Machine Learning Scientist 4 - Generative Models, Evaluation

Accountabilities:

• • Advance generative AI research: Monitor the latest developments in LLMs, generative models, and related research, identifying techniques with potential to solve meaningful business and product challenges.
  • Develop advanced ML solutions: Apply state-of-the-art machine learning and deep learning approaches to problems such as summarization quality, generated-content assessment, and automated evaluation.
  • Build LLM-based evaluators: Research and develop AI-powered “judge” systems capable of assessing whether generated content meets defined quality standards and improving evaluation reliability.
  • Own end-to-end experimentation: Lead the full machine learning development lifecycle, from research and hypothesis formation through model training, experimentation, statistical analysis, evaluation, and iteration.
  • Translate research into applications: Partner closely with machine learning scientists and engineers to integrate models into scalable business applications and technology platforms.
  • Shape technical strategy: Serve as a subject matter expert, identifying high-value opportunities, defining technical roadmaps, and influencing strategic decisions across multidisciplinary teams.
  • Communicate complex ideas: Translate sophisticated ML and generative AI concepts into clear recommendations and insights for both technical and non-technical stakeholders.
  • Contribute to the ML community: Engage with internal and external research communities by sharing knowledge, learning from emerging work, and contributing to a culture of technical excellence and innovation.

  Requirements:

  • Demonstrated experience following advances in LLMs and generative AI research, with the ability to distinguish promising developments from those that are less applicable to real-world business problems.
  • Strong foundations in machine learning and deep learning, particularly in natural language processing, natural language understanding, and text generation.
  • Research or applied experience in summarization, generated-text evaluation, LLM-as-a-judge methodologies, or related evaluation techniques is highly valuable.
  • Familiarity with vision-based generative models, including diffusion models, is a plus.
  • Expertise in one or more LLM post-training approaches and an understanding of modern techniques for improving generative model performance.
  • Proven ability to design, execute, and analyze rigorous ML experiments, including hypothesis development, controlled experimentation, statistical validation, and interpretation of results.
  • Strong research mindset combined with the ability to connect technical innovation to practical product or business outcomes.
  • Excellent communication and collaboration skills, with the ability to influence technical strategy and work effectively across scientific, engineering, and business functions.
  • Ability to operate independently, identify high-impact opportunities, and take ownership of complex problems from initial research through implementation.

  Benefits:

  • Annual compensation range: $300,000–$537,000, with the range varying according to location, job family, background, skills, and experience.
  • Flexible compensation structure: Compensation is generally provided through annual salary, with the option to determine the preferred balance between salary and stock options each year.
  • Health coverage: Comprehensive health plans, including support for physical and mental health.
  • Financial benefits: 401(k) retirement plan with employer match and stock option program.
  • Health accounts: Health Savings Accounts and Flexible Spending Accounts.
  • Protection benefits: Disability programs, life insurance, and serious injury benefits.
  • Family support: Family-forming benefits designed to support employees through different stages of family life.
  • Time off: Paid leave programs, with salaried employees receiving flexible time off.
  • Remote work: Full-time remote position within the United States.
  • Inclusive environment: Commitment to diversity, inclusion, equal opportunity, and meaningful candidate and employee experiences.