Overview:
- **MEMBERS ONLY**SIGN UP NOW***. is looking for an Applied Researcher I to join their AI Foundations team.
- The ideal candidate will have a PhD or MS with at least 2 years of experience in Applied Research.
- This role will involve partnering with cross-functional teams to deliver AI-powered products, leveraging a broad stack of technologies, and engaging in high impact applied research.
- The AI Foundations team is responsible for bringing **MEMBERS ONLY**SIGN UP NOW***.'s vision for AI to life, working with product, technology, and business leaders to apply the state of the art in AI to their business.
- The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount **MEMBERS ONLY**SIGN UP NOW***. is willing to pay at the time of this posting.
- This role is also eligible for performance-based incentive compensation.
- **MEMBERS ONLY**SIGN UP NOW***. offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
- **MEMBERS ONLY**SIGN UP NOW***. is an equal opportunity employer committed to diversity and inclusion in the workplace.
- If you require an accommodation during the application process, please contact **MEMBERS ONLY**SIGN UP NOW***. Recruiting.
- **MEMBERS ONLY**SIGN UP NOW***. does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site.
- This role is expected to accept applications for a minimum of 5 business days.
- No agencies please.
Job Title: Applied Researcher I
Salary: $230,000 - $278,100 (depending on location)
Experience: PhD or MS with at least 2 years of experience in Applied Research
Team Description:
- The AI Foundations team is at the center of bringing **MEMBERS ONLY**SIGN UP NOW***.'s vision for AI to life.
- Their work touches every aspect of the research life cycle, from partnering with Academia to building production systems.
- They work with product, technology, and business leaders to apply the state of the art in AI to their business.
Responsibilities:
- Partner with cross-functional teams to deliver AI-powered products.
- Leverage a broad stack of technologies to reveal insights hidden within huge volumes of data.
- Build AI foundation models through all phases of development.
- Engage in high impact applied research to push the latest AI developments into the next generation of customer experiences.
- Translate the complexity of your work into tangible business goals.
Ideal Candidate:
- Has a PhD or MS with at least 2 years of experience in Applied Research.
- Loves the process of analyzing and creating.
- Shares **MEMBERS ONLY**SIGN UP NOW***.'s passion to do the right thing for their customers.
- Continually researches and evaluates emerging technologies.
- Stays current on published state-of-the-art methods and seeks out opportunities to apply them.
- Thrives on bringing definition to big, undefined problems.
- Loves asking questions and pushing hard to find answers.
- Is not afraid to share new ideas.
- Challenges conventional thinking and works with stakeholders to identify and improve the status quo.
- Is passionate about talent development for their own team and beyond.
- Is comfortable with open-source languages and passionate about developing further.
- Has hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
- Has a deep understanding of the foundations of AI methodologies.
- Has experience building large deep learning models and expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
- Has an engineering mindset and a track record of delivering models at scale.
- Has experience in delivering libraries, platform level code, or solution level code to existing products.
- Has a track record of coming up with high-quality ideas or improving upon existing ideas in machine learning.
- Has the ability to own and pursue a research agenda.
Basic Qualifications:
- Currently has, or is in the process of obtaining, a PhD, with an expectation that the required degree will be obtained on or before the scheduled start date, or . with at least 2 years of experience in Applied Research.
Preferred Qualifications:
- PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
- LLM.
- PhD focus on NLP or Masters with 5 years of industrial NLP research experience.
- Multiple publications on topics related to the pre-training of large language models.
- Member of a team that has trained a large language model from scratch.
- Publications in deep learning theory.
- Publications at top conferences such as ACL, NAACL, and EMNLP, Neurips, ICML, or ICLR.
- PhD focus on topics in geometric deep learning.
- Multiple papers on topics relevant to training models on graph and sequential data structures.
- Worked on scaling graph models to greater than 50m nodes.
- Experience with large scale deep learning based recommender systems.
- Experience with production real-time and streaming environments.
- Contributions to common open source frameworks.
- Proposed new methods for inference or representation learning on graphs or sequences.
- Worked with datasets with 100m+ users.
- PhD focused on topics related to optimizing training of very large deep learning models.
- Multiple years of experience and/or publications on topics such as Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, and Model Compression.
- Experience optimizing training for a 10B+ model.
- Deep knowledge of deep learning algorithmic and/or optimizer design.
- Experience with compiler design.
- PhD focused on topics related to guiding LLMs with further tasks.
- Demonstrated knowledge of principles of transfer learning, model adaptation, and model guidance.
- Experience deploying a fine-tuned large language model.
- Publications studying tokenization, data quality, dataset curation, or labeling.
- Contribution to a major open source corpus.
- Contribution to open source libraries for data quality, dataset curation, or labeling.
Salary Information:
