ML Applied Scientist | ML Research Engineer

Description

Help us transform how machine learning and artificial intelligence systems are evaluated. Our team conducts research, builds tools, and develops systems that help improve, refine, and scale evaluation. We’re looking for technical staff members with strong backgrounds in applied AI/ML who are passionate about solving high-impact problems and advancing the state-of-the-art in the domain of evaluation.

This role will be multifunctional. You will collaborate closely with highly skilled machine learning researchers and engineers, socio-technical research scientists, software engineers, and domain experts to help develop and deliver groundbreaking advances to mission-critical evaluation programs and methods.

We believe the most exciting problems in machine learning research arise at the intersection with real-world use cases and that this is also where the most critical breakthroughs come from. In this role, you will identify and operationalize problems, prototype solutions, and deliver results.

As a ML Applied Scientist, you will

• Become intimately familiar with product use cases, requirements, and data in order to develop the insights required for solving hard evaluation problems

• Develop models and systems that reliably solve complex evaluation problems

• Develop and refine benchmarks that help feature engineers advance the state-of-the-art

• Research and develop methodological innovations that advance the state-of-the-art in AI evaluation

• Collaborate on an applied R&D team that is oriented around principles of continuous delivery, iterative development, and fault tolerance

Minimum Qualifications

• Substantive experience solving real-world problems with LLMs

• Experience with LLM benchmarking and evaluation

• Experience applying software engineering best practices to ML development

• Strong research fundamentals

• Substantive experience with one or more LLM training or post-training paradigm

• Experience with PyTorch and/or MLX

Preferred Qualifications

Substantive experience with ML/AI development and evaluation, ideally in domains like natural language processing, natural language understanding, search, code generation, or language generation

Extensive experience using LLMs to solve downstream ML/AI development problems, such as synthetic data generation, auto-labeling, and data curation

Expertise in LLM fine-tuning and/or pre-training, bonus points for RL experience

PyTorch and/or MLX expertise

Publications in relevant conferences (NeurIPS, ICML, ACL, etc.) are a plus but not mandatory

Minimum Education & Experience

6+ years of relevant industry experience with a Bachelor's degree; or 4+ years with a Master's degree; or a PhD 3+ years industry experience; or equivalent work experience.