Staff Backend Software Engineer - India

As a Staff Backend Software Engineer at Fiddler, your primary responsibilities will include designing and building scalable cloud platforms to monitor and analyze machine learning models, developing enterprise-grade APIs, and creating new metrics to evaluate model quality. Additionally, collaborating with product teams and enhancing existing software functionalities will also be key aspects of your role.

To qualify for the Staff Backend Software Engineer role at Fiddler, candidates should have a Master's or Bachelor's Degree in Computer Science or a related field, along with over 10 years of relevant experience in software engineering. Proficiency in Python and experience with ML models in production are essential. Familiarity with distributed systems, such as Redis or RabbitMQ, is also recommended.

Candidates for the Staff Backend Software Engineer position at Fiddler should be well-versed in Python, with experience working with ML frameworks like HuggingFace and various distributed systems infrastructures such as PostgreSQL and Celery. Knowledge of cloud services such as AWS or Google Cloud, as well as Kubernetes, would be a great advantage.

Fiddler offers a unique working environment where innovation meets ethics in AI. As a rapidly growing startup, you will be surrounded by a team of intelligent and empathetic individuals dedicated to making a real impact. The learning opportunities are immense, as you will be at the forefront of developing tools that ensure safe and responsible AI solutions.

The Staff Backend Software Engineer at Fiddler plays a critical role in shaping the future of AI technology by creating tools that enable organizations to monitor and analyze their machine learning models. This impact is pivotal in building trust in AI systems and ensuring they operate transparently and ethically.

In your response, focus on specific projects where you have implemented machine learning models, discussing the technologies utilized, the challenges faced, and how you ensured their performance and robustness. Highlight any quantifiable outcomes if possible.

Discuss your methodical approach, including how you gather logs, analyze performance metrics, and utilize monitoring tools. Consider mentioning any specific examples where your troubleshooting led to significant improvements.

Explain your process for API design, mentioning best practices like versioning, documentation, and testing. Also, highlight your experience in user feedback integration to make ongoing improvements.

Share examples of past collaborations with product managers or data scientists, detailing how effective communication and understanding each team's needs led to the successful launch of products or features.

Choose a specific project and describe the complexity involved. Discuss the technologies leveraged, the specific challenges faced, and how you overcame them to achieve project goals.

Identify tools you have used in the past for monitoring model performance, such as Prometheus or Grafana, and discuss how they helped you ensure the reliability of AI systems.

Talk about your commitment to continuous learning—attending conferences, participating in online forums, or following industry leaders. Emphasize how this knowledge directly benefits your work.

Outline your coding standards, discussing aspects like clarity, modularity, and use of comments. You might connect how these practices lead to a more collaborative engineering environment.

Recount a specific incident where you identified areas for improvement in existing processes or practices, detailing the steps taken to implement changes and the positive outcomes that resulted.

Give examples of how you simplify concepts, using analogies or visual aids. Emphasize the importance of tailoring your communication style to the audience, ensuring that they grasp essential points without getting lost in jargon.