- 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.
- Develop and implement novel post-training techniques to optimize machine learning models
- Benchmark and evaluate post-training techniques on a variety of datasets and model architectures; Analyze experimental results to gain insights into model behavior and identify areas for improvement.
- Implement post-training models and algorithms; Optimize model performance and scalability for deployment in production environments.
- Collaborate with research scientists and engineers to identify ares for post-training optimization
- 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, feature engineering, and model evaluation is beneficial.
- Proficiency in programming languages such as Python, C++, or Java
- Familiarity with ML deployment and orchestration
- Strong problem solving, analytical, communication, and collaboration skills with the ability to analyze complex datasets and derive actionable insights.
- You enjoy building things from the ground up in a fast-paced, collaborative environment.
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Member of Technical Staff: Research Engineer, Post-Training - San Francisco, United States - essential AI
Description
Job Description
Job DescriptionEssential 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 RoleThe Research Engineer, Post-Training will be responsible for developing and implementing techniques to optimize and fine-tune models after the initial training process, with the goal of improving performance, robustness, and efficiency. You will work cross-functionally to identify areas for post-training optimization and measure the impact on model performance. You will also be responsible for benchmarking and evaluating post-training techniques on a variety of datasets and model architectures.
What you'll be working onWe 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.
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