Essential AI’s mission is to deepen the partnership between humans and computers, unlocking collaborative capabilities that far exceed what could be achieved today. We believe that building delightful end-user experiences requires innovating across the stack - from the UX all the way down to models that achieve the best user value per FLOP.

We believe that a small, focused team of motivated individuals can create outsized breakthroughs. We are building a world-class multi-disciplinary team who are excited to solve hard real-world AI problems. We are well-capitalized and supported by March Capital and Thrive Capital, with participation from AMD, Franklin Venture Partners, Google, KB Investment, NVIDIA.

The Role

The Machine Learning Infrastructure Engineer will be responsible for architecting, building the compute infra that powers training and serving of our models. This requires a full understanding of the complete backend stack → from frameworks to compilers to runtimes to kernels. In addition, the role requires familiarity with tools and services common in cloud based infra like Kubernetes and Dockers.

What you’ll be working on

• Design, build, and maintain scalable machine learning infrastructure to support our model training, inference and applications

• Design and implement scalable machine learning and distributed systems that enable training and scaling of LLMs. Work on parallelism methods improve training of in a fast and reliable way

• Develop tools and frameworks to automate and streamline ML experimentation and management

• Collaborate with other researchers and product engineers to bring magical product experiences through large language models

• Working on lower levels of the stack to build high performing and optimal training and serving infrastructure including researching new techniques and writing custom kernels as needed to achieve improvements

• Be willing to optimize performance and efficiency across different accelerators

What we are looking for

• A strong understanding of architectures of new AI accelerators like TPU, IPU, HPU etc and their tradeoffs.

• Knowledge of parallel computing concepts and distributed systems.

• Prior experience in performance tuning of training and/or inference LLM workloads. Experience with MLPerf or internal production workloads will be valued.

• 6+ years of relevant industry experience in leading the design of large-scale & production ML infra systems.

• Experience with training and building large language models using frameworks such as Megatron, DeepSpeed, etc and deployment frameworks like vLLM, TGI, TensorRT-LLM etc

• Comfortable with working under-the-hood with kernel languages like OAI Triton, Pallas and compilers like XLA

• Experience with INT8/FP8 training and inference, quantization and/or distillation

• Knowledge of container technologies like Docker and Kubernetes and cloud platforms like AWS, GCP, etc.

• Intermediate fluency with network fundamentals like VPC, Subnets, Routing Tables, Firewalls etc

We encourage you to apply for this position even if you don’t check all of the above requirements but want to spend time pushing on these techniques.

We are based in-person in SF and fully onsite 5 days a week. We offer relocation assistance to new employees.