At a glanceSummarised by Seekless from the posting.
Must have5
PhD in ML or equivalent industry research experience
Deep understanding of LLM architectures and the principles of AI inference
Expertise in a relevant topic, such as speculative decoding, quantization theory, model distillation, reinforcement learning
A track record of producing results that people build on: research papers, open-source projects or blog posts that prove out novel ideas
Good communication: the ability to explain complex ideas simply, give honest feedback and document findings in a reproducible way
Nice to have2
Familiarity with GPU and accelerator architectures and kernel optimization (CUDA, ROCm, Metal, Triton, etc.)
Experience deploying models under on-device constraints (memory bandwidth, latency budgets, and thermal and power ceilings)
Skills
CUDA
ROCm
Metal
Triton
Benefits
Equity
Strong base salary
About Us
Base Compute is an AI inference lab. Our mission is to bring AGI on device. We believe in a world where everyone has access to intelligence: fast, private and always available on your device.
We’re building the infrastructure for the next generation of on-device AI, from silicon-level optimizations to distributed inference systems.
We’re working on hard problems at the intersection of inference efficiency, model intelligence and autonomous research.
The Role
We’re looking for a Founding ML Researcher to work at the frontier of on-device AI. This role is for someone who identifies problems and potentials, designs and executes experiments and derives insights that translate into real-world performance.
You’ll have significant ownership over our research agenda and direct influence on the technical bets the company makes.
What You’ll Work On
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Inference research: Identifying and validating new approaches to on-device efficiency, including speculative decoding variants, novel quantization schemes and entirely new techniques yet to be discovered
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Model routing research: Building the intelligence that decides how requests are served between on-device vs. frontier API models
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Autoresearch pipelines: Designing systems that can autonomously explore, hypothesize and evaluate research ideas that accelerate our R&D loop
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Evaluations and benchmarks: Developing rigorous evals that measure performance in the real world, outside of clean academic settings
What We’re Looking For
•
PhD in ML or equivalent industry research experience
•
Deep understanding of LLM architectures and the principles of AI inference
•
Expertise in a relevant topic, such as speculative decoding, quantization theory, model distillation, reinforcement learning
•
A track record of producing results that people build on: research papers, open-source projects or blog posts that prove out novel ideas
•
Good communication: the ability to explain complex ideas simply, give honest feedback and document findings in a reproducible way
•
Nice-to-haves:
Nice to have
•
Familiarity with GPU and accelerator architectures and kernel optimization (CUDA, ROCm, Metal, Triton, etc.)
•
Experience deploying models under on-device constraints (memory bandwidth, latency budgets, and thermal and power ceilings)
What We Offer
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Founding team equity and strong base salary
•
Direct influence on technical direction: your ideas will shape the roadmap
•
Work on genuinely hard problems that haven’t been solved yet
•
Small team, fast iteration, low bureaucracy
Location
The team is based in Melbourne and Berlin and works in-person from the office most days. We require strong written and spoken English, since the team collaborates across time zones.