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

<h3>Accountabilities:</h3><li>
<strong>AI and generative model research:</strong>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.</li><li>
<strong>Model development and evaluation:</strong>Lead the end-to-end machine learning lifecycle, including research, experimentation, model training, performance evaluation, and optimization of generative AI systems.</li><li>
<strong>Text generation and summarization:</strong>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.</li><li>
<strong>LLM evaluation and quality assurance:</strong>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.</li><li>
<strong>Personalization and recommendation optimization:</strong>Combine generative content capabilities with recommendation system principles to optimize promotional assets for specific audiences, improving relevance, engagement, and overall user impact.</li><li>
<strong>Production integration and deployment:</strong>Collaborate with machine learning scientists and engineers to integrate research models into business applications, platforms, and production workflows.</li><li>
<strong>Technical strategy and cross-functional leadership:</strong>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.</li><li>
<strong>Research community engagement:</strong>Contribute to the broader machine learning community through knowledge sharing, collaboration, and engagement with internal and external research networks.</li><h2>Requirements:</h2><li>Strong foundational knowledge of<strong>machine learning, deep learning, natural language processing, natural language understanding, and text generation</strong>, with the ability to apply advanced research techniques to practical challenges.</li><li>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.</li><li>Expertise in one or more<strong>LLM post-training approaches</strong>, supported by practical knowledge of model adaptation, optimization, and evaluation.</li><li>Experience leading machine learning projects from research and experimentation through model training, evaluation, and integration into production environments.</li><li>Strong analytical and problem-solving abilities, with a research-oriented mindset and the capacity to develop innovative solutions to complex technical challenges.</li><li>Excellent communication and collaboration skills, including the ability to explain sophisticated machine learning concepts to diverse audiences and influence technical and business decisions.</li><li>Demonstrated ability to work across multidisciplinary teams, identify opportunities for innovation, and establish technical direction for high-impact initiatives.</li><h2>Preferred qualifications:</h2><li>Research or practical experience in text summarization, generated-text quality assessment, or LLM-based evaluation methodologies, including LLM-as-a-judge techniques.</li><li>Familiarity with computer vision and generative vision models, particularly diffusion models.</li><li>Experience with recommendation systems, personalization algorithms, or other user-facing optimization systems.</li><li>A track record of contributing to machine learning research communities, sharing technical knowledge, and collaborating with research and engineering professionals.</li><h2>Benefits:</h2><li>
<strong>Competitive annual compensation:</strong>Salary range of<strong>$300,000–$537,000</strong>, depending on location, professional background, skills, experience, and market conditions.</li><li>
<strong>Flexible compensation structure:</strong>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.</li><li>
<strong>Comprehensive healthcare:</strong>Access to health insurance plans supporting employees' medical needs.</li><li>
<strong>Mental health support:</strong>Resources and programs designed to support mental well-being.</li><li>
<strong>Retirement savings:</strong>A 401(k) retirement plan with an employer match.</li><li>
<strong>Equity opportunities:</strong>Participation in a stock option program.</li><li>
<strong>Disability protection:</strong>Access to disability benefits and life and serious injury coverage.</li><li>
<strong>Health savings and spending accounts:</strong>Access to Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA).</li><li>
<strong>Family-forming benefits:</strong>Benefits designed to support employees and their families through family-forming journeys.</li><li>
<strong>Paid leave:</strong>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.</li><li>
<strong>Remote work flexibility:</strong>A fully remote position based in the United States.</li><li>
<strong>Inclusive recruitment:</strong>A commitment to equal employment opportunities, diversity, and reasonable accommodations throughout the hiring process.</li><li>
<strong>Professional growth and innovation:</strong>Opportunities to contribute to advanced AI research, collaborate with experienced technical teams, and influence the development of next-generation machine learning solutions.</li>