Senior AI Research Engineer - Canada

The role of a Senior AI Research Engineer at Leonardo.Ai involves several key responsibilities including training and optimizing models for visual intelligence, solving engineering challenges related to model training across thousands of GPUs, and designing high-performance distributed system infrastructures. You'll also collaborate with cross-functional teams to enhance platform features and stability while mentoring junior engineers, which plays a crucial role in the overall strategic planning of the projects.

To excel as a Senior AI Research Engineer at Leonardo.Ai, candidates should possess strong skills in Python, deep learning frameworks, and cloud computing technologies like AWS or GCP. A solid understanding of AI concepts, particularly generative AI and computer vision, as well as experience in model training and optimization is essential. Familiarity with tools such as Torch, SageMaker, and CUDA, as well as a creative mindset to explore and implement new AI techniques, are also highly valued.

At Leonardo.Ai, professional development is a top priority for Senior AI Research Engineers. The company provides continuous learning opportunities, encourages attendance at industry conferences, and supports tailored career growth paths. Engineers receive mentorship from experienced professionals, ensuring that they can stay abreast of the latest tools and techniques within the AI space, helping them grow both personally and professionally.

The work culture at Leonardo.Ai is inclusive and diverse, focusing on creating an empowering environment where all employees feel valued. Senior AI Research Engineers enjoy a flexible work environment, allowing them to choose between remote work or vibrant office settings. The company fosters teamwork and collaboration, while also prioritizing work-life balance and employee well-being through various initiatives, perks, and company events.

Senior AI Research Engineers at Leonardo.Ai can expect a rewarding benefits package designed to support both their professional and personal lives. This includes a competitive salary with equity options, an inclusive parental leave policy, an annual Vibe & Thrive allowance for well-being, 20 days of annual leave, and RRSP matching. Additionally, flexible leave options and opportunities for professional development are readily available, empowering engineers to thrive in their careers.

In answering this question, share specific projects or experiences where you trained large-scale AI models. Discuss the challenges you faced, how you optimized performance in high-load environments, and the tools or frameworks you utilized. Emphasize your analytical skills and ability to make data-driven decisions during the training process.

When asked about your approach, outline your problem-solving methodology, which may include identifying the problem, gathering data, deciding on an optimal solution, implementing it, and analyzing the results. It's essential to demonstrate your ability to collaborate with team members and utilize iterative techniques for continuous improvement.

Explain your techniques for optimizing model inference, such as model pruning, quantization, or minimizing data transfer overhead. Provide examples of how you have effectively reduced latency and maximized resource utilization in previous projects, ensuring that your solutions deliver real-time results.

Share your familiarity with cloud computing platforms, highlighting specific services you have used for machine learning operations, such as EC2 or SageMaker. Discuss any projects where you designed and implemented scalable architectures for AI solutions, as well as your understanding of cost optimization and security considerations in cloud environments.

Articulate your mentoring strategy, such as setting clear expectations and providing constructive feedback. Use a specific example of how you helped a junior engineer improve their skills or tackle a challenging project, emphasizing the impact of your guidance on their professional growth.

When answering, highlight specific resources you utilize, such as research papers, blogs, webinars, and conferences. Discuss any active participation in the AI community, like contributing to open-source projects or attending meetups, to illustrate your commitment to staying informed about the latest trends and advancements.

Describe specific challenges you've encountered, such as data integration complexities or model training issues, and how you addressed them. It’s essential to demonstrate your problem-solving skills and adaptability when working with varied data types such as text, video, and imagery.

Explain your strategies for performance tuning, including profiling, load testing, and analyzing various components. Refer to specific tools or techniques you have utilized to identify bottlenecks in system performance and how you implemented changes that positively impacted overall efficiency.

Detail your organizational strategies, such as project management tools, prioritization frameworks, or time-blocking techniques. Discuss how clear communication with your team members plays a crucial part in ensuring deadlines are met without sacrificing quality.

In your response, convey your enthusiasm for Leonardo.Ai's mission, culture, or innovative projects. Mention how your values align with the company's vision for democratizing design and creativity through AI, and how you believe your expertise can contribute to achieving those goals.