Member of Technical Staff - System Engineering

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Member of Technical Staff - System Engineering South San Francisco, California, United States, Toronto (hybrid) About Phylo Phylo is an applied research lab building agentic intelligence to accelerate discovery for every biomedical scientist. We believe AI agents will fundamentally transform how biomedical research is done, enabling faster and more systematic scientific progress. Our growing team brings together world class researchers and engineers across AI and biology. Backed by a $13.5M seed round led by a16z, Menlo Ventures and Anthropic, and advised by Nobel Prize laureate and pioneering biologists, Phylo is building the next generation of AI systems for the life sciences. About the role We're looking for an engineer to build and operate the core systems that power Phylo's agentic AI platform in production. You'll design and implement the distributed systems, compute environments, and service architecture that allow our AI agents to run reliably at scale - across cloud-native deployments and enterprise environments. We are an AI-native engineering team: we use coding agents heavily, move fast, and hold a very high bar for engineering excellence. What You'll Work On - Design and build production systems that orchestrate agent execution and power AI-driven scientific workloads. - Build and operate scalable, reliable infrastructure across cloud, hybrid, and on-prem enterprise environments. - Develop systems for sandboxed execution, secure task isolation, and controlled compute environments. Design and implement security, access control, and compliance foundations suitable for enterprise deployments. - Partner closely with ML and science teams to translate computational workflows into robust, production-grade distributed

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