Machine Learning Research Engineer

About Neural Magic

Based in Somerville, Massachusetts, Neural Magic is a series A startup backed by leading investors including Andreessen Horowitz, NEA, NEA, Pillar, VMware, Verizon Ventures, Comcast Ventures, and Amdocs. At Neural Magic we believe the future of AI is open and we are on a mission to bring the power of open-source LLMs and VLLM to every enterprise on the planet. Neural Magic accelerates AI for the enterprise and brings operational simplicity to GenAI deployments. As a leading developer and maintainer of the vLLM project and inventor of state-of-the-art techniques for model quantization and sparsification, Neural Magic provides a stable platform for enterprises to build, optimize and scale LLM deployments.

Our Mission

Neural Magic is on a mission to bring the power of open-source LLMs and vLLM to every enterprise on the planet.

Your Role

As an ML Research Engineer, you will be at the forefront of innovation, collaborating with our team to tackle the most pressing challenges in model performance and efficiency. Your research will directly impact the development of our cutting-edge software platform, helping to shape the future of AI deployment and utilization.

Join us in shaping the future of AI!

Responsibilities

• Research & Innovate: Spearhead the development of cutting-edge research projects focused on improving LLM performance, efficiency, and scalability.

• Prototype & Experiment: Design and implement prototypes to test novel algorithms and techniques, pushing the boundaries of model optimization and inference serving.

• Analyze & Evaluate: Conduct rigorous experiments and analysis to measure the impact of your research, document findings, and share insights with the team.

• Collaborate & Communicate: Work closely with product and engineering teams to translate research prototypes into production-ready features, ensuring seamless integration with our platform.

• Contribute & Share: Stay abreast of the latest advancements in the field, contribute to open-source projects, and share your findings through publications and presentations.