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Machine Learning Engineer

Join us at Diagnostic Robotics, where we empower healthcare providers, payers, and value-based care teams with unprecedented insights and automation. Our futuristic tools predict adverse health events and help care teams avoid them, all while streamlining workflows, easing administrative burdens, and automating mundane tasks. With systems trained on tens of billions of data points from claims and other data combined with decades of research-based clinical expertise, we exist to reduce healthcare costs, make care teams more effective, and save lives.

What you’ll be doing:

You’ll work and collaborate closely with the team’s product manager, data engineers, full stack developers, medical physicians, client-facing teams, and directly with clients. You will be the E2E owner of our data science and machine learning products. This includes maintaining and improving our ML pipelines, driving data-driven insights, and building new AI and analytics products.

You'll be taking ownership of your projects from ideation to productization.You’ll be expected to contribute not just as a technical expert, but as a partner to the product team, bringing your deep understanding of data to help shape business strategy and deliver valuable solutions.

We strongly emphasize autonomy, and default to hiring Managers of One (https://signalvnoise.com/posts/1430-hire-managers-of-one). You’ll also be expected to help other engineers succeed.

As you join our core team, you will have the opportunity to revolutionize every facet of our dev organization. From igniting a vibrant culture to shaping the very projects we undertake and revolutionizing our approaches, your influence will be immense.

If you want to dive deeper into what being an engineer in our engineering team means, let’s talk. 

Key Responsibilities:

  • End-to-End ML Product Development: Design, develop, deploy and maintain ML solutions from concept to production. You’ll be responsible for end-to-end ownership, including data preparation, feature engineering, model selection, training, and deployment.
  • ML Pipeline Development: Build scalable and reliable ML pipelines using orchestration tools, you’ll manage the automation of model training, evaluation, and deployment.
  • Data Analysis and Insights: Perform data analysis tasks utilizing SQL and pandas to work with large datasets and generate key findings for stakeholders.
  • Collaboration with the Product Team: Work closely with cross-functional teams, including product managers, engineers, medical physicians, and business leaders, to define features and products, and deliver solutions that align with business goals.
  • Software Engineering Best Practices: Apply strong software engineering principles in your work, ensuring that the code is clean, maintainable, and follows industry best practices. Participate in code reviews, testing, and continuous integration.
  • Knowledge Sharing and Mentoring: Act as the data expert in the company, helping others to understand the power of data science and machine learning and mentoring team members as needed.
  • Stay Current with Emerging Technologies and Healthcare initiatives: Leverage new advancements in AI/ML, with a focus on generative AI, to ensure our solutions remain cutting-edge.



Key Requirements:

  • Experience:
    • At least 6 years of software development experience (either in machine learning engineering or other software engineering disciplines).
    • At least 3 years of experience with machine learning engineering, with a proven track record of deploying ML models to production and working with large-scale datasets.
  • Technical Skills:
    • Strong experience building ML pipelines using tools such as Argo Workflow, Airflow, or similar.
    • Proficiency in SQL for data extraction and analysis. Experience with other data processing tools (e.g. Pandas, Dask, PySpark) is a plus.
    • Solid experience with machine learning frameworks (e.g. Scikit-learn TensorFlow, PyTorch).
    • Familiarity with cloud platforms (preferably AWS) and containerization technologies (Docker, Kubernetes).
    • Strong software engineering skills, including version control (Git), testing, and CI/CD practices.
    • Bonus if you have experience with Generative AI (e.g., GPT models, transformers, diffusion models) or other advanced AI techniques.
  • Soft Skills:
    • Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
    • Passion for healthcare and business: You should be driven to understand the business needs of the company and how data science can add value.
    • A growth mindset with a desire to continuously learn and stay updated with the latest trends in AI machine learning and healthcare.

Bonus Points:

  • Experience with cloud-native ML frameworks and deployment.
  • Familiarity with healthcare data (e.g. HIPAA, HL7, FHIR).
  • Experience with MLOps principles and tools for model monitoring, retraining, and versioning.

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What You Should Know About Machine Learning Engineer, Diagnostic Robotics

Join us at Diagnostic Robotics as a Machine Learning Engineer, where we empower healthcare providers, payers, and teams with unprecedented insights and automation. In this exciting role, you’ll collaborate closely with our product manager, data engineers, developers, and even medical professionals, to ensure we’re making the best use of our incredible data and AI capabilities. You’ll own the end-to-end development of our machine learning products, meaning you’ll take them from the initial idea all the way through to productization. Your day-to-day will involve improving our ML pipelines, generating data-driven insights, and developing new AI products. Here at Diagnostic Robotics, we not only value technical expertise but also your ability to partner with our product teams to help shape our strategies and deliver awesome solutions. We encourage autonomy and a mindset of being a Manager of One, where your influence can revolutionize our development organization. Plus, you’ll have the chance to mentor others as you share your deep knowledge of machine learning and data science. With your skills, you can help us to innovate and refine our approach in the fast-paced healthcare landscape. If you’re passionate about healthcare and eager to dive into the world of AI and ML, we would love to connect and discuss what being a Machine Learning Engineer with us truly means!

Frequently Asked Questions (FAQs) for Machine Learning Engineer Role at Diagnostic Robotics
What are the key responsibilities of a Machine Learning Engineer at Diagnostic Robotics?

As a Machine Learning Engineer at Diagnostic Robotics, you’ll be responsible for end-to-end ML product development, including designing, developing, deploying, and maintaining machine learning solutions. You will build scalable ML pipelines, perform data analysis using SQL and pandas, work closely with cross-functional teams, and ensure software engineering best practices are followed. Additionally, your role includes mentoring others and staying current with emerging technologies in AI and healthcare.

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What qualifications are required for the Machine Learning Engineer position at Diagnostic Robotics?

Candidates for the Machine Learning Engineer role at Diagnostic Robotics should have at least 6 years of software development experience and 3 years specifically in machine learning engineering. Familiarity with ML frameworks like Scikit-learn, TensorFlow, or PyTorch, along with experience in building ML pipelines using tools such as Airflow, is essential. Proficiency in SQL and cloud platforms, mainly AWS, is preferred, and a solid foundation in software engineering practices is required.

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How does a Machine Learning Engineer contribute to healthcare at Diagnostic Robotics?

At Diagnostic Robotics, a Machine Learning Engineer plays a vital role in powering healthcare insights and automating workflows, ultimately leading to better patient outcomes. By developing AI solutions that predict adverse health events, the Engineer directly supports healthcare providers in avoiding these events, thus reducing costs and improving care team efficiency. This impactful work contributes to the overall mission of saving lives and transforming healthcare delivery.

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What soft skills are essential for the Machine Learning Engineer role at Diagnostic Robotics?

For the Machine Learning Engineer role at Diagnostic Robotics, strong communication skills are crucial as you'll need to explain intricate technical concepts to non-technical stakeholders. A passion for healthcare, a growth mindset, and the desire to understand business needs are also important. These soft skills complement your technical expertise to ensure that your contributions effectively align with company goals.

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What opportunities are available for growth and mentorship as a Machine Learning Engineer at Diagnostic Robotics?

As a Machine Learning Engineer at Diagnostic Robotics, you'll have numerous opportunities for growth and mentorship. The company emphasizes knowledge sharing, allowing you to mentor less experienced team members and share your expertise in data science and ML. Additionally, with a vibrant culture and the autonomy to influence projects, you can drive your career forward while contributing to the company’s innovative mission.

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Common Interview Questions for Machine Learning Engineer
What is your experience with deploying machine learning models in production?

In your response, highlight any past projects where you deployed ML models, discussing the frameworks and tools you used. Provide specifics on the dataset size, challenges faced during deployment, and how you ensured model performance post-launch.

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Can you explain your process for building ML pipelines?

Outline the steps you typically follow when building ML pipelines, including data collection, preprocessing, feature engineering, model selection, and deployment. Emphasize any relevant tools you've used, such as Airflow or Argo Workflow, to automate these processes.

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How do you ensure the accuracy and reliability of your machine learning models?

Discuss the validation techniques you employ, such as cross-validation or A/B testing. Share any metrics you monitor, like precision, recall, or F1 score, and how you handle model retraining or refinement in production.

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What role do you think collaboration plays in successful machine learning projects?

Emphasize your belief that collaboration is key in ML projects. Share experiences where working with team members from different backgrounds (e.g., product managers, engineers, healthcare professionals) led to better outcomes and innovative solutions.

Join Rise to see the full answer
Describe your experience with data analysis and working with large datasets.

Share specific examples of projects where you analyzed large datasets, detailing the tools you used, such as SQL or Pandas. Discuss how your analysis informed decision-making and contributed to project success.

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What emerging technologies or trends in machine learning excite you the most?

Discuss current trends in AI and ML that you're passionate about, such as generative AI or advancements in healthcare algorithms. Share how you keep your skills current and any relevant projects you've worked on.

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How do you approach debugging machine learning models?

Explain your systematic approach to debugging models, including reviewing data pipelines, analyzing feature importance, and testing various algorithms. Provide an example of a particularly challenging bug and how you resolved it.

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Can you discuss a project where you contributed to business strategy through data?

Share a specific project where your data insights directly influenced business decisions. Explain the context, your analysis process, and the outcome, showcasing your ability to align technical work with business goals.

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What are your thoughts on best practices in software engineering as they relate to machine learning?

Discuss the importance of clean, maintainable code, robust documentation, and version control. Highlight any testing practices you follow to ensure model reliability and how you incorporate CI/CD into your workflow.

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How do you prioritize tasks when working on multiple projects?

Share your strategies for prioritizing multiple projects, such as assessing project impact, deadlines, and resource availability. Discuss tools or methodologies you use, such as Agile or Kanban, to manage your workload effectively.

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At Diagnostic Robotics, we imagine a world where clinicians, consumers, and payers have access to all the information they need to make smarter, more cost-effective decisions about patient care. Our intuitive, user-friendly diagnostic solutions le...

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BENEFITS & PERKS
Dental Insurance
Vision Insurance
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Paid Holidays
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Full-time, remote
DATE POSTED
December 24, 2024

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