Staff Machine Learning Engineer, Instruction-tuning & Alignment

As a Staff Machine Learning Engineer at Scribe, you'll be at the forefront of developing and deploying AI-powered software solutions. Key responsibilities include exploring advanced techniques in artificial intelligence, fine-tuning large language models, optimizing multimodal product research, and collaborating with product teams to enhance user experiences. Your work will directly impact how our millions of users interact with Scribe's software.

To qualify for the Staff Machine Learning Engineer position at Scribe, candidates should possess a degree from a top machine learning program and have a minimum of 5 years of industry experience dealing with complex ML challenges. Strong programming skills in Python and experience with instruction-based finetuning and deploying LLMs are essential. Communication and project ownership skills are also vital to ensure success in this collaborative environment.

At Scribe, we prioritize the growth and development of our team members. Staff Machine Learning Engineers benefit from a culture of continuous learning, engaging in cutting-edge research, and the opportunity to work on impactful projects. Our environment encourages sharing knowledge and collaborating with highly skilled teammates, allowing you to master your craft while contributing to our mission of enhancing user experiences.

The compensation for the Staff Machine Learning Engineer position at Scribe ranges from $190,000 to $250,000 USD, complemented by competitive equity options and comprehensive benefits. Factors such as your experience, location, and individual qualifications influence the final compensation package, reflecting the value we place on our team members at Scribe.

While startup experience is not a strict requirement for the Staff Machine Learning Engineer role at Scribe, it can be extremely beneficial. Candidates who have previously worked in fast-paced environments often excel in our culture, which emphasizes agility and innovation. Familiarity with startup dynamics can enhance your ability to contribute effectively to our growing team.

When answering this question, provide specific examples of models you've fine-tuned and the techniques you used. Be sure to mention any metrics or outcomes that illustrate the success of your work, and discuss how you approached solving challenges during the process.

Discuss your approach to model optimization, such as hyperparameter tuning, data preprocessing techniques, or utilizing certain frameworks. Illustrate your methods with examples from past projects, focusing on measurable performance improvements that resulted from your strategies.

When discussing your project prioritization, highlight your ability to set clear objectives based on user impact, urgency, and collaboration with team members. Provide a specific example of a time when you had to manage multiple tasks effectively and how your prioritization led to successful outcomes.

Choose a particular challenge that showcases your technical skills and problem-solving abilities. Narrate the context, your approach to addressing the issue, and the results of your solution, emphasizing what you learned in the process.

Explain your belief in the importance of collaboration by sharing examples of how working closely with cross-functional teams has enhanced your projects. Highlight specific instances where teamwork led to innovative solutions and how you foster a collaborative atmosphere.

Discuss your approach to simplifying complex technical concepts and tailoring your communication style to suit your audience. Provide examples of how you've successfully conveyed findings, insights, or project updates in a clear and relatable manner to non-technical stakeholders.

Share your methods for keeping up with the field, such as following industry publications, participating in online courses, or attending conferences. Discuss specific trends or technologies that have recently caught your attention and explain how you’re incorporating new knowledge into your work.

Give a detailed account of a project where you implemented instruction-based finetuning. Describe the context, methods used, and the impact of this approach on model performance, ensuring you highlight any challenges encountered and how you overcame them.

Discuss your exposure to RLHF, mentioning any specific projects where you implemented it or techniques you explored. Focus on the outcomes and insights gained from these experiences, as well as your understanding of when RLHF is most beneficial in machine learning applications.

Answer this question by discussing your approach to balancing accuracy with real-world constraints. Provide examples of how you’ve navigated these trade-offs in past projects, emphasizing your understanding of the importance of robust and efficient models in production settings.