Director, Generative AI

Key Skills

Aartificial intelligence, AWS, Azure, C Programming, C++, Generative AI, Google Cloud Platform (GCP), GPUs, Large Language Models - LLMs, Machine learning techniques, Python Programming, PyTorch, SDK, TensorFlow

Job Description

We are looking for an experienced Director, Generative AI to help us build the foundations of our Generative AI platform. You will lead a team of AI engineers and applied scientists, and work on a wide range of initiatives, whether that’s building large-scale distributed training clusters, optimizing training pipelines to underlying hardware/software stack, or deploying LLMs on GPU instances for real-time applications and decisioning systems, all in our public cloud infrastructure. You will work closely with other infrastructure and research teams to design and implement key capabilities of our platform. Examples of projects you will work on:

• Deploy a thousand-node training cluster optimizing storage and networking stack, with tightly coupled training pipelines to take advantage of multiple parallelism strategies, in our public cloud.

• Design and build fault-tolerant infrastructure to support long-running large-scale training tasks reliably despite failure of individual nodes, using containers and check-pointing libraries.

• Design and build run-time infrastructure for serving large ML models such as LLMs and FMs in our public cloud as well as secure third-party cloud providers.

• Build infrastructure for deploying search indexes and embeddings in vector databases that will work closely with the rest of our platform.

• Develop SDKs and APIs for our user community to build agents, information retrieval and anomaly/fraud detection systems on our Gen AI platform. 

Basic Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering or a technical field

• At least 7 years of experience designing and building large-scale ML systems

• At least 6 years of experience developing AI/ML algorithms in Python or C/C++

• At least 5 years of experience leading teams of engineers and applied scientists

• At least 3 years of experience with the full ML development lifecycle using open-source AI/ML frameworks and public cloud

Preferred Qualifications:

• Master’s degree or PhD in Computer Science, a related technical field, or or equivalent practical experience with experience in leading applied science and engineering teams.

• Background in machine learning with experience in large scale training and deployment of deep neural nets and/or transformer architectures.

• Authored research publications in top peer-reviewed conferences, or industry-recognized open-source contributions in the space of neural networks, distributed training and SysML.

• Experience with machine learning frameworks such as TensorFlow or Pytorch, Lightning, Mosaic ML on GPU clusters with a focus on distributed training, checkpointing and fault-tolerance.

• Ability to move fast in an environment with ambiguity at times, and with competing priorities and deadlines. Experience at tech and product-driven companies/startups preferred.

• Ability to iterate rapidly with researchers and engineers to improve a product experience while building the core components of a platform.

• Experience working with public cloud infrastructure such as AWS, Azure or GCP.

• Familiarity with deploying large neural network models in demanding production environments.

At this time, Capital One will not sponsor a new applicant for employment authorization for this position.

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 Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

New York City (Hybrid On-Site): $274,800 - $313,600 for Director, Machine Learning Engineering

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.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.