : Python, Git, Docker, CI/CD, pytest

Unique Challenges

• Earth Observation Analytics & Product Innovation :
  • Design and build full-stack analytics solutions using multi-modal Earth observation data (optical satellite imagery, SAR radar, and weather data) to create new value on the AxelGlobe platform.
  • Research, prototype, and productionize advanced computer vision and machine learning algorithms to solve complex real-world problems.
  • Rapidly translate business needs into scalable, production-ready EO data products.
• End-to-End Data Pipeline Ownership :
  • Develop and operate robust, large-scale data processing pipelines and MLOps workflows that handle petabyte-scale satellite imagery and geospatial data.
  • Build reliable, maintainable, and observable systems that move seamlessly from experimentation to production.
• Cross-Functional Impact & Space-Tech Collaboration :
  • Work closely with business and product teams to identify high-impact use cases and turn cutting-edge EO insights into practical solutions.
  • Help shape the future of commercial Earth observation analytics while gaining exposure to satellite operations and the broader space industry.