Engineering Lead, AI

As an Engineering Lead, AI at Incention, your main responsibilities include leading the technical strategy for the AI stack, scaling the AI infrastructure, and developing tools that enable efficient deployment of AI-driven experiences. You will also collaborate with various teams to bring ideas into production and mentor fellow engineers, ensuring best practices are followed in building robust AI systems.

To qualify for the Engineering Lead, AI role at Incention, candidates should have at least 8 years of software engineering experience, a proven track record of shipping AI/ML products, and deep knowledge of LLMs and system design. Strong leadership and communication skills are critical, along with a passion for mentoring others in a cross-functional setting.

At Incention, the work environment is collaborative, innovative, and dynamic. The Engineering Lead, AI will engage with cross-functional teams, including engineering, product, and creative individuals, fostering a space where creativity flourishes and everyone contributes to the company’s mission to reshape storytelling through AI.

Incention provides a range of benefits for the Engineering Lead, AI role, including comprehensive health coverage, unlimited PTO, quarterly mental health days, a 401k plan, and flexible parental leave options to support work-life balance and employee well-being.

You should consider applying for the Engineering Lead, AI position at Incention if you are passionate about the intersection of technology and storytelling. This role offers the opportunity to lead innovative projects, work with cutting-edge AI technology, and be part of a forward-thinking team dedicated to pushing the boundaries of interactive storytelling.

When discussing your experience with AI/ML products, highlight specific projects where you've contributed to their development. Talk about the technologies used, your role in the project, and the impact these products had on users or the business. Using metrics to showcase success can also add significant value.

To effectively answer this, outline the strategies you've employed in past roles to scale AI infrastructure. Discuss any frameworks, tools, or methodologies that worked well, as well as any challenges faced and how you overcame them. It’s also beneficial to emphasize your collaborative approach with product and engineering teams.

Discuss your strategies for establishing best practices, including continual learning, setting up coding standards, conducting code reviews, and promoting testing and deployment protocols. Provide examples of how these practices positively influenced the team’s output and system reliability.

Share a specific example illustrating your leadership in guiding a cross-functional team. Focus on how you facilitated collaboration, resolved conflicts, and aligned everyone towards a common goal. Highlight the successful outcomes from that teamwork and any lessons learned.

Convey your commitment to continuous learning by mentioning various resources you follow, such as journals, blogs, conferences, or online courses. Discuss specific topics in AI that fascinate you and how you’ve applied new knowledge to your work.

When answering this question, emphasize your approach to mentorship, including providing constructive feedback, sharing your knowledge, and encouraging independent problem-solving. Share examples of how mentoring has contributed to your team members' growth and the overall success of the projects.

Discuss your understanding of prompt engineering, including how it impacts AI performance. Provide examples of your work where you employed prompt engineering to improve model outputs and user interactions, showcasing your problem-solving capabilities in real-time scenarios.

Talk about specific tools and frameworks you have used for AI development, such as TensorFlow, PyTorch, or any programming languages like Python. Explain why you prefer these tools, mentioning any projects where they helped in achieving project goals.

When discussing system design, outline your process, including requirements gathering, architecture design, and scalability considerations. Provide examples of past designs that successfully meet user needs and were scalable, emphasizing your analytical and strategic thinking.

Identify a few challenges you have encountered in AI system development, such as data limitations or integration issues. Describe the steps you took to resolve these challenges, focusing on your analytical skills and collaboration with your team for effective solutions.