Senior Software Engineer - Generative AI

As a Senior Software Engineer at C3 AI, your main responsibilities will include developing systems and tools for using Generative AI technology, establishing scalable machine learning pipelines, collaborating with product teams, mentoring junior engineers, and influencing the long-term development of the C3 Generative AI Suite. You'll play a vital role in enhancing our enterprise applications and pushing the boundaries of machine learning technology.

To qualify for the Senior Software Engineer role at C3 AI, you should have a Bachelor’s degree in Computer Science or a related field, with a preference for a Master’s degree. You’ll need excellent programming skills in Python, experience with Java and JavaScript, and comprehensive knowledge of data structures and algorithms. Additionally, experience in deploying machine learning algorithms in production environments is crucial.

At C3 AI, we focus on developing the infrastructure and frameworks that facilitate the effective application of Generative AI technologies. This includes creating scalable tools and systems for machine learning pipelines, collaborating with data scientists to implement advanced training approaches, and enabling production-grade deployment of large-scale models. We're committed to pushing innovation and learning in the Generative AI space.

Candidates applying for the Senior Software Engineer position at C3 AI should ideally have a minimum of 3 years in a fast-paced software environment, with a demonstrated track record in software engineering projects. Experience in applying machine learning concepts, especially through real-world deployments, is essential to succeed in this role.

At C3 AI, Senior Software Engineers have ample opportunities for career development, whether it's through mentoring junior team members, leading innovative projects, or engaging with cross-functional teams to define product directions. C3 AI values initiative and offers pathways for advancing your expertise and responsibility in the field of Generative AI.

When discussing your experience with Generative AI, highlight any projects you've worked on involving technologies like LangChain, Semantic Kernel, or ChatGPT Plugins. Discuss specific challenges you faced and how you overcame them, as well as the impact your contributions had on the overall success of the project.

In your response, share a structured approach. Talk about iterating over model selection, feature engineering, and hyperparameter tuning. Discuss any tools or frameworks you've used (like TensorFlow or PyTorch) to monitor performance and make data-driven enhancements, emphasizing the importance of model validation and testing.

Discuss your experience with distributed systems, parallel processing, and managing heterogeneous hardware. Include examples of how you've utilized technologies like GPUs or TPUs, and explain your methods for optimizing performance during training and inference stages to ensure efficiency.

Choose a project where you had to navigate complex problems or work under tight deadlines. Discuss your leadership role, the obstacles you faced, your decision-making process, and how you motivated your team. Highlight the successful outcomes or improvements resulting from your efforts.

Be prepared to discuss your strongest programming languages, with a focus on Python. Give examples of projects where you've applied these languages to develop machine learning applications or tools, demonstrating your coding capabilities and knowledge of various paradigms such as object-oriented and functional programming.

Emphasize the importance of best practices like code reviews, unit testing, and adhering to coding standards. Discuss tools you use for version control like Git and methodologies like Agile, and explain how these contribute to maintaining high standards and facilitating team collaboration.

Share your expertise with various deep learning algorithms, including convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Provide examples of how you've implemented these algorithms in projects and discuss the challenges you've faced and how you've overcome them.

Showcase your knowledge of essential data structures such as arrays, linked lists, and trees, and discuss how these are important in optimizing algorithms for machine learning tasks. Provide examples of how you've applied these principles in real-world projects.

Discuss the resources you follow, such as academic journals, relevant online courses, or tech blogs. Mention any conferences or workshops you attend and describe how you actively engage with the AI community through forums or social media to stay current with industry advancements.

Reflect on your mentoring style by discussing the importance of knowledge sharing and guidance. Explain how you’ve tailored your mentoring approach based on individual needs, encouraged open communication, and helped junior engineers navigate their career paths while fostering a collaborative team environment.