Machine Learning Scientist 4/5 - Generative Models

Design rigorous experiments to test hypotheses, from setup through statistical validation — e.g., comparing two summary-generation algorithms with limited sample sizes, accounting for experimental limitations, and drawing sound conclusions. Translate complex technical concepts for both technical and non-technical stakeholders to inform strategic decisions. Lead end-to-end ML development: research, model training, and evaluation. Partner with ML scientists and engineers to integrate models into business applications and platforms. Act as a subject matter expert, working cross-functionally to identify high-impact opportunities and define and execute roadmaps. Track advancements in the field and apply them to improve the promotional writing experience for members. Track record of designing, running, and analyzing rigorous experiments — from hypothesis formation through statistical validation of results. Strong foundation in ML and Deep Learning, especially in Natural Language Processing / Understanding and Generation. Research experience in summarization or generated-text evaluation is a big plus; background in vision models such as diffusion models is a plus. Expertise in one or more LLM post-training approaches. Strong programming skills in Python, with experience in common ML/DL frameworks such as PyTorch or TensorFlow. PhD (preferred) or MSc in ML, Computer Science, or a related field. Proven track record of bringing clarity and leading ambitious roadmaps to solve complex problems. Exceptional communication and collaboration skills, and passionate about positively contributing to the team and company culture. Comfortable with ambiguity, able to take ownership, and able to thrive with minimal oversight or process.