Machine Learning Engineer

As a Machine Learning Engineer at Stream in San Francisco, you will be responsible for designing and implementing machine learning solutions to process and extract insights from a vast array of medical documents. Your role will also involve integrating these models into our existing backend infrastructure and continuously staying current with advancements in the machine learning and AI sectors to optimize our product offerings.

To apply for the Machine Learning Engineer role at Stream, candidates should have a solid background in machine learning, ideally with experience in model deployment and production. Proficiency in programming languages like Python, familiarity with AWS services, and an understanding of data storage solutions such as DynamoDB and Redshift are also essential. A passion for healthcare technology and a desire to work in a fast-paced startup environment are key attributes we're looking for.

As a Machine Learning Engineer at Stream, you will work with an innovative tech stack that includes serverless computing on AWS using Lambdas and Fargate, as well as CI/CD pipelines utilizing CDK and GitHub Actions. You’ll also engage with DynamoDB and Redshift for data storage, and work on web applications using Svelte (TypeScript) and SST, along with exploring various large language models and traditional AI models to enhance our offerings.

The working environment for a Machine Learning Engineer at Stream is collaborative and supportive, fostering a balance between hard work and fun. Located in San Francisco's Mission district, we operate in-person for four days a week, allowing for team bonding and idea sharing. Our team is comprised of passionate individuals in their 30s who value work-life balance while being fully committed to delivering outstanding results.

As a Machine Learning Engineer at Stream, you can expect a comprehensive benefits package that includes health, dental, and vision insurance, a 401k plan, and daily lunches provided to the team. Additionally, we enjoy monthly outings that promote team building and a vibrant work culture, alongside the opportunity to work with bright and curious minds dedicated to driving innovation in the healthcare technology space.

When explaining your process for developing machine learning models, outline the steps you typically follow—beginning with problem identification, gathering and preprocessing data, selecting appropriate models, training and validating those models, and finally deploying them to production. Mention any specific techniques or tools you favor, ensuring to relate your experience to the environment and challenges faced by Stream.

To stay updated with advancements in machine learning, I regularly read research papers, follow key thought leaders in the field on social media, and engage in online courses or webinars. Additionally, participating in machine learning conferences and forums allows me to network with other professionals and gain insights into emerging technologies that could benefit a company like Stream.

When discussing your experience with deploying machine learning models in production, provide a detailed example of a project where you successfully took a model from development to production. Talk about the tools and frameworks you used, the challenges you faced, and how you ensured the model performed as expected post-deployment. Relate this back to the specific workflows used at Stream to show your compatibility.

To ensure the accuracy of my machine learning models, I employ techniques such as cross-validation, hyperparameter tuning, and using metrics specific to the model and problem at hand. I ensure robust validation by testing the model against a separate dataset and continuously monitoring its performance in production, which is crucial for a role at Stream where accuracy in medical document management is vital.

When explaining a challenging machine learning problem, choose one that showcases your problem-solving skills. Outline the context, the steps you took to address it, and the eventual outcome. Highlight elements such as teamwork or interdisciplinary collaboration, particularly those involving areas relevant to Stream's mission in the healthcare sector, to reinforce your fit for the company.

In handling missing or corrupted data, I assess the impact of the missing values on my analysis, utilizing techniques such as imputation, data transformation, and cleaning processes to address these issues. Discussing specific tools or libraries you used can add depth to your answer, showing how you can maintain data integrity in critical applications like those at Stream.

My experience with AWS includes deploying machine learning models using services like Lambda and Fargate, which are also part of Stream's tech stack. I am adept at leveraging AWS's scalability and reliability to improve deployment workflows and ensure that our machine learning solutions can handle the scale of medical document processing effectively.

When prioritizing tasks across multiple projects, I assess deadlines, project scope, and impact. I utilize project management tools and methodologies like Agile to ensure that my work is aligned with company goals. At Stream, where agility is crucial, this approach enables me to manage time effectively and contribute significantly to our machine learning initiatives.

Collaborating with non-technical teams requires clear communication and understanding of their perspectives. I ensure to explain technical concepts in layman’s terms, focusing on how technical solutions can address their challenges. Sharing regular updates and inviting feedback encourages collaboration, which is essential at Stream where cross-functional teamwork is key to our success.

The biggest challenges facing machine learning in healthcare include data privacy concerns, the need for high accuracy, and the integration of complex healthcare data sources. Addressing these challenges often requires innovative thinking and a robust strategy, making it essential for a Machine Learning Engineer at Stream to be aware of these issues and propose solutions that align with our mission.