Manager, Applied Researcher

About the team and role

Role responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Use technologies such as PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and vector databases to analyze large numeric and textual datasets.
• Build foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to advance customer experiences and translate technical complexity into business goals.
• Evaluate emerging technologies, define ambiguous problems with stakeholders, and pursue impactful research projects.

Ideal candidate qualifications

• Hands-on experience developing AI foundation models and solutions with open-source tools and cloud platforms; deep understanding of AI methodologies.
• Experience building large deep learning models for language, images, events, or graphs, with expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Track record delivering models at scale and delivering libraries, platform code, or solution code into existing products.
• Research accomplishments such as first-author publications or projects, plus ability to own a research agenda and independently pursue long-running work.
• Basic qualification: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, completed by start date; alternatively, an MS in a related field plus 2 years of applied research experience.

Preferred research background

• Preferred backgrounds include a PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• LLM/NLP specialization may include NLP research experience, publications on LLM pretraining, experience training large language models, or publications in deep learning theory and relevant research venues.
• Optimization specialization may include large-model training optimization, sparsification, quantization, parallelism, gradient checkpointing, compression, optimizer design, or compiler design.
• Fine-tuning specialization may include supervised or instruction tuning, transfer learning, model adaptation, and deployment of fine-tuned language models.

Work arrangement

• The company describes its culture as hybrid, with three days in the office per week.

Compensation

• Expected annual salary range is $173,000 to $197,400, with eligibility for performance-based incentive compensation that may include cash bonuses.

Benefits

• Benefits include coverage for spouses, domestic partners, and dependents; a one-time work-from-home allowance; mental health coverage up to $3,000; and annual tuition subsidies up to $5,000.
• The company describes access to upskilling tools and mentorship and a flexible, high-performance work culture.