Role: Geospatial Data Scientist
Employment Type: Full Time
Educational Qualification: B.S. or M.S. in Computer Science, Remote Sensing, Geoinformatics, Environmental Science, Physics, or a related field (or equivalent practical experience).
Work Experience:
- 0–3 years of experience applying machine learning to geospatial, remote sensing, or other scientific/spatial data (internships and academic research projects count).
Role Description
We are seeking a Geospatial AI Practitioner to join our Analytics team. This is a hands-on, build-and-execute role: you'll implement, train, evaluate, and validate machine learning and deep learning models on Pixxel's hyperspectral satellite data and allied geospatial modalities, working under the guidance of senior scientists on both production and R&D projects. You'll get exposure across the full stack of our intelligence platform, from raw data preparation and physically-informed modeling, through foundation-model fine-tuning, to the embedding- and retrieval-based systems we're building to search and reason over petabytes of imagery.
Responsibilities & Duties
- Build, train, and evaluate geospatial AI/ML models for applications such as crop classification, forest/biomass estimation, water quality retrieval, invasive species and land cover mapping, and change detection, under the direction of senior team members.
- Prepare and preprocess hyperspectral, multispectral, and SAR datasets: co-registration, atmospheric/radiometric correction, glint and shadow masking, chip generation, and label wrangling.
- Fine-tune and benchmark existing geospatial and foundation models (e.g., Prithvi, SatMAE, Segment Anything) on new tasks and datasets.
- Contribute to embedding- and segmentation-based approaches for large-scale image search and object-centric retrieval, as part of our broader platform's semantic search capabilities.
- Integrate multimodal data sources: hyperspectral, SAR, weather, and ground-truth/in-situ data, into modeling pipelines.
- Support transfer learning and domain adaptation efforts to extend models from well-labeled to label-scarce regions.
- Validate models rigorously against ground truth and known physical/spectral relationships, and help build out shared validation tooling and benchmarks used across the team.
- Write clean, well-documented, and reasonably efficient Python code, and contribute to shared libraries and pipelines used by the broader analytics team.
- Collaborate with data engineers, solutions scientists, and product managers to move models from notebook to production pipeline.
- Document methodology, maintain experiment tracking, and clearly communicate results to both technical and non-technical stakeholders.
Desirable Skills & Certifications
- Solid Python programming skills and working knowledge of ML libraries such as PyTorch, TensorFlow, or scikit-learn.
- Foundational understanding of spectral data — how absorption features, band selection, or atmospheric/surface effects influence what a sensor measures — and willingness to build deeper hyperspectral expertise on the job.
- Familiarity with core geospatial data handling: raster/vector formats, coordinate reference systems, and tools such as Rasterio, xarray, GDAL, or PyProj.
- Understanding of fundamental ML concepts (model training, validation, evaluation metrics) and willingness to learn domain-specific methods (radiative transfer, crop phenology, embedding-based retrieval) on the job.
- Comfortable working with large raster/imagery datasets and basic cloud or HPC compute environments.
- Strong communication skills and eagerness to learn from and collaborate closely with senior scientists.
- Coursework, research, or project experience involving hyperspectral, multispectral, or SAR satellite imagery.
- Exposure to cloud-based EO platforms such as Google Earth Engine, SentinelHub, or OpenEO.
- Familiarity with geospatial foundation models, self-supervised learning, or embedding-based image retrieval concepts.
- Experience with STAC catalogs or other geospatial data cataloging/indexing systems.
- A portfolio, GitHub repo, thesis, or publication demonstrating applied geospatial, remote sensing, or spectral analysis.
Candidate Acumen
This role is a strong fit for someone early in their career who wants to build real depth in geospatial and spectral AI on production satellite data and real customer problems, rather than toy datasets. You'll work alongside and be mentored by a team of PhD-level remote sensing scientists spanning the US and India, with a clear path to grow into deeper technical ownership as you build track record, whether that's foundation-model development, a specific vertical (e.g., aquatic/water quality, forestry, agriculture), or platform-level engineering.
Benefits
Health insurance coverage
Unlimited leaves & flexible working hours
Role-based remote work and work-from-home benefit
Relocation assistance
Professional Mental Wellness services
Creche facility for primary caregivers (limited to India)
Employee Stock Options for all hires
About Pixxel
Pixxel is a space data company and spacecraft manufacturer redefining Earth observation with hyperspectral imaging. The company’s first three commercial hyperspectral satellites—Fireflies—deliver imagery at 5-meter resolution and 135+ spectral bands, providing 50x richer detail than traditional Earth observation systems and unlocking insights across agriculture, climate, energy, environment, and more.
Once fully deployed, Pixxel’s constellation of 18-24 satellites will capture imagery across up to 250 bands in VNIR and SWIR ranges, with a 40 km swath and daily global revisit capability. Pixxel’s most unique strength is its full-stack approach, integrating every layer of the value chain from satellite hardware and manufacturing to AI-powered analytics.
Pixxel’s satellite constellation is complemented by Aurora, its in-house Earth Observation Studio that simplifies satellite imagery analysis and democratises remote sensing for all. Designed to make hyperspectral data more accessible, Aurora by Pixxel combines high-frequency imagery with AI-powered tools to generate actionable insights, even for users without technical backgrounds. The third pillar of Pixxel’s ecosystem is its in-house satellite manufacturing capability. Beyond building its own spacecraft, Pixxel also provides satellite systems and subsystems to other organisations. This dual capacity sets it apart in a sector where most companies focus on either payload design or data operations, but not both.
Pixxel’s team is young but deeply mission-aligned, with a culture rooted in curiosity, speed, and long-term thinking. As the company grows its constellation and expands Aurora, the focus remains on making space-based insights practical, scalable, and genuinely helpful so that the health of the planet becomes measurable and action becomes possible.
Pixxel was the only Indian startup selected for the Techstars Starburst Space Accelerator in Los Angeles and has been recognised in TIME’s Best Inventions of 2023, Fast Company’s Most Innovative Companies, and Via Satellite’s Top Innovators list.
For more information, see our Careers page or follow Pixxel on LinkedIn and X/Twitter.
Culture
Pixxel is an organization where we enable our employees to work on world-changing problems that they are passionate about, and a place where they can be their best selves day in and day out while ensuring they have fun every day.
Central to our employee-first ethos is a commitment to ensuring that every team member feels valued and heard. We nurture a healthy and supportive work environment where we prioritise well-being and growth in all aspects of life
Link to Pixxel in Media