What You Will Do

We are looking for a Machine Learning Engineer to help build cutting edge ML infrastructure for building and serving LLM’s  at Moveworks. This role will be critical in building, optimizing and scaling end-to-end machine learning systems. The ML infra team covers a variety of responsibilities including distributed training and inference pipeline for large language models(LLM), model evaluation and monitoring framework, LLM latency optimization, etc. These frameworks serve as a strong foundation for our hundreds of ML and NLP models in production serving hundreds of millions of enterprise employees. We are solving many challenges on scalability of services as well as optimization of core algorithms. 

Your work will impact the way our customers experience AI. Put another way, this role is absolutely critical to the long term scalability of our core AI product and ultimately the company. You will be responsible for building and productionizing ML infrastructure that runs state of the art models. If you are looking for a high-impact, fast-moving role to take your work to the next level, we should have a conversation.  

• Design, build and optimize scalable machine learning infrastructure to support training, evaluation, and deployment of large language models.
• Build abstractions to automate various steps in different ML workflows
• Collaborate with cross functional teams of engineers, data analytics, machine learning experts, and product to build new features.

What You Bring To The Table

• 5+ years of industry experience in Machine Learning, Infrastructure or related fields
• Experience with deep learning frameworks such as Pytorch or Huggingface or LLM serving frameworks such as vLLM or TensorRT-LLM.
• Experience with building and scaling end-to-end machine learning systems
• Experience building scalable micro services and ETL pipelines
• Expertise in Python and experience with performant language such as C++ or GoLang
• Bachelor's in Computer Science, Computer Engineering, Mathematics, or equivalent field.
• A love of research publications in the machine learning and software engineering communities
• Effective communicator with experience collaborating cross-functionally with other teams

Nice To Have

• Experience with ML Inference optimization using TensorRT. 
• Experience with distributed training  frameworks such as Deepspeed.
• Experience in managing and scaling GPU Inference services via Kubernetes