About Earth Species Project
Earth Species Project (ESP) is a non-profit using AI to decode animal communication to find new ways of listening to and learning from the rest of nature. Our ultimate goal is to drive a renewed relationship with the rest of nature that allows the diversity of life to thrive.
ESP partners with biologists and machine learning researchers at leading universities and institutions around the world, and we are honored to be supported by many forward-looking philanthropists and groups, including Reid Hoffman, Waverley Street Foundation, the Paul G Allen Family Foundation and the National Geographic Society.
About the role
We're looking for a Senior Infrastructure Engineer (Backend/Data Performance) to help us build the foundational pipelines that power Earth Species Project's mission to understand animal communication with advanced AI. You’ll design and optimize scalable systems that let our researchers experiment faster, with production-quality reliability.
Your work will focus on data infrastructure and backend performance, creating pipelines and storage layers that can handle diverse species data at scale. You’ll collaborate closely with researchers, engineers, and external partners to make complex AI workflows simple, efficient, and reliable.
In this role you will
Design and optimize high-performance data pipelines for distributed training and storage (using tools like Arrow, DuckDB, LanceDB, BigQuery, vector databases).
Focus on low-level optimizations (latency, throughput, reliability, GPU usage).
Build monitoring and visualization tools for tracking data quality, pipeline performance, and experiments.
Optimize distributed AI workloads for reliability, latency, and efficiency.
Scope and supervise projects so that interns, PhD students, and post-docs can contribute and collaborate effectively.
Support recruiting efforts and help shape the growth of the infrastructure team.
Your background looks something like
5+ years of backend or infrastructure engineering experience
Strong Python programming skills (bonus points for lower-level languages)
Experience with distributed systems and cloud platforms (AWS, GCP, Azure)
Hands-on experience with containerization (Docker, Kubernetes) and infrastructure as code (Terraform)
Experience building or supporting ML/AI infrastructure in production
Experience with high-performance data tools (DuckDB, Apache Spark, Delta Lake)
GPU orchestration and large-scale model training experienceFamiliarity with ML platforms (SageMaker, Vertex AI) and frameworks (PyTorch, JAX)
Experience mentoring junior engineers, interns, or researchers and breaking down complex projects into manageable tasks
Experience participating in technical hiring processes and evaluating candidates
It would be even better if you
Have deep knowledge of training architectures, CUDA programming, or TPU optimization
Have Full-stack development experience with frameworks like
React for building web applications
Experience managing HPC infrastructure with tools like Slurm or Kubernetes clusters
Background in monitoring stacks (Prometheus, Grafana) for ML pipeline observability
About the hiring process
Initial interview: 30-minute discussion to align on experience and expectations
Technical screening: Two interviews and a take home exercise covering coding and system design
Panel interview: Assess team alignment
Final interview: Conversation with our Chief Scientist
ESP is committed to equal employment opportunities regardless of race, color, religion, gender, gender identity or expression, pregnancy, sexual orientation, marital status, ancestry, national origin, genetics, disability, age, veteran status, and criminal history, consistent with legal requirements. We encourage folks of all backgrounds and perspectives to apply.
If you require any accommodations, please email us at [email protected], and we’ll work with you to meet your accessibility needs.
