Planet

Staff Applied AI Engineer

Planet

full-time

Posted on:

Location Type: Hybrid

Location: 🇺🇸 United States

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Salary

💰 $203,300 - $254,100 per year

Job Level

Lead

Tech Stack

AWSAzureBigQueryCloudGoogle Cloud PlatformNumpyPostGISPythonRemote Sensing

About the role

  • Develop and optimize multimodal LLM applications
  • Build embedding & similarity search pipelines at planetary scale—batch over xarray/Zarr cubes, index with BigQuery or PostGIS, etc.
  • Fine‑tune multimodal foundation models (e.g. CLIP-like models, MAEs, ViTs) for Earth‑observation tasks: change detection, land‑cover, semantic search, object counting
  • Design and execute machine learning workflows for geospatial analysis
  • Define success criteria and model benchmarks, adding instrumentation and model versioning where appropriate
  • Co‑design tool schemas & guardrails with backend engineers so LLM‑generated JSON plans execute safely
  • Collaborate with research scientists and engineers to design innovative models for remote sensing applications
  • Assist in automating the preprocessing and labeling geospatial data for AI tasks
  • Evaluate and improve algorithms for feature detection and classification in satellite imagery
  • Publish findings internally & externally (e.g. IGARSS, CVPR, etc.)

Requirements

  • Advanced degree in Computer Science, Artificial Intelligence, Remote Sensing, or similar
  • 12+ years expertise (or demonstrably equivalent) in Computer Science, Artificial Intelligence, Remote Sensing, or a related field
  • Experience with remote sensing, satellite image analysis, and geospatial data
  • Experience with rapid prototyping of AI Applications, especially search, LLMs, and agents, e.g. Google ADK, Model Context Protocol, CrewAI, Langchain, etc.
  • Extensive experience in developing and deploying AI/ML models, with a focus on geospatial applications and foundation models, embeddings, and frontier VLLMs
  • Excellent understanding of generative AI techniques, including LLMs and embeddings.
  • Proficient in Python and deep learning frameworks and high-performance distributed computing and IO frameworks using the python ecosystem, e.g. xarrays, dask, numpy, BigQuery, etc.
  • Expertise with computer vision and natural language processing techniques and familiarity with joint multimodal embeddings generators like CLIP and its more recent variants, as well as the operation and use of MMVLMs (multi-model vision-language models)
  • Familiarity with multi-dimensional geometry, statistics, linear algebra, optimization, and the internals of standard deep learning architectures
  • Fluency in full stack-development development and effective GUI implementation for web applications which rely on back-end scientific and AI systems
  • Knowledge of geospatial data formats and analysis tools (e.g., GDAL, GeoPandas, Rasterio)
  • Excellent problem-solving skills and ability to work in a dynamic research environment
  • Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and big data workflows
  • Excellent communication and collaboration skills

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
multimodal LLM applicationsembedding pipelinessimilarity search pipelinesmachine learning workflowsAI/ML modelsgenerative AI techniquesPythondeep learning frameworkscomputer visionnatural language processing
Soft skills
problem-solvingcommunicationcollaboration