Member of Technical Staff — Inference-TPU
Company websiteradixark.com (RadixArk, opens in new tab)
Base salary $200K to $400K
Employer-published annual base salary, USD. Advertised base range in Palo Alto, CA +1 more
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationPalo Alto, CA +1 more
- WorkplaceOn-site
- Status at last checkSource accepting applications when checked
- Last checked (1 h ago) · observed since
All places named
- Palo Alto, CA
- San Francisco, CA
About this job
About the Role
RadixArk is looking for a Member of Technical Staff — TPU Systems to build high-performance inference and training systems using JAX, XLA, and Pallas. You'll push model workloads to their limits on TPU hardware, working on SGLang-JAX and other critical infrastructure that enables efficient deployment of frontier models on Google's tensor processing units.
Requirements
3+ years experience building production ML systems utilizing JAX/Torch, XLA, or TPU-focused frameworks.
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or equivalent industry experience
Deep understanding of XLA internals preferred: HLO, MLIR, operator fusion, SPMD partitioning, and sharding strategies.
Strong performance tuning instincts across compiler and runtime layers
Experience with distributed inference systems (e.g. SGLang, vLLM) or training frameworks (e.g. Miles, Alpa, Pathways)
Proficiency in Python with demonstrated ability to write high-performance, production-quality code
Experience writing custom GPU/TPU/AI Accelerator kernels. Familiarity with Pallas for kernel development is strongly preferred.
Responsibilities
Build high-performance inference and training systems using JAX/XLA/Pallas, including SGLang-JAX
Push large-model workloads to the limits on the newest TPU hardwares
Optimize end-to-end latency and throughput for LLM serving on TPU infrastructure
Design and implement SPMD strategies for efficient distributed inference and training
Design and implement Pallas kernels for operations that require customized low level control for best performance
Profile and optimize XLA compilation pipelines and HLO graph transformations
Collaborate with kernel engineers and compiler teams to achieve performance wins across the stack
Contribute to open-source projects with TPU optimization guides, benchmarks, and architectural insights
About RadixArk
RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (30K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework). Founded by AI infrastructure veterans from xAI and NVIDIA, we're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training. Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.
Compensation
Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Equal Opportunity
RadixArk is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
Source: RadixArk on Ashby · View original posting (opens in new tab)
Pay in context
This role's base floor ($200,000) is above 70% of the 5,070 Software Engineering roles in California listed on this site; 196 others list the same floor. Their median floor is $176,800; the middle half runs from $149,374 to $204,000.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 8, 2026.
This listingMember of Technical Staff — Inference-TPU$200K to $400K
Member of Technical Staff — Inference-Kernel, Compiler & Communication$200K to $400K
Member of Technical Staff — Reliability-CI Infrastructure$200K to $400K
Member of Technical Staff — Inference-Multimodal & Diffusion$200K to $400K
Member of Technical Staff — Developer Technology$200K to $400K
Member of Technical Staff — Inference-Multi-Hardware$200K to $400K
Member of Technical Staff — Product$200K to $400K
Chart as a table
| Role | Published base |
|---|---|
| Member of Technical Staff — Inference-TPU (this listing) | $200K - $400K |
| Member of Technical Staff — Inference-Kernel, Compiler & Communication | $200K - $400K |
| Member of Technical Staff — Reliability-CI Infrastructure | $200K - $400K |
| Member of Technical Staff — Inference-Multimodal & Diffusion | $200K - $400K |
| Member of Technical Staff — Developer Technology | $200K - $400K |
| Member of Technical Staff — Inference-Multi-Hardware | $200K - $400K |
| Member of Technical Staff — Product | $200K - $400K |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2025-12-30
Where this pay comes from
Location as the employer wrote it: Palo Alto • San Francisco
Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
- Method
- Employer text, read with fixed rules and re-verified
- Checked
- Rule set
- us-base-v2
The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
