AI Software Engineer

As an AI Software Engineer at webAI, you will be responsible for building, optimizing, and maintaining AI-related software packages and services. Your role involves designing and implementing efficient, production-ready AI/ML systems. Collaboration is key, as you will work closely with research, engineering, and product teams to understand requirements and integrate innovative algorithms into our platform.

Candidates for the AI Software Engineer position at webAI should hold a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. You should have at least 5 years of experience focusing on AI/ML software engineering, showcasing strong skills in C++ and Python, along with proficiency in tools like PyTorch. Familiarity with distributed systems and AI concepts is also beneficial.

Having experience in CUDA programming for GPU acceleration, knowledge of ML model quantization techniques, and familiarity with MLOps tools can significantly set you apart when applying for the AI Software Engineer role at webAI. Contributions to open-source ML projects and previous experience in distributed systems development are also valuable.

At webAI, our culture emphasizes core values like truth, ownership, tenacity, and humility. We encourage transparency in communication and decision-making, along with a commitment to taking responsibility for outcomes. Employees are encouraged to persist through challenges and maintain a mindset of learning and respect for one another's strengths.

webAI provides a competitive salary and performance-based incentives, along with a comprehensive benefits package that includes health, dental, and vision coverage. Additional perks include a Health and Wellness stipend, Continuing Education credits, unlimited PTO, and parental leave, among others. Our commitment to employee wellness is reflected in our flexible work schedules and support for continuous growth.

In answering this question, highlight specific projects where you've implemented ML models. Discuss your experience with different algorithms, frameworks like PyTorch, and detail any challenges you faced during deployment, emphasizing problem-solving strategies.

Explain your approach to optimization, detailing techniques like algorithm refinement, code profiling, and leveraging GPU acceleration. Give examples of how these strategies improved performance in past projects.

Discuss practices such as rigorous testing, continuous integration/deployment pipelines, and code reviews. Highlight past experiences where these practices ensured the reliability of an AI application.

Share your proficiency with both languages, mentioning specific projects where you've used them. Discuss your experience in integrating C/C++ into Python, as well as any complexities you've encountered.

Emphasize the importance of communication in teamwork. You could describe a scenario where you collaborated with research teams to gather requirements or where you had to integrate feedback into your project implementation.

Provide an overview of distributed systems principles, perhaps discussing how they relate to scalability in AI applications. Mention any relevant projects where you applied this knowledge.

Mention resources you use, such as journals, conferences, or online courses. Discuss how staying current has benefited your work, especially in understanding new algorithms or tools.

Be candid about a specific challenge, like model training or data quality issues, and explain how you addressed it. This showcases your problem-solving abilities and resilience.

Choose a project that illustrates your skills. Discuss the objectives, your role, the technologies used, and the results achieved, emphasizing the impact of your work.

Discuss the importance of data privacy in AI, touching on compliance, ethical considerations, and how you ensure that your applications respect user data, citing any relevant regulations or best practices.