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 Research Scientist, Post-Training will lead our post-training research efforts to improve the efficiency, performance and robustness of our models. You will work cross-functionally to develop novel techniques for fine-tuning, adapting, and updating models to drive business value for our customers and enhance our products.

What you’ll be working on

• You will lead or be a core contributor to our research bets that advance the the real-world capabilities of our models.

• You will collaborate closely with our product teams to close the loop between research and product, identify capability gaps and evaluate progress.

• Lead research initiatives focused on post-training models for enterprises; Design and execute research experiments to enhance the post-training capabilities of our models

• Develop new algorithms and methods for efficient and effective model fine-tuning and adaptation

• Construct synthetic datasets and evaluations for enterprise use-cases.

• Analyze results and draw insights to guide future research projects

• Collaborate with with cross-functional teams to integrate post-training innovations into production systems

• Stay abreast of the latest advancements in post-training techniques (RLHF, RLAIF, DPO etc.) incorporating relevant findings into research projects and product development efforts

What we are looking for

• Research experience with a focus on post-training and optimizing large language models using frameworks such as Megatron, DeepSpeed, MaxText, etc.

• You have strong ML fundamentals and first principles thinking that guides your approach to research.

• You have experience of coming up with new methods or improving existing techniques in ML or related fields

• Experience with data engineering, in particular, optimization of data pipelines.

• Proficiency in programming languages commonly used in machine learning research such as Python

• Strong problem solving, analytical, communication, and collaboration skills.

• You enjoy building things from the ground up in a fast-paced, collaborative environment.

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. We offer relocation assistance to new employees.