AI Research Scientist 4/5 - Generative Models, Recommender Systems

Accountabilities:: AI and generative model research: Monitor the latest developments in large language models, generative AI, and deep learning research, identifying promising techniques and translating them into practical solutions for real-world business challenges. Model development and evaluation: Lead the end-to-end machine learning lifecycle, including research, experimentation, model training, performance evaluation, and optimization of generative AI systems. Text generation and summarization: Apply state-of-the-art techniques to improve generated content quality, with a particular focus on summarization and the development of reliable LLM-based evaluation systems. LLM evaluation and quality assurance: Design and implement AI-based evaluators, including LLM-as-a-judge approaches, to assess generated outputs against defined quality standards and improve the reliability of generative systems. Personalization and recommendation optimization: Combine generative content capabilities with recommendation system principles to optimize promotional assets for specific audiences, improving relevance, engagement, and overall user impact. Production integration and deployment: Collaborate with machine learning scientists and engineers to integrate research models into business applications, platforms, and production workflows. Technical strategy and cross-functional leadership: Serve as a subject matter expert, identify high-impact opportunities, define technical roadmaps, and communicate complex research findings to technical and non-technical stakeholders to support strategic decisions. Research community engagement: Contribute to the broader machine learning community through knowledge sharing, collaboration, and engagement with internal and external research networks. Requirements: Strong foundational knowledge of machine learning, deep learning, natural language processing, natural language understanding, and text generation , with the ability to apply advanced research techniques to practical challenges. Demonstrated experience tracking and interpreting scientific literature on LLMs and generative models, with the ability to identify relevant breakthroughs and translate them into effective business applications. Expertise in one or more LLM post-training approaches , supported by practical knowledge of model adaptation, optimization, and evaluation. Experience leading machine learning projects from research and experimentation through model training, evaluation, and integration into production environments. Strong analytical and problem-solving abilities, with a research-oriented mindset and the capacity to develop innovative solutions to complex technical challenges. Excellent communication and collaboration skills, including the ability to explain sophisticated machine learning concepts to diverse audiences and influence technical and business decisions. Demonstrated ability to work across multidisciplinary teams, identify opportunities for innovation, and establish technical direction for high-impact initiatives. Preferred qualifications: Research or practical experience in text summarization, generated-text quality assessment, or LLM-based evaluation methodologies, including LLM-as-a-judge techniques. Familiarity with computer vision and generative vision models, particularly diffusion models. Experience with recommendation systems, personalization algorithms, or other user-facing optimization systems. A track record of contributing to machine learning research communities, sharing technical knowledge, and collaborating with research and engineering professionals. Benefits: Competitive annual compensation: Salary range of $300,000–$537,000 , depending on location, professional background, skills, experience, and market conditions. Flexible compensation structure: Compensation is structured around annual salary and stock options, allowing eligible employees to choose their preferred salary-to-stock allocation each year. The position does not include bonuses. Comprehensive healthcare: Access to health insurance plans supporting employees' medical needs. Mental health support: Resources and programs designed to support mental well-being. Retirement savings: A 401(k) retirement plan with an employer match. Equity opportunities: Participation in a stock option program. Disability protection: Access to disability benefits and life and serious injury coverage. Health savings and spending accounts: Access to Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA). Family-forming benefits: Benefits designed to support employees and their families through family-forming journeys. Paid leave: Full-time salaried employees have access to flexible time off, while eligible full-time hourly employees accrue 35 days annually for vacation, holidays, and paid sick time. Remote work flexibility: A fully remote position based in the United States. Inclusive recruitment: A commitment to equal employment opportunities, diversity, and reasonable accommodations throughout the hiring process. Professional growth and innovation: Opportunities to contribute to advanced AI research, collaborate with experienced technical teams, and influence the development of next-generation machine learning solutions.