Research Engineer - Inference

• High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy.
• Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you.
• AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations.
• Excellence everywhere: Everything we do should match the quality of our AI models.
• Global team: We prioritize your talent, not your location.
• Deploying state-of-the-art models to production and owning the path from research checkpoint to serving infrastructure.
• Optimizing inference performance across the stack, including latency, throughput, and cost, using techniques such as quantization, distillation, KV-cache optimization, batching strategies, and custom kernels.
• Building and tuning high-performance serving systems for real-time, streaming workloads where every millisecond matters.
• Creating tooling and infrastructure that lets researchers ship new models to production quickly, safely, and with confidence in their performance characteristics.
• Experience deploying and serving ML models in production, ideally for latency-sensitive or real-time applications.
• Strong engineering skills in GPU programming and inference optimization (e.g., CUDA, Triton, TensorRT, or serving frameworks such as vLLM or SGLang).
• The capacity to autonomously profile, diagnose, and eliminate bottlenecks across the serving stack, from model architecture to kernels to orchestration, and to build the tooling to measure it.
• This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw.
• We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

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