AI Engineer

As an AI Engineer at Amplifier Health, your primary responsibilities will include collaborating with AI Researchers to train and implement Large Acoustic Models (LAMs) using extensive datasets, developing and maintaining efficient data pipelines, optimizing model training processes, and deploying models into production environments. You will also work closely with our software engineering team to integrate AI models into our healthcare platform.

To be considered for the AI Engineer role at Amplifier Health, you should hold a Master’s degree in Computer Science, Engineering, or a related field. You need strong software engineering skills, particularly in Python or similar languages, and experience with deep learning frameworks like TensorFlow or PyTorch. Familiarity with cloud computing platforms (AWS, Azure, GCP) and MLOps practices is also essential.

In addition to the required qualifications, beneficial skills for the AI Engineer position at Amplifier Health include experience with audio and speech processing technologies, familiarity with containerization technologies like Docker and Kubernetes, and contributions to open-source projects or machine learning competitions. Being an innovative problem solver who can thrive in a collaboration-focused environment will also be valuable.

As an AI Engineer at Amplifier Health, you will work on impactful projects that leverage voice-based diagnostics to improve healthcare outcomes. You'll tackle real-world challenges in developing AI technology that transforms how we diagnose and manage diseases, making healthcare more accessible and effective for everyone.

Working as an AI Engineer at Amplifier Health offers numerous benefits, including being part of a mission-driven company making significant advancements in healthcare technology. You'll enjoy competitive compensation, a supportive and collaborative work environment, opportunities for professional growth, and the chance to work with a talented team on transformative projects.

When answering this question, highlight specific projects where you used TensorFlow or PyTorch, including the challenges faced, how you approached them, and what results were achieved. Discuss your understanding of model training, tuning hyperparameters, and ensuring performance scalability.

In your response, mention techniques you have used to optimize training, such as batch normalization, dropout, or data augmentation. Explain your approach to experimenting with different architectures and hyperparameters to achieve the best performance while reducing computational costs.

Provide examples of data pipelines you've built, focusing on your experience with data ingestion, processing, and augmentation. Discuss the tools and frameworks you used to ensure efficiency and how these pipelines supported model training and deployment.

Emphasize your ability to work in a team by discussing a project that involved collaboration between AI researchers, software engineers, and potentially other stakeholders. Highlight your role in facilitating communication and ensuring successful integration of AI solutions into platforms.

Share the resources and methods you utilize to stay updated on advancements in AI, such as attending conferences, participating in online courses, contributing to open-source projects, or engaging in relevant online communities. This shows your commitment to continuous learning.

Discuss your systematic approach to debugging, which could include monitoring model performance metrics, analyzing training data quality, and testing various configurations on validation datasets. Highlight specific instances where you successfully resolved issues.

Explain your experience with cloud services like AWS, Azure, or GCP, particularly in deploying, scaling, and managing AI models. Mention any specific services you've used for model training, storage, or MLOps and how they contributed to project success.

Describe the steps you follow from model validation to deployment, focusing on aspects like ensuring scalability, reliability, and monitoring after deployment. Mention any tools or methodologies you've used to facilitate smooth transitions from development to production.

Talk about a specific challenge, how you addressed it, and what you learned from the experience. This reflects your problem-solving abilities and resilience in the face of complex AI-related issues.

Express your passion for healthcare innovation and how your skills align with Amplifier Health’s mission. Highlight any previous experiences or projects that fuse AI with real-world health solutions and how they inspire your desire to contribute to the team.