Machine Learning Technical Director

As a Two Six Technologies (TST) Machine Learning (ML) Lead Research Engineer, you will lead applied ML research teams across TST, with particular focus on the Cyber business unit.  As lead ML research engineer, your primary area of focus will be adversarial ML research within the computer vision and/or audio ML domains.  You will provide subject matter expertise and mentoring to ML researchers on multiple teams, creating products based on state-of-the-art industry and academic concepts.  You will create, analyze, and red-team ML systems, ensuring their functionality and robustness in a variety of situations.  You will provide feedback to your teams, provide feature requests, and help create user interfaces, tutorials, and documentation.

Duties:

• Develop novel adversarial ML attacks to evaluate the robustness of innovative ML algorithms and defenses from leading publishers in academia and industry
• Synthesize results and research from evaluation findings to advance the state-of-the-art and guide future research for ML adversarial methods
• Partner with engineers to build and maintain a leading adversarial ML testbed currently used by several industry, government, and academic partners
• Evaluate the viability of ML solutions for multiple programs, focusing on adversarial ML research within the computer vision and/or audio ML domains
• Work collaboratively with peers, including software developers and research scientists, in an agile environment on a fully-remote team
• Communicate your methods and results to a variety of stakeholders and customers

Requirements:

• 8+ years experience in ML or related fields
• Academic or industry conference publications in ML or related disciplines
• Graduate technical degree in CS, EE, math, or similar field, or equivalent experience
• Academic or industry conference publications in ML or related disciplines
• Experience developing in Python and in using PyTorch, TensorFlow, or JAX
• Experience with common machine learning pipelines, model architectures, and metrics
• Experience reading and analyzing academic publications in machine learning

Nice-to-haves:

• Established record as a Principal Investigator on one or more ML or related projects
• Experience with agile development, CI testing, code reviews, and working with other people’s code