Role

Responsibilities

Representation Learning

• Design and implement self-supervised learning (SSL) objectives (e.g., Masked Autoencoders, DINO-style methods, contrastive learning) tailored for geospatial data
• Develop multi-modal representations spanning optical, SAR, elevation, and derived signals
• Ensure representations transfer effectively across tasks such as segmentation, classification, and change detection
• Design evaluation strategies to measure generalization across geographies, sensors, and time

Model Development & Scaling

• Design and scale models based on Vision Transformers (ViT), hybrid architectures, or State Space Models (e.g., Mamba) to large parameter regimes
• Apply modern training techniques such as RMSNorm, FlashAttention, mixed precision, and gradient checkpointing
• Run scaling experiments, ablations, and architecture explorations grounded in empirical rigor
• Leverage insights from scaling behavior to make compute-efficient decisions across model size, data, and training strategy

Temporal Dynamics

• Develop methods to model time-series satellite data, capturing:
  • Seasonal patterns
  • Temporal dependencies
  • Long-term land-use changes
• Explore sequence modeling, memory mechanisms, and temporal tokenization strategies

Systems-Level Thinking

• Design ML systems as end-to-end pipelines (data ingestion → curation → training → evaluation → deployment → feedback)
• Make explicit trade-offs between model quality, latency, cost, and data freshness
• Work with platform teams to optimize:
  • Distributed training (FSDP, DeepSpeed)
  • GPU utilization
  • Data pipelines and experiment throughput
• Build reusable components and abstractions, not one-off models

Preferred Background

Experience

• 5–8 years of experience in ML research or applied research roles
• Experience in large-scale foundation model development (vision, multimodal, speech, or related domains)
• Experience training and/or fine-tuning billion-parameter models
• Experience working with sequence, video, or temporal data
• Exposure to geospatial foundation models such as:
  • Prithvi
  • Clay
  • Segment Anything Model (SAM) (nice to have)

Technical Skills

• Expert-level proficiency in PyTorch or JAX
• Strong experience with:
  • Distributed training (FSDP / DeepSpeed)
  • Large-scale datasets and training pipelines
• Familiarity with transformer architectures and training dynamics
• Bonus: CUDA / performance optimization experience

Additional Strengths

• Familiarity with efficient scaling techniques (e.g., Mixture of Experts) is a plus
• Strong experimental rigor and ability to design meaningful ablations
• Track record of publishing or contributing to state-of-the-art research in representation learning or generative modeling