Software Engineer (Data Pipelines & Interface)

Metamorphic•Palo Alto, CA

About The Position

Metamorphic is developing new approaches to intelligence by combining machine learning with large-scale experimental neuroscience, informed by the principles that make the brain efficient, flexible, and robust. We are building foundation models trained on rich, continuous neural data — a high-resolution model of the brain at a scale never before possible. Our founding team spans machine learning, neuroscience, and neurotechnology, with prior work including the MICrONS project, Neuropixels, and the Enigma project, as well as foundational scientific contributions in learning, neural computation, and generative modeling. Our work sits at the frontier of AI research, and we believe the highest-impact discoveries will come from researchers and engineers working as a single, tightly collaborative team. The name Metamorphic reflects our belief that the next advances in intelligence will come from a change in form, beyond scale — from artificial to natural intelligence. About the Role We are hiring Software Engineers to build the data platform that powers our research and model-development efforts. You will help design and maintain the systems that move data from acquisition through transformation and storage into reliable downstream interfaces for analytics, research workflows, model training, and long-term archival use. This includes database design and administration, ETL/ELT pipelines, ingestion services, data modeling, schema evolution, observability, and performance optimization across both SQL and NoSQL systems. This role sits at the intersection of software engineering, data engineering, and scientific infrastructure. You will work closely with researchers, ML engineers, and infrastructure teams to ensure that our data systems are robust enough for production use, flexible enough for evolving research needs, and maintainable enough to serve as a foundation for years of scientific and engineering work. You will have substantial autonomy in shaping how our long-lived scientific data platform is represented, administered, and evolved as the company scales.

Requirements

Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Computational Neuroscience, or a related field.
Strong software engineering skills.
Strong working proficiency in Python and database query languages.
Experience designing, building, and maintaining production object stores and ETL/ELT pipelines.
Deep familiarity with relational and NoSQL database systems, including schema design, indexing, query optimization, and administration.
Experience designing durable data models and schemas for complex, evolving datasets.
Experience maintaining data platforms in production environments, including tools for monitoring, observability, troubleshooting, and incident response.
Experience building high-throughput ingestion systems and optimizing data movement across storage, compute, and network boundaries.
Familiarity with workflow orchestration tools (e.g. Airflow, Dagster, Prefect).
Experience collaborating closely with various teams simultaneously (e.g. research, ML, scientific, etc.) to translate ambiguous requirements into robust, consensus-driven data pipeline specs.

Nice To Haves

Experience serving as a database administrator (DBA) or having substantial DBA-style ownership of production data systems.
Experience with scientific, biomedical, behavioral, or neural datasets, especially where data provenance and long-term reuse matter.
Experience with performance-critical compiled or systems languages (e.g. Rust, Zig, C++).
Experience with containerization, and scaling container orchestration (e.g. via Docker, Kubernetes).
Experience with provisioning and managing distributed GPU compute infrastructure (e.g. via Ray, Dstack, Skypilot).
Proficiency with MLOps platforms for experiment tracking and reproducibility (e.g. MLflow, W&B).
Background in scientific computing, computational neuroscience, life sciences, or ML-adjacent research environments.

Responsibilities

Design and maintain systems that move data from acquisition through transformation and storage into reliable downstream interfaces for analytics, research workflows, model training, and long-term archival use.
Database design and administration.
Develop and manage ETL/ELT pipelines.
Build ingestion services.
Data modeling and schema evolution.
Implement observability and performance optimization across SQL and NoSQL systems.
Ensure data systems are robust, flexible, and maintainable.
Shape the representation, administration, and evolution of the scientific data platform.
Collaborate closely with researchers, ML engineers, and infrastructure teams.
Translate ambiguous requirements into robust, consensus-driven data pipeline specifications.