Founding ML Engineer

As a Founding ML Engineer at Operator, you'll be entrusted with designing and training multi-step workflows and refining existing ML models. Your key responsibilities will include building low-latency inference infrastructures and developing benchmarks to better understand our data and model performance. Furthermore, you'll engage closely with customers to align our products with their needs, ensuring that we leverage your expertise to craft innovative solutions.

To qualify for the Founding ML Engineer position at Operator, you should have a minimum of 3 years of experience in deep learning and at least 5 years of professional experience overall. Strong Python programming skills are essential, along with proficiency in ML frameworks like PyTorch or TensorFlow. Having a strong grasp on the latest advancements in LLMs and experience in building and deploying ML infrastructure is also vital.

At Operator, you'll have the unique opportunity to be part of the founding team and contribute directly to the company’s trajectory. We prioritize cultivating a positive, driven, and ambitious work culture, where your ideas and talents will be valued. Additionally, solving complex technological challenges alongside an experienced team from top-tier companies creates a rich learning environment for you to grow and innovate.

We're seeking candidates who exhibit a high degree of autonomy and self-direction. Excellent written and verbal communication skills are essential, as we prioritize collaboration and sharing ideas. We value individuals who can balance a long-term perspective with daily actionable goals and who possess a history of technical excellence—whether through professional roles or personal projects.

A Founding ML Engineer at Operator should have experience with ML frameworks like PyTorch and TensorFlow, as well as familiarity with state-of-the-art LLMs. Knowledge of building and running ML infrastructure is critical, including experience with vector databases and embedding models. Any experience in training generative models or ASR and TTS models would be advantageous.

When discussing your experience with deep learning frameworks, be specific about the projects you've worked on using PyTorch or TensorFlow. Highlight your role, the challenges you faced, and how you overcame them. Demonstrating your hands-on experience with these tools will illustrate your technical expertise to the interviewers.

In response to this question, describe your methodical approach to fine-tuning models, including data validation, identifying performance metrics, and making adjustments based on feedback. Providing a real-life example can strengthen your answer and showcase your problem-solving abilities.

Discuss the key performance metrics you focus on, such as accuracy, precision, and recall. Explain how you set up benchmark tests, the tools you use for tracking performance, and how you interpret the results to inform your iterative model improvements.

Share a relevant project where you took an ML model from inception through deployment. Focus on your contributions, the steps involved, and the learning outcomes, showcasing your adaptability and innovative mindset in the process.

To address this, outline your strategies for engaging with customers, such as regular check-ins, gathering feedback, and incorporating insights into product iterations. Highlight your belief in the importance of customer collaboration for building successful products.

In your answer, share your insights on LLM advances, their applications, and potential limitations. Demonstrating a forward-thinking mindset and awareness of industry trends will resonate well with interviewers.

Discuss the resources you turn to for staying informed, whether it's through academic papers, conferences, or online forums. This shows your commitment to continuous learning and adapting to new technologies.

When addressing this question, provide a clear example of a significant technical challenge and detail your approach to solving it. Focus on the techniques and tools used, your thought process, and the outcome to effectively demonstrate your problem-solving skills.

Discuss your framework for task prioritization, emphasizing your use of goals, timelines, and feedback loops to make informed decisions. Sharing your approach will illustrate your organizational skills and strategic mindset.

Express your enthusiasm for the unique opportunity to be part of the founding team and your eagerness to tackle significant challenges. Highlight how your values align with the company's mission and culture, which will show your genuine interest in joining Operator.