Member of Technical Staff - Distributed Training 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 From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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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

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Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff - Distributed Training 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 Training Infrastructure team is building the distributed systems that power our next-generation Liquid Foundation Models. As we scale, we need to design, implement, and optimize the infrastructure that enables large-scale training. This is a high-ownership training systems role focused on runtime/performance/reliability (not a general platform/SRE role). You'll work on a small team with fast feedback loops, building critical systems from the ground up rather than inheriting mature infrastructure. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: - Loves distributed systems complexity: Our team builds systems that keeps long training runs stable, debugs training failures across GPU clusters, and improves performance. - Wants to build: We need builders who find satisfaction in robust, fast, reliable infrastructure. - Thrives in ambiguity: Our systems support model architectures that are still evolving. We make decisions with incomplete information and iterate quickly. - Aligns with team priorities and delivers: Our best engineers align with team priorities while pushing back with data when they see

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