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Senior Team Lead - AI Inference

Weka - U.S. Remote

Posted Mar 26, 2026

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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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
Senior 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

Company stage
Growth-stage 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

Senior Team Lead - AI Inference U.S. Remote We are seeking a Senior Team Lead to lead our AI Inference team. In this role, you will bridge the gap between complex research and production-grade engineering, while cultivating a high-performing team culture. You will lead and grow a squad of 3 developers, balancing hands-on technical contribution with strong people leadership - setting direction, unblocking your team, and driving execution on high-performance systems that optimize Large Language Model (LLM) serving. The ideal candidate combines deep technical expertise in inference and scale with the leadership maturity to mentor, motivate, and develop engineers in the evolving ecosyste m of serving frameworks like vLLM and LMCache. What You'll Work On - Lead & Own: Take end-to-end ownership of AMG's core inference infrastructure - from the NVMe Token Warehouse and GDS data paths to the vLLM/LMCache serving stack - driving technical decisions and delivery outcomes. - Technical Direction: Guide a team of engineers through design, implementation, and delivery of high-throughput, low-latency LLM inference systems, setting high standards for code quality, architecture, and reliability. - Build at Scale: Stay hands-on across the AMG stack (Python, C++, CUDA, vLLM, NIXL/Dynamo, Kubernetes), contributing directly to production systems while providing technical leadership to the team. - Solve Hard Problems: Tackle the real frontier challenges of inference engineering - disaggregated prefill/decode, persistent off-HBM KV caching, RDMA-based transport, and multi-tier GPU memory hierarchies - that define what's possible at scale. - Grow People & Teams: Mentor and coach engineers through regular 1:1s,

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