Description
Tyto Athene is seeking a driven and adaptable Machine Learning Engineer to help shape the future of cybersecurity through automation and machine learning. This role is an opportunity to work across diverse environments—supporting internal product teams, commercial security clients, and U.S. federal customers—while solving meaningful problems with cutting-edge tools and research. At Tyto Athene , innovation meets mission-critical impact. Join a team where your ideas lead to real-world solutions for government and commercial partners. Work remotely with talented engineers, grow your skills, and help us redefine what’s possible with automation.
Responsibilities:
- Serve as a subject matter expert in Machine Learning and its applications in Cyber Defense, researching and implementing differentiating and novel ML-based solutions to problems SOCs face.
- Developing agentic solutions for Cyber Incident Response.
- Solve complex security and architecture challenges with creativity and precision, with a focus on real-time performance and scalability.
- Identify and recommend improvements to architectures, especially in secure environments.
Qualifications
Required:
- U.S. Citizenship (required for federal projects)
- 3+ years of experience with ML
- 3+ years of experience with Cybersecurity, (purple preferred)
- Strong MLOps skills and understanding of the severity of risks such as semantic drift in a security-oriented system.
- Experience developing pipelines that feed to an SOC, ideally an active system such as anomaly detection or IDS rather than just a static or periodically updated dashboard.
- Proficiency with major cloud platforms, such as AWS, Azure and GCP
- Excellent problem-solving, communication, and collaboration skills
- Ability to work independently with distributed teams in a fast-paced, agile environment
- Eagerness to learn new technologies and drive innovation
Desired:
- Passionate about cybersecurity and the impact AI will have on the speed and scale of the industry.
- Understands ML theory outside of the current trends, e.g. knows pre-LLM NLP theory and how approaches such as genetic algorithms and energy-based models work.
- Exposure to DoD NetSecOps, even if only as a compliant, s.t. the applicant has an understanding of where the focuses are in DoD network security and what could help them.
- Understands DoD and federal compliance and expectations with regards to security.
- Understands more sophisticated and still-experimental ML approaches such as world models, neuro-symbolic learning, spiking neural networks, experimental RL objectives such as curiosity or information density, etc.
About Tyto Athene
Compensation: