ML Engineer

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 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.

You’ll have significant ownership over our entire inference stack and direct influence on the technical bets the company makes.

What You’ll Work On

• Inference engine development: Building and scaling our custom inference engine, handling everything from weight loading and KV-cache management to efficient request scheduling

• 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

• 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

• Performance profiling: Identifying and eliminating bottlenecks across the entire stack, from memory bandwidth ceilings to kernel interleaving

What We’re Looking For

• 3+ years of experience in ML engineering or systems programming (Rust, C/C++), with a strong track record of building performance-critical software

• Expertise in GPU programming and hardware optimization across various platforms (CUDA, ROCm, Metal, Triton, or similar)

• Solid understanding of modern LLM architectures, including parsing formats and implementing optimization techniques (quantization, speculative decoding, etc.)

• 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

• Good communication: the ability to explain complex architectural decisions simply, give honest feedback and document systems cleanly