About Us/n 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. /n We’re building the infrastructure for the next generation of on-device AI, from silicon-level optimizations to distributed inference systems. /n We’re working on hard problems at the intersection of inference efficiency, model intelligence and autonomous research. /n The Role/n We’re looking for an ML Engineer to work at the frontier of on-device AI.

This role is for someone who lives at the intersection of systems engineering and machine learning, turning state-of-the-art research into hyper-optimized, production-ready infrastructure. /n You’ll have significant ownership over our entire inference stack and direct influence on the technical bets the company makes. /n What You’ll Work On/n /n

- /n Inference engine development: Building and scaling our custom inference engine, handling everything from weight loading and KV-cache management to efficient request scheduling /n
- /n Cross-platform silicon optimization: Writing and tuning custom kernels and leveraging hardware-specific instructions to squeeze maximum performance out of diverse architectures, including Apple Silicon, NVIDIA, AMD, Snapdragon, and other edge platforms /n
- /n Systems architecture: Developing robust, low-latency serving runtimes in C++ to manage model routing, continuous batching, and novel decoding strategies under strict thermal and memory constraints /n
- /n Performance profiling:



Identifying and eliminating bottlenecks across the entire stack, from memory bandwidth ceilings to kernel interleaving /n /n What We’re Looking For/n /n
- /n 3+ years of experience in ML engineering or systems programming (Rust, C/C++), with a strong track record of building performance-critical software /n
- /n Expertise in GPU programming and hardware optimization across various platforms (CUDA, ROCm, Metal, Triton, or similar) /n
- /n Solid understanding of modern LLM architectures, including parsing formats and implementing optimization techniques (quantization, speculative decoding, etc.) /n
- /n A strong sense of ownership and autonomy: the ability to take ambiguous architectural challenges and drive them from research translation directly into production-ready infrastructure /n
- /n Good communication: the ability to explain complex architectural decisions simply, give honest feedback and document systems cleanly /n
- /n Nice-to-haves: /n /n - /n Familiarity with ML compilers (torch.compile, custom operators) /n - /n Experience with low-precision inference (INT8/FP8/FP4) /n - /n Knowledge of Edge LLMOps /n /n /n What We Offer/n /n
- /n Equity and strong base salary /n
- /n Direct influence on technical direction: your ideas will shape the roadmap /n
- /n Work on genuinely hard problems that haven't been solved yet /n
- /n Small team, fast iteration, low bureaucracy /n /n Location/n The team is based in Melbourne and Berlin and works in-person from the office most days. We require solid written and spoken English, since the team collaborates across time zones.

📌 ML Engineer (Melbourne)
🏢 Base Compute
📍 Melbourne