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

Jobgether SRL; United States; Remote; USD 300,000 - 537,000; Full time

Benefits offered by this job

Competitive pay

Stock options

Health insurance

Mental health support

401(k) match

Disability protection

HSA/FSA

Remote work

Job summary

Jobgether SRL in the United States is seeking a senior ML researcher to advance AI and generative models. You will drive the end-to-end lifecycle from research to production, collaborating with engineers and product teams to translate cutting-edge techniques into scalable solutions.

The role emphasizes LLM evaluation, text generation, personalization, and cross-functional leadership, offering a fully remote position from anywhere in the United States and a comprehensive benefits package.

Qualifications

• Strong foundational knowledge of machine learning, deep learning, NLP, NLU, and text generation.
• Experience tracking scientific literature on LLMs and generative models and translating findings to business.
• Experience leading ML projects end-to-end from research to production.
• Excellent communication and collaboration across multidisciplinary teams.

Responsibilities

• Monitor latest developments in LLMs and generative AI and translate into practical solutions.
• Lead end-to-end ML lifecycle including training and evaluation.
• Improve content quality with summarization and evaluation systems.
• Design AI-based evaluators (LLM-as-a-judge) for outputs.
• Combine generative content with recommendation systems to improve relevance.
• Collaborate to integrate models into production workflows.
• Define technical roadmaps and communicate findings to stakeholders.
• Engage with research communities.

Skills

ML fundamentals

NLP & NLU

Text generation

LLM post-training

Model adaptation

Production deployment

Cross-functional leadership

Communication skills

Job description

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.