Senior AI Research Engineer

As a Senior AI Research Engineer at Cyberhaven, you will apply both traditional and cutting-edge machine learning technologies to create innovative solutions for addressing critical cybersecurity challenges. You'll work directly with customers and real data in the early stages of prototyping, collaborating closely with our CTO and product teams to transition those prototypes into impactful products. Your role will also involve leveraging exclusive datasets and advanced techniques, such as graph neural networks and transformers, to enhance our AI-driven data protection strategy.

To qualify for the Senior AI Research Engineer role at Cyberhaven, you should have a robust background in applying machine learning techniques to solve real-world problems, either in research or industry contexts. Familiarity with transformer models like BERT or GPT, proficiency in frameworks such as PyTorch or TensorFlow, and excellent communication skills are essential. Bonus points if you have deep learning research experience, publications, or a Ph.D. While not mandatory, having experience in building applications on large language models or conducting anomaly detection will also enhance your application.

At Cyberhaven, a Senior AI Research Engineer can expect to work on exciting projects that harness the latest in AI technology, focused on solving critical cybersecurity challenges. You will engage in prototyping foundational models and innovative applications tailored to improving data security. The work encompasses collaborative efforts to refine these prototypes into market-ready products, addressing significant insider threats to sensitive data through advanced AI-driven solutions.

Cyberhaven is dedicated to the continuous learning and growth of its team members. As a Senior AI Research Engineer, you can count on career advancement opportunities supported by a culture that values deep thinking and ownership. With access to cutting-edge technologies, mentorship from experienced professionals, and an opportunity to contribute to impactful projects, your professional development is a priority at Cyberhaven.

The work environment at Cyberhaven is collaborative, dynamic, and centered on innovation. As a Senior AI Research Engineer, you'll work with a passionate team of experts in AI and cybersecurity. We encourage maintaining a strong focus on customer needs while ensuring our solutions are effective. You'll find Cyberhaven to be a place where diversity is celebrated, where you will be able to thrive while tackling significant problems in data protection through AI.

When discussing your experience with machine learning techniques relevant to cybersecurity, focus on specific projects where you've successfully applied these techniques. Highlight your familiarity with various models, such as transformers, and explain how they've contributed to solving real-world security issues. Always support your answers with quantitative results or outcomes, as this demonstrates your ability to deliver effective solutions.

When answering what attracts you to the position, emphasize your passion for both AI and cybersecurity and how they intersect at Cyberhaven. Mention your enthusiasm about working on data protection solutions that address critical insider threats. Reflect on how Cyberhaven's innovative approach and commitment to evolving technologies inspire you to be a part of their mission.

Explain your methodology for tackling complex AI research problems by detailing your analytical thinking, creativity, and problem-solving techniques. Discuss your process in identifying the core issue, conducting data analysis, experimenting with various models, and refining solutions. Showcase your ability to collaborate with team members to seek diverse perspectives in addressing challenges.

Share a specific example of a project that required you to quickly learn a new technology or framework. Discuss your approach to ramping up your knowledge efficiently—whether it was through online resources, collaboration with colleagues, or hands-on experimentation—and highlight the positive outcomes of the project as a result of your efforts.

When discussing your experience with graph neural networks, provide details of specific projects or scenarios where you applied this technology. Talk about the challenges you faced, the methodologies you used, and the results achieved. If you have published work related to this topic or can reference peer-reviewed articles, it would further bolster your argument and demonstrate your expertise.

Outline your strategies for staying current with trends in AI and cybersecurity, such as following relevant journals, attending conferences, participating in online forums, and engaging with industry thought leaders. Mention specific blogs, podcasts, or networking events you've found particularly informative. This demonstrates your commitment to continuous learning and professional growth.

Discuss your insights on the transformative impact AI is having—and will continue to have—on cybersecurity practices. Elaborate on how AI can enhance threat detection, automate responses, and improve the overall security posture of organizations. Show your understanding of the balance between machine intelligence and human oversight in effective cybersecurity strategies.

When answering this question, emphasize your communication skills and the importance of relatability when explaining complex concepts. Detail how you would break down complex ideas into simple, digestible terms using relatable analogies, visuals, or practical examples that convey the essence of the technology. Highlight past instances where you've effectively communicated technical information to diverse audiences.

Select a specific achievement that demonstrates your technical acumen, leadership skills, or innovative approach to problem-solving. Articulate the context of the achievement, the steps you took to realize it, and the impact it had on the organization or the industry. Include metrics or broader implications when possible to emphasize the significance of your work.

Describe your approach to collaborative research and development by reflecting on past experiences in team settings. Focus on your ability to communicate openly, share knowledge, and respect diverse perspectives. Highlight any methods you've used for fostering collaboration, such as sprint planning, regular check-ins, or community sharing tools, and emphasize how this approach leads to more robust and successful outcomes.