ROLE
– Designed and built training and benchmarking datasets across two satellite sensors and eight change detection tasks, establishing consistent annotation standards and quality control processes.
– Built and optimized scalable data pipelines for scene ingestion, preprocessing, chip generation, and dataset export, enabling efficient handling of multi-sensor satellite data.
– Developed an interactive dashboard to evaluate and demonstrate change detection results for competition judges, providing clear visualizations of detected changes, confidence scores, and model performance metrics.
– Optimized model inference performance by improving latency, throughput, and memory utilization to meet competition runtime and resource constraints.
– Develop a change captioning system that generates natural language descriptions of changes by leveraging two time-step satellite images and a change detection mask.
– Build the agentic interface layer for interacting with change detection outputs, enabling users to query, interpret, and reason about detected changes through conversational or programmatic interfaces.
– Evaluate trade-offs between model complexity, accuracy, and inference performance.
– Benchmark and validate models against ground truth datasets and competition evaluation metrics.
– Continuously iterate on model architectures and training strategies based on performance feedback.
– Write clean, efficient, and maintainable Python code for data processing and model development.
– Contribute to shared utilities and machine learning libraries used across the competition team.
– Collaborate closely with cross-functional teams by: Providing feedback on dataset quality and annotation issues, Partnering with the ML team on model evaluation and performance improvements, Coordinating with data engineers to optimize data pipelines.
– Document datasets, models, pipelines, and technical processes comprehensively to ensure reproducibility and provide clear technical insights for team members and competition judges.

REQUIREMENTS
– Strong programming skills in Python with hands-on experience using libraries and frameworks such as PyTorch, TensorFlow, NumPy, Pandas, or equivalent.
– Practical experience or strong academic coursework in data pipelines, data processing, or data engineering workflows.
– Basic understanding of the end-to-end machine learning lifecycle, including model training, evaluation, and deployment at scale.
– Ability to work independently on well-defined workstreams, take ownership of deliverables, and proactively seek guidance from senior scientists when needed.
– Strong attention to detail, with an understanding that high-quality data is critical to model performance.
– Eagerness to learn quickly, adapt to new challenges, and thrive in a fast-paced, competition-driven environment.
– Currently pursuing or recently completed a Bachelor’s degree in Computer Science, Machine Learning, Data Science, Engineering, Physics, or a related field.

BENEFITS
– Health insurance coverage
– Unlimited leaves & flexible working hours
– Role-based remote work and work-from-home benefit
– Relocation assistance
– Professional Mental Wellness services
– Employee Stock Options for all hires