Senior Software Engineer

As a Senior Software Engineer (SSE-II) at Weekday's clients, you will lead complex backend projects, focusing on performance enhancements and large-scale migrations. You'll design and develop backend solutions for video processing and AI-powered content generation. Your role will also involve refining algorithms for clip relevancy, collaborating with product and data science teams to integrate AI-driven features, and mentoring junior engineers.

To qualify for the Senior Software Engineer position at Weekday's clients, candidates should have a Bachelor's or Master's degree in Computer Science or a related field, along with 7+ years of experience in backend development. Strong expertise in backend technologies, API development, AI, and machine learning is also necessary, especially knowledge of large language models (LLMs), performance optimization, and debugging.

In the Senior Software Engineer role at Weekday's clients, you will contribute to AI-driven content generation by designing backend solutions that integrate advanced algorithms for video processing and human-like content production. This includes optimizing LLMs and ensuring efficient prompt selection to enhance content relevancy and quality.

Preferred qualifications for the Senior Software Engineer role at Weekday's clients include familiarity with video processing technologies like ffmpeg, experience with cloud platforms such as AWS, Azure, or GCP, and knowledge of CI/CD pipelines. Having prior experience working with machine learning, artificial intelligence, and relevance teams is also beneficial.

The work environment for a Senior Software Engineer at Weekday's clients is collaborative and engaging, emphasizing teamwork and creativity. You will work closely with product and data science teams, engage in mentorship opportunities, and take part in rapid experimentation, making it a dynamic setting where your contributions directly influence innovative projects.

In answering this question, share specific projects or systems you’ve worked on, your role in designing APIs, and the technologies you used. Highlight your understanding of best practices in API design, including REST or GraphQL principles, and any impact your contributions had on the project’s success.

When responding, discuss concrete techniques you've used for performance optimization, such as profiling, reducing latency, or improving throughput. Explain how you analyze bottlenecks and the methods you’ve applied to optimize algorithms specifically for AI content generation or video processing.

In your answer, provide a brief overview of your experience working with AI and ML, focusing on any specific technologies or frameworks you've used, such as TensorFlow or PyTorch. Discuss how you’ve applied machine learning techniques in backend development, particularly in leveraging large language models.

Explain your approach to collaboration, emphasizing communication tools and strategies you’ve used to ensure teams are aligned. Share an example of a successful collaboration that resulted in enhanced features or product outcomes, demonstrating your teamwork skills and ability to innovate.

Choose a specific project that showcases your leadership skills and problem-solving abilities. Describe the challenges faced, your approach to overcoming them, the technologies used, and the outcomes, illustrating your capacity to manage complexities in software engineering.

Discuss methods you employ to ensure code quality, such as conducting thorough code reviews, writing unit tests, and adhering to coding standards. Provide examples of how you’ve implemented these strategies in past projects to maintain high-quality output.

In your response, detail your hands-on experience with deploying applications on cloud platforms. Highlight specific services you’ve used, such as serverless functions or storage solutions, and how these technologies contributed to the scalability and reliability of the projects.

Share insights into your understanding of MLOps, focusing on how you’ve utilized it in the deployment and evaluation of large language models. Describe any frameworks, tools, or processes you've implemented in your workflow to ensure smooth MLOps practices.

When addressing this question, showcase your mentoring philosophy and techniques. Discuss how you provide guidance, encouragement, and constructive feedback, as well as specific instances where your mentorship has led to significant growth for junior team members.

Your answer should reflect genuine interest in the company's projects and values. Share how their mission resonates with your career goals, what excites you about the technology stack they use, and how you believe you can contribute to their innovative solutions.