Senior Software Engineer, AI/ML

As a Senior Software Engineer, AI/ML at Striim, you will be responsible for designing, developing, testing, and maintaining scalable data streaming solutions. This involves integrating AI components and collaborating with cross-functional teams to incorporate AI-driven features into the streaming engine. You’ll work alongside data scientists to develop solutions that improve efficiency and accuracy in data processing, lead projects, mentor junior engineers, and ensure the seamless operation of our advanced platform.

To be considered for the Senior Software Engineer, AI/ML role at Striim, candidates should have a minimum of 4 years of professional experience integrating AI/ML models into software applications, particularly in real-time data processing environments. A Bachelor's degree in Computer Science or a related field is required, along with proven experience with AI frameworks like TensorFlow or PyTorch, excellent communication skills, and the ability to mentor junior engineers.

Striim offers a dynamic range of benefits for its Senior Software Engineer, AI/ML employees, including a competitive salary, pre-IPO stock options, comprehensive health care plans, and generous paid time off. Additionally, you’ll be part of an innovative culture that promotes collaboration, continuous learning, and a healthy work-life balance, all within our vibrant Palo Alto office.

Collaboration is at the heart of Striim's culture for the Senior Software Engineer, AI/ML role. You will collaborate with passionate engineers and diverse teams, both in-person in our Palo Alto office and remotely, to design and implement cutting-edge solutions. This hybrid approach ensures that innovative ideas thrive as we work together to enhance our platform's capabilities and deliver exceptional value to our customers.

The work environment at Striim for the Senior Software Engineer, AI/ML is dynamic and collaborative, emphasizing teamwork and innovation. As part of a Silicon Valley startup, you can expect a culture that values creativity and encourages employees to grow. The vibrant office in Palo Alto supports in-person collaboration, while a hybrid work model provides flexibility, enabling you to engage with colleagues effectively across various locations.

When preparing your answer, emphasize your role, specific challenges faced, and how you overcame them while integrating AI/ML solutions. Highlight any frameworks you used, the impact of your work on the project’s success, and your collaboration with other team members.

Discuss your previous projects involving real-time data processing, emphasizing the technologies and methodologies employed. Provide examples of how you tackled challenges and improved the efficiency of data streams, possibly citing specific algorithms or tools you used.

Explain your approach to optimizing distributed systems, including your understanding of latency, load balancing, and fault tolerance. Share past experiences where your strategies led to noticeable performance improvements.

Detail your mentoring philosophy and practical steps you would take to guide a junior engineer in using AI frameworks, including hands-on sessions, code reviews, and fostering an open dialogue for questions and growth.

Share specific tools and technologies you have used for monitoring and troubleshooting in software environments. Discuss the benefits of these tools and how they helped improve system reliability and issue resolution.

Describe your strategies for clear communication with non-technical stakeholders, including simplifying technical jargon, using visual aids, and ensuring mutual understanding of project goals and timelines.

Provide an example of a challenge in model deployment, detailing the issue, your problem-solving approach, and the outcome. Emphasize lessons learned and how you would apply these in future projects.

Discuss techniques you’ve used such as hyperparameter tuning, model selection and versioning, as well as evaluating model performance metrics, emphasizing your analytical approach to model optimization.

Share your methods for staying informed about AI/ML advancements, such as attending conferences, participating in webinars, engaging in online communities, and continuous learning through courses or certifications.

Reflect on your passion for innovation, collaboration, and the dynamic nature of startup culture. Discuss how this environment encourages creative problem-solving and aligns with your professional aspirations.