Senior Engineer - AI (Remote)

As a Senior Engineer - AI at Pair Team, you'll lead the charge in developing AI-powered features that solve real-world healthcare challenges. Your role includes rapid prototyping, testing, and launching AI-driven solutions, optimizing learning processes, and collaborating with cross-functional teams to improve patient outcomes.

To qualify for the Senior Engineer - AI role at Pair Team, you'll need at least 2 years of experience in a high-performing tech environment, familiarity with modern AI tools, and a proven track record in delivering AI-driven features. A creative problem-solving mindset and the ability to work across the tech stack are also essential.

Pair Team is committed to your professional development. As a Senior Engineer - AI, you'll have opportunities for rapid career progression, mentorship roles, and the chance to work on innovative projects that push the boundaries of healthcare technology.

At Pair Team, we foster a collaborative and inclusive culture that values integrity and challenges the status quo. You’ll be part of a diverse team working collectively to improve healthcare outcomes through AI, with ample support for your personal and professional growth.

In your role as Senior Engineer - AI at Pair Team, you will engage with a variety of modern AI tools and frameworks, such as OpenAI and Llama, while also potentially utilizing Ruby on Rails or similar backend technologies to create impactful AI solutions.

When responding to this question, highlight specific projects where you've used AI models like LLMs or frameworks such as OpenAI. Describe the challenges you faced and the impact of your solutions, showcasing your expertise in applying AI in practical scenarios.

Discuss your workflow for rapid prototyping, including methods you use to validate assumptions quickly. Emphasize your experience with iterative development and how you utilize feedback loops to refine AI features that meet user needs effectively.

Talk about the quality gates and evaluation pipelines you implement to monitor AI performance. Include examples of metrics you track and how you make decisions to improve the reliability of your AI features, particularly in sensitive healthcare contexts.

Share instances where you've successfully collaborated with product, operations, and clinical teams. Highlight how you aligned technical outcomes with business needs, ensuring that AI initiatives supported overall patient care goals.

Offer a detailed account of a challenging situation where your problem-solving skills shone through. Outline the problem, your approach to crafting a solution, and the resulting impact, stressing both the speed and quality of your work.

Illustrate your methods for addressing ambiguity, such as breaking down large problems into manageable parts and iterating quickly. Explain how you maintain a balance between speed and quality while navigating uncertain environments.

Discuss your approach to mentoring, including setting clear expectations, providing constructive feedback, and promoting a culture of learning and collaboration. Share specific examples of how you’ve helped develop skills within your team.

Highlight your organizational skills and decision-making processes. Talk about frameworks you use to evaluate priorities based on project deadlines, stakeholder needs, and the potential impact of your work in the healthcare AI domain.

Provide specific examples of AI features that you've built, detailing the problem they addressed, the technology stack utilized, and the outcomes achieved. This demonstrates your practical experience and ability to create meaningful solutions.

Explain how you've effectively managed and processed large datasets, including your techniques for ensuring data quality and integrity. Discuss any specific challenges you've encountered and how you overcame them in your AI development projects.