Applied Research Engineer

Role overview Turn research ideas and state-of-the-art models into dependable systems that can deliver value in real products. This applied role bridges research and engineering: you’ll reproduce and optimize models, build scalable services and demos, and adapt deployments to practical constraints such as latency, throughput, and cost.

Responsibilities - Convert research concepts into scalable production services and demonstrations. - Work with researchers to reproduce models and improve their inference performance. - Optimize latency, throughput, and operating cost for deployment needs. - Instrument experiments and maintain reproducible model deployment pipelines. - Coordinate with product colleagues to prioritize research based on user impact.

Requirements - At least three years of machine learning engineering or applied research experience, with strong Python skills. - Hands-on knowledge of model optimization, quantization, distillation, and inference engineering. - Experience deploying machine learning models on cloud services and using container orchestration such as Kubernetes. - Familiarity with model monitoring, A/B testing, and staged rollout methods.

Benefits and work setup Full-time role with global remote or hybrid options. The package includes salary and equity, cloud and GPU credits for prototyping, learning and publication support, and career development across research and product tracks.