Founding Platform Engineer, Data & ML Systems

About the role

We are hiring a Founding Platform Engineer to build the infrastructure backbone behind our training, evaluation, and inference stack. The role involves building the systems that make biological data usable for model development at speed and at scale, including ingestion, indexing, search, retrieval, dataset interfaces, reproducibility, validation, orchestration, observability, and distributed performance.

Responsibilities

• Build and maintain data infrastructure for model training, evaluation, and inference
• Design and scale high-performance inference serving systems for biological foundation models
• Standardize dataset interfaces so biological data is consistent, discoverable, and easy to use across the team
• Ingestion and processing pipelines for public, proprietary, and customer datasets
• Indexing, search, and retrieval systems that make large datasets queryable and useful in practice
• Establish safeguards and validation systems so datasets are reproducible, versioned, and trustworthy once standardized
• Improve throughput, latency, and reliability of distributed data loading and ML pipelines
• Profile and eliminate performance bottlenecks across GPU, networking, and storage layers
• Automate fault detection and recovery for serving and training systems
• Build internal tools for dataset inspection, debugging, quality control, and operational visibility
• Partner closely with ML engineers and researchers to ensure the platform fits real workflows
• Define how we handle permissions, privacy, compliance boundaries, and operational rigor for sensitive biological and customer data

Requirements

• Deep experience in backend, infrastructure, distributed systems, or data platform engineering
• Scalable data pipelines or stateful distributed systems in production experience
• Experience building or operating large-scale inference or training systems
• Understanding of GPU execution constraints, memory trade-offs, and data-loading bottlenecks around training workloads
• Experience with dataset infrastructure for large-scale ML systems, training pipelines, or inference-adjacent systems
• Hands-on experience with data indexing, search, or retrieval infrastructure, and understanding how to make large datasets discoverable, queryable, and usable in practice
• Reasoning about system-level trade-offs between latency, throughput, and cost
• Experience working with privacy-sensitive or compliance-sensitive data systems
• Building internal developer tools for ML or data teams
• Strong instincts around reliability, reproducibility, and operational simplicity
• Comfortable with cloud infrastructure, containers, Kubernetes, Infrastructure-as-Code, CI/CD, and observability
• Produce maintainable code and make pragmatic architecture decisions under time pressure
• Thriving in a small team where ownership is broad and priorities can change quickly

Qualifications

• Experience with biological, genomic, or scientific data formats and workflows
• Contributions to open-source data or ML infrastructure projects
• Experience building streaming or real-time data systems
• Background in database internals, storage engines, or query optimization
• Experience designing systems that serve both batch training and low-latency inference workloads

Skills

• Experience with cloud infrastructure, containers, Kubernetes, Infrastructure-as-Code, CI/CD, and observability
• Strong instincts around reliability, reproducibility, and operational simplicity
• Comfortable with cloud infrastructure, containers, Kubernetes, Infrastructure-as-Code, CI/CD, and observability
• Produce maintainable code and make pragmatic architecture decisions under time pressure
• Thriving in a small team where ownership is broad and priorities can change quickly

Benefits

The quality of our data and ML platform directly determines research speed, model quality, and customer trust. The right person will make the entire company faster and will shape the foundation we build on for years.

Pay

Competitive salary commensurate with experience.

Schedule

Full-time, remote position.