Member of Technical Staff, Cloud Orchestration

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• 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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Verification

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Salary

$200K-$400K From the posting source checked Jun 20, 2026

401(k) match

Reported not verified - source not recorded; source URL not recorded; timestamp not recorded

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Market context

U.S. role benchmark (BLS OEWS)

$61,842 U.S. median for this role

Projected growth (BLS Employment Projections)

+1.9% - Slower

385% above the BLS role benchmark for operations aggregate.

Posted salary is far from this role benchmark; treat it as low confidence.

Matched to SOC 11-1021 - Operations aggregate by role bucket.

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

Role

Role function

Operations From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff, Cloud Orchestration San Francisco, California, United States Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware-a position that took years to build. About the Role We're looking for an cloud orchestration engineer to build the operational backbone that keeps vLLM running reliably at massive scale. You'll design the systems for cluster management, deployment automation, and production monitoring that enable teams worldwide to serve AI models without friction. You'll ensure that vLLM deployments are observable, debuggable, and recoverable, turning operational complexity into infrastructure that just works. Skills and Qualifications Minimum qualifications: - Bachelor's degree or equivalent experience in computer science, engineering, or similar. - Strong experience with Kubernetes and container orchestration at scale. - Experience designing and implementing custom Kubernetes operators. - Proficiency in Python/Rust/Go and infrastructure-as-code tools (Terraform, Helm, etc). - Experience managing GPU clusters and debugging hardware issues. - Ability to work across cloud platforms (AWS, GCP, Azure) and on-premise infrastructure. Preferred qualifications: - Experience with ML-specific orchestration tools (Ray, Slurm). - Knowledge of GPU scheduling, multi-tenancy, and resource optimization. - Familiarity with vLLM deployment patterns and configuration. - Track record of improving operational reliability for ML systems. Bonus points if you have: - Experience deploying inference systems on large-scale GPU (1,000+) clusters. Logistics - Location: This role is based in San Francisco, California.

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