Job Title: Applied Researcher II
Job Responsibilities:
- Partner with a cross-functional team consisting of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products that transform customer interactions with their finances.
- Utilize a diverse stack of technologies such as Pytorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs to extract insights from large volumes of numeric and textual data.
- Develop AI foundation models across all phases, including design, training, evaluation, validation, and implementation.
- Conduct high-impact applied research to advance the latest AI developments and enhance customer experiences.
- Communicate complex work effectively to align with tangible business objectives.
- Innovate by researching and evaluating emerging technologies, and apply state-of-the-art methods, technologies, and applications where applicable.
- Lead by challenging conventional thinking and collaborating with stakeholders to improve existing processes.
- Develop talent within the team and beyond.
- Demonstrate technical expertise in open-source languages and cloud computing platforms, with hands-on experience in developing AI foundation models and solutions.
- Possess a deep understanding of AI methodologies and experience in building large deep learning models on various data types, specializing in areas such as training optimization, self-supervised learning, robustness, explainability, and RLHF.
- Exhibit an engineering mindset by delivering scalable models in terms of training data and inference volumes.
- Contribute to existing products by delivering libraries, platform-level code, or solution-level code.
- Innovate in machine learning through first-author publications or projects.
- Own and pursue a research agenda by selecting impactful research problems and autonomously conducting long-term projects.
Education and Experience Information:
Basic Qualifications:
- Currently pursuing or holding a PhD with at least 2 years of experience in Applied Research, or a Master's degree with at least 4 years of experience in Applied Research.
Preferred Qualifications:
- PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
- Specialization in NLP with a PhD focus or a Master's degree with 5 years of industrial NLP research experience.
- Publications on pre-training of large language models, SSL techniques, and model pre-training optimization.
- Experience in training large language models from scratch.
- Publications in deep learning theory and at major conferences like ACL, NAACL, EMNLP, Neurips, ICML, or ICLR.
- Expertise in optimizing training and inference for large deep learning models, with multiple years of experience and/or publications on topics like Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, and Model Compression.
- Experience optimizing training for large-scale models (10B+ parameters).
- Deep knowledge of deep learning algorithmic and optimizer design, and experience with compiler design.
- PhD focused on finetuning topics like Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, and Parameter Tuning.
- Demonstrated knowledge of transfer learning, model adaptation, and model guidance.
- Experience deploying fine-tuned large language models.
Salary Information:
- Cambridge, MA: $257,300 - $293,700
- McLean, VA: $257,300 - $293,700
- New York, NY: $280,700 - $320,400
- San Francisco, CA: $280,700 - $320,400
- San Jose, CA: $280,700 - $320,400
Additional Information:
- The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
- Offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits.
- Applications accepted for a minimum of 5 business days.
- Equal opportunity employer committed to diversity and inclusion.
- Consideration for employment without regard to sex, race, age, national origin, religion, disability, sexual orientation, gender identity, and other protected statuses.
- Compliance with laws regarding criminal background inquiries.
- Reasonable accommodations available for applicants requiring assistance during the application process.
- Technical support available for questions about the recruiting process.
