Senior Engineering Manager, AI (Artificial Intelligence)-FULLY REMOTE

As a Senior Engineering Manager, AI at Splunk, you'll be responsible for leading the development of core AI/ML models and algorithms that are essential for our product's key use cases, particularly in cybersecurity and observability. You'll create a long-term strategy for integrating AI/ML technology across our product portfolio, collaborate closely with product leads, and mentor a talented team of engineers. Staying current on the latest AI/ML research is also a critical part of the role to ensure we incorporate valuable advancements into our technology roadmap.

To be considered for the Senior Engineering Manager, AI position at Splunk, you should possess in-depth knowledge and a proven track record in AI/ML technology, including deep learning and natural language processing. Experience leading machine learning teams, developing large-scale models, and familiarity with cybersecurity or observability is highly preferred. Furthermore, skills in executing innovation strategies at a technical and organizational level are essential to drive business objectives successfully.

At Splunk, the Senior Engineering Manager, AI operates within a collaborative ecosystem, leading a diverse team of applied scientists and engineers. You'll partner with product leads and other cross-functional team members, fostering an engaging environment where members can communicate effectively, learn continuously, and hold each other accountable for achieving business goals. Mentoring junior team members is also an essential aspect of the role, ensuring they develop their skills and contribute to collective achievements.

AI/ML plays a pivotal role in the Senior Engineering Manager position at Splunk, as the technologies are critical to shaping the product portfolio's future. You'll oversee the development of innovative models and algorithms that enhance our capabilities in cybersecurity and observability. As we strive to make machine data accessible and valuable, your leadership in driving AI/ML integration will significantly enhance customer experiences and ensure that Splunk remains at the forefront of industry advancements.

Splunk fosters a culture that prioritizes belonging, creativity, and collaboration, making it an exciting place for someone in the Senior Engineering Manager, AI role. The company is committed to diversity, equity, and inclusion, creating an environment where employees feel supported and inspired to bring their authentic selves to work. Enjoying a positive, engaging atmosphere ensures that innovative ideas are generated and nurtured, leading to driving impactful changes in the AI/ML landscape.

When developing a long-term AI/ML strategy for a product, I would first assess the current capabilities and limitations of the existing technology. Collaborating with cross-functional teams, I would identify key user needs and market opportunities to prioritize areas for innovation. Keeping abreast of the latest AI/ML research would also inform decision-making. Finally, I would establish clear milestones and execute an iterative process to refine our approach continuously.

Certainly! In my previous role, I led a project where we implemented a machine learning model that significantly improved anomaly detection in a cybersecurity product. By overseeing a cross-disciplinary team, establishing KPIs, and iterating based on feedback, we not only met our deadlines but also exceeded performance expectations, leading to a 40% reduction in false positives. Communication and continuous improvement were key to our success.

Some challenges in leading an AI/ML team include keeping pace with the rapid technological advancements in the field and ensuring that team members remain engaged and motivated. Additionally, harmonizing diverse skill sets in a team can be difficult, so I prioritize regular knowledge-sharing sessions to align on objectives and foster collaboration. Having clear communication and focusing on collective goals are also crucial for overcoming these challenges.

I prioritize reading academic journals, attending relevant conferences, and participating in webinars to stay updated on the latest advancements in AI/ML. I also network regularly with peers and thought leaders in the industry. This continuous learning helps me to not only improve my skillset but also to bring fresh insights and ideas to the team at Splunk, ensuring we are always utilizing cutting-edge technology in our projects.

To mentor junior team members effectively, I would begin with one-on-one discussions to understand their backgrounds, strengths, and areas for growth. I'd provide them with guided opportunities to work on projects while encouraging them to ask questions and seek feedback. Regular check-ins and collaborative activities would help them feel supported and foster a culture of continuous learning. Celebrating their achievements, no matter how small, also boosts their confidence and motivation.

In my previous positions, I was deeply involved in deploying AI/ML models across various production systems. My experience includes ensuring that we have a robust infrastructure and monitoring systems in place to maintain the model's performance in real-time. Working closely with DevOps teams, I learned the importance of defining clear guidelines for model retraining and performance evaluation to adapt quickly to changes in data trends.

To ensure cross-functional collaboration in AI projects, I advocate for regular alignment meetings with stakeholders from different teams. By establishing joint goals and defining clear expectations, we can create a shared vision for the project. Utilizing collaboration tools and maintaining an open feedback loop drives communication, ensuring everyone stays informed and engaged through every phase of development.

Key metrics to measure the success of an AI project typically include accuracy, precision, recall, and F1-score, which help assess model performance. Additionally, tracking user engagement and customer satisfaction post-implementation can provide insights into how well the AI solution meets user needs. Regular monitoring and adjustments based on these metrics can ensure continued success and alignment with business goals.

I approach innovation within my team by fostering an environment where experimentation is encouraged. I organize brainstorming sessions that invite diverse perspectives on problem-solving, and I support team members in pursuing innovative ideas through dedicated time for exploration. By providing resources, tools, and a safe space for trial and error, we can collaboratively discover breakthroughs that can significantly impact our projects.

In the first 90 days as a Senior Engineering Manager, AI at Splunk, my priorities would be to familiarize myself with the team dynamics, assess ongoing projects, and identify immediate opportunities for improvement. I would work to build strong relationships across departments and align with key stakeholders on strategic priorities. Creating a clear roadmap for the integration of AI/ML across the product portfolio would be my primary focus, ensuring we maintain momentum and set a strong foundation for our goals.