Member of Technical Staff - Edge Inference Engineer

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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Verification

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

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

Role

Role function

Engineering Verified - from the job posting source checked Jun 20, 2026

Seniority

Staff Plus Verified - from the job posting source checked Jun 20, 2026

Work mode

Remote Inferred - source not recordedchecked Jun 20, 2026

In-office days

0 days Inferred - source not recordedchecked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered Verified - from the job posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff - Edge Inference Engineer San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Our Edge Inference team compiles Liquid Foundation Models into optimized machine code that runs on resource-constrained devices: phones, laptops, Raspberry Pis, and watches. We are core contributors to llama.cpp and build the infrastructure that makes efficient on-device AI possible. You will work directly with the technical lead on problems that require deep understanding of both ML architectures and hardware constraints. This is high-ownership work where your code ships to production and directly impacts model performance on real devices. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: - Works autonomously: Given a target device and performance goal, you figure out how to get there without hand-holding. You diagnose bottlenecks, prototype solutions, and iterate until you hit the target. - Thinks at the hardware level: You understand cache hierarchies, memory access patterns, and instruction-level optimization. You can reason about why code is slow before reaching for a profiler. - Bridges ML and systems: You understand how neural networks work mathematically (matrix operations,

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