Applied Researcher I: AI Foundations Team at **Members Only**
Salary Information:
- Minimum and maximum full-time annual salaries for this role are listed below, by location:
- New York City (Hybrid On-site): $230,000 - $262,500 for Applied Researcher I
- San Francisco, California (Hybrid On-site): $243,700 - $278,100 for Applied Researcher I
- San Jose, California (Hybrid On-site): $243,700 - $278,100 for Applied Researcher I
- Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
- This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non-discretionary depending on the plan.
Job Title:
- Applied Researcher I: AI Foundations Team at **Members Only**
Experience Information:
- Currently has, or is in the process of obtaining, a PhD, with an expectation that 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 [choose correct set based on focus of role]:
- 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 (. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)
- Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)
- Publications in deep learning theory
- Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
- Behavioral Models
- PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
- Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR
- 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 (pytorch-geometric, DGL)
- Proposed new methods for inference or representation learning on graphs or sequences
- Worked datasets with 100m+ users
- Optimization (Training & Inference)
- PhD focused on topics related to optimizing training of very large deep learning models
- Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression
- Experience optimizing training for a 10B+ model
- Deep knowledge of deep learning algorithmic and/or optimizer design
- Experience with compiler design
- Finetuning
- PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)
- Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance
- Experience deploying a fine-tuned large language model
- Data Preparation
- 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
- Has a deep understanding of the foundations of AI methodologies.
- Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
- An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
- Experience in delivering libraries, platform level code or solution level code to existing products.
- A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
- Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.
Job Description:
- AI Foundations team at **Members Only** is at the center of bringing our vision for AI to life.
- Work touches every aspect of the research life cycle, from partnering with Academia to building production systems.
- Work with product, technology and business leaders to apply the state of the art in AI to our business.
- Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.
- Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
- Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
- Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
- Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
Key Responsibilities:
- Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact
