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Senior Platform Engineer (Compute, Data, AI, Security) – Biotechnology

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Lead platform engineering for biotech sequencing infrastructure, focusing on compute, data, AI, and security systems. Responsibilities include orchestrating lab devices, building cloud/data pipelines, designing orchestration/scheduling systems, and developing AI tools. Requires 5+ years of platform/infrastructure engineering experience with AWS, Python, and distributed systems expertise.

Key Highlights
5+ years of platform/infrastructure engineering experience required
Strong AWS (ECS, Batch, Step Functions) and Python (FastAPI/Django) expertise
Leadership in distributed systems, task queues (Celery/SQS), and LLM/agent systems
Key Responsibilities
Orchestrate lab devices (robots, sequencers, Linux servers) and data ingestion pipelines
Build cloud infrastructure and data pipelines for sequencing data storage/processing
Design orchestration/scheduling systems for bioinformatics workloads
Develop AI infrastructure and agentic systems for quality monitoring/logistics
Lead cross-functional projects from definition to production deployment
Mentor teammates and contribute to platform/backend engineering
Technical Skills Required
AWS Orchestration Python (Backend) Distributed Systems
Benefits & Perks
Base salary: $205,000 to $235,000 annually
Visa sponsorship available
Nice to Have
Curiosity about biotechnology/scientific domains
Experience with LLM/agent systems (tooling, evaluation, permissions, prompts)

Job Description

About the Role

Lead platform engineering for the compute, data, AI, and security infrastructure behind a biotechnology sequencing operation. You will combine hands-on engineering with technical leadership to build reliable systems that support lab operations and scientific data processing.

What You'll Do

Own services that orchestrate lab devices, including robots, sequencers, and on-premises Linux servers, as well as the data ingestion pipeline.

Build cloud infrastructure and data pipelines to store, process, and deliver large volumes of sequencing data.

Design reliable orchestration and scheduling systems for large-scale bioinformatics workloads using queues, workflows, and state machines.

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