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Machine Learning Systems Engineer (Staff/Senior) - job 1 of 4

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.

Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.

We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense.

The Role

As an ML Systems Engineer at Abridge, you will be responsible for scaling and deploying machine learning models to handle increasing traffic demands and integrating them with various platforms. You'll play a pivotal role in building a scalable infrastructure that not only supports current deployments but also lays the foundation for long-term growth. Your role will be critical in ensuring our AI-driven healthcare platform is powered by robust, scalable, and efficiently deployed models.

What You'll Do

  • Architect, design, and implement ML software systems for deploying and managing models at scale.

  • Stand up ML models for inference, starting with critical models like the 'linkages' model, and ensure they are capable of handling traffic increases.

  • Develop and maintain infrastructure that supports efficient ML operations, including model evaluations, deployments, and training at scale.

  • Collaborate closely with ML researchers, engineers, and cross-functional teams to ensure seamless integration of models with services like Zoom and Athena.

  • Work with stakeholders across machine learning and operations teams to iterate on systems design and implementation.

  • Optimize and maintain the performance of ML systems to ensure high availability, fault tolerance, and smooth scalability.

  • Troubleshoot production issues and continuously improve systems to enhance performance and efficiency.

What You'll Bring

  • 5+ years of experience in ML model deployment and scaling, with a focus on production-quality software

  • Strong proficiency in Python and Kubernetes, with experience building scalable ML infrastructure

  • Expertise in designing fault-tolerant, highly available systems.

  • Experience working with cloud environments, Infrastructure as Code (IaC), and managing deployments using Kubernetes.

  • Proficiency in optimizing system performance, debugging production issues, and designing systems for scalability and security.

  • Experience in software design and architecture for highly available machine learning systems for use cases like inference, evaluation, and experimentation

  • Excellent understanding of low-level operating systems concepts, including multi-threading, memory management, networking and storage, performance, and scale

  • Bachelor's/Master’s Degree or greater in Computer Science/Engineering, Statistics, Mathematics, or equivalent

  • Excellent interpersonal and written communication skills

Ideally, You Have

  • Experience with large-scale ML platforms like Ray, Databricks, or AnyScale

  • Expertise with ML toolchains such as PyTorch or TensorFlow

  • Proven experience working with distributed systems and handling inference at scale

  • Background in working with teams and leaders to deliver impactful ML-powered solutions in fast-paced environments

  • in machine learning toolchains and techniques, such as Pytorch or Tensorflow

  • Demonstrated experience incubating and productionizing new technology, working closely with research scientists and technical teams from idea generation through implementation

We value people who want to learn new things, and we know that great team members might not perfectly match a job description. If you’re interested in the role but aren’t sure whether or not you’re a good fit, we’d still like to hear from you.

Base Salary: $200,000 USD - $265,000 USD per year + Equity

The salary range provided is based on transparent pay guidelines and is an estimate for candidates residing in the San Francisco and New York City metro areas. The actual base salary will vary depending on the candidate's location, relevant experience, skills, qualifications, and other job-related factors. Additionally, this role may include the opportunity to participate in a company stock option plan as part of the total compensation package.

Must be willing to work from our SF office at least 3x per week

This position requires a commitment to a hybrid work model, with the expectation of coming into the office a minimum of (3) three times per week. Relocation assistance is available for candidates willing to move to San Francisco within 6 months of accepting an offer.

Must be willing to travel up to 10%

Abridge typically hosts a three-day builder team retreat every 3-6 months. These retreats often feature internal hackathons, collaborative project sessions, and social events that allow the team to connect in person.

We value people who want to learn new things, and we know that great team members might not perfectly match a job description. If you’re interested in the role but aren’t sure whether or not you’re a good fit, we’d still like to hear from you.

Why Work at Abridge?

  • Be a part of a trailblazing, mission-driven organization that is powering deeper understanding in healthcare through AI!

  • Opportunity to work and grow with talented individuals and have ownership and impact at a high-growth startup.

  • Flexible/Unlimited PTO — Salaried team members can take off as much approved time off as they need, plus 13 paid holidays

  • Equity — For all salaried team members

  • Medical insurance — We pay 100% of the premium for you + 75% for dependents. 3 Aetna plans to choose from.

  • Dental & Vision insurance — We pay 100% of the premium for you + 75% for dependents. 2 Aetna plans to choose from.

  • Flexible Spending (FSA) & Health Savings (HSA) Accounts

  • Learning and Development budget — $3,000 per year for coaching, courses, workshops, conferences, etc.

  • 401k Plan — Contribute pre-tax dollars toward retirement savings.

  • Paid Parental Leave — 16 weeks paid parental leave, for all full-time employees

  • Flexible working hours — We care more about what you accomplish than what specific hours you’re working.

  • Home Office Budget — We provide up to $1,600 in a one-time reimbursement to set up your home office.

  • Sabbatical Leave — 30 days of paid Sabbatical Leave after 5 years of employment.

  • ...Plus much more!

Life at Abridge

At Abridge, we’re driven by our mission to bring understanding and follow-through to every medical conversation. Our culture is founded on doing things the “inverse” way in a legacy system—focusing on patients, instead of the system; focusing on outcomes, instead of billing; and focusing on the end-user experience, instead of a hospital administrator's mandate.

Abridgers are engineers, scientists, designers, and health policy experts from a diverse set of backgrounds—an experiment in alchemy that helps us transform an industry dominated by EHRs and enterprise into a consumer-driven experience, one recording at a time. We believe in strong ideas, loosely held, and place a high premium on a growth mindset. We push each other to grow and expose each other to the latest in our respective fields. Whether it’s holding a PhD-level deep dive into understanding fairness and underlying bias in machine learning models, debating the merits of a Scandinavian design philosophy in our UI/UX, or writing responses for Medicare rules to influence U.S. health policy, we prioritize sharing our findings across the team and helping each other be successful.

Diversity & Inclusion

Abridge is an equal opportunity employer. Diversity and inclusion is at the core of what we do. We actively welcome applicants from all backgrounds (including but not limited to race, gender, educational background, and sexual orientation).

Staying Safe - Protect Yourself From Recruitment Fraud

We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article. Please exercise caution and cease communications if something feels suspicious about your interactions. 

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$200000K
$265000K

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What You Should Know About Machine Learning Systems Engineer (Staff/Senior), Abridge

Abridge is on the lookout for a talented Machine Learning Systems Engineer (Staff/Senior) to join our innovative team in San Francisco! Founded in 2018, our mission is to enhance the understanding of healthcare through advanced AI technologies. We specialize in transforming patient-clinician conversations into structured clinical notes efficiently and in real-time, all while allowing healthcare professionals to focus on their patients. As an ML Systems Engineer at Abridge, you will be at the forefront of scaling and deploying machine learning models to meet the demands of our growing platform. You’ll engineer scalable infrastructures crucial for deploying high-quality ML models over time while collaborating with talented professionals from various fields. This isn’t just a job; it’s an opportunity to make a meaningful impact in healthcare! You will lead the way in architecting software systems that manage model deployments, ensuring they're robust enough for our ambitious goals. If you have experience in deploying ML models at scale, a passion for problem-solving, and a collaborative spirit, this role is perfect for you. We believe in learning every day, and even if you don’t tick every box, if you’re passionate about healthcare and technology, we want to hear from you. Ready to embark on this exciting journey with Abridge? Let’s empower better healthcare together!

Frequently Asked Questions (FAQs) for Machine Learning Systems Engineer (Staff/Senior) Role at Abridge
What are the main responsibilities of a Machine Learning Systems Engineer at Abridge?

As a Machine Learning Systems Engineer at Abridge, your key responsibilities will include architecting and implementing ML software systems, scaling models for high traffic, and optimizing performance. You’ll collaborate with cross-functional teams to ensure smooth integrations and maintain infrastructure for efficient ML operations. Your role is crucial in ensuring that our healthcare platform operates smoothly and evolves with our growing demands.

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What qualifications are required for the Machine Learning Systems Engineer role at Abridge?

To qualify for the Machine Learning Systems Engineer position at Abridge, candidates should have over 5 years of experience in ML model deployment and scaling. Proficiency in Python and Kubernetes is essential, along with a strong understanding of cloud environments and Infrastructure as Code (IaC). A degree in computer science, engineering, statistics, or a related field, along with excellent communication skills, is also required.

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What skills are ideal for a Machine Learning Systems Engineer at Abridge?

Ideal candidates for the Machine Learning Systems Engineer role at Abridge should possess skills in designing fault-tolerant systems, optimizing performance, and troubleshooting production issues. Experience with large-scale ML platforms and proficiency in ML toolchains like PyTorch or TensorFlow are highly beneficial. Additionally, understanding low-level operating system concepts is desirable for ensuring system efficiency and scalability.

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What opportunities for learning and development does Abridge offer for Machine Learning Systems Engineers?

At Abridge, learning is a key part of our culture. As a Machine Learning Systems Engineer, you will receive a yearly budget of $3,000 for coaching, courses, and workshops to foster your professional growth. We encourage team members to expose each other to the latest industry knowledge, ensuring continuous development along with hands-on experiences that both inspire and challenge.

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What is the company culture like at Abridge for a Machine Learning Systems Engineer?

Abridge fosters a collaborative and innovative company culture where diversity and inclusion are at the core of our values. As a Machine Learning Systems Engineer, you’ll work alongside a diverse team of MDs, AI scientists, and other professionals who prioritize patient-focused outcomes. We encourage open discussions around new ideas and emphasize a growth mindset, making every day an opportunity to learn.

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Common Interview Questions for Machine Learning Systems Engineer (Staff/Senior)
Can you describe your experience with scaling machine learning models?

In your response, emphasize specific projects where you scaled ML models, mentioning the techniques used and the outcomes achieved. Highlight any challenges you faced and how you overcame them to show your problem-solving abilities.

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How do you ensure the robustness and reliability of an ML system?

Explain your approach to designing fault-tolerant, highly available systems. Outline strategies you implement for testing, monitoring, and troubleshooting production issues to maintain high system uptime and performance.

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What are your preferred tools or platforms for ML deployment?

Discuss specific tools or platforms like Kubernetes, PyTorch, or TensorFlow that you’ve used, and explain why you prefer them. Connect your experience to how these tools can contribute to the goals of Abridge’s systems.

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Can you give an example of a cross-functional collaboration you’ve been part of?

Share a story of collaborating with various teams, detailing your role, the goals set, and the results achieved. Focus on how your work contributed to the success of the project and enhanced communication among teams.

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How do you approach optimizing the performance of ML systems?

Describe the techniques you employ for performance optimization, such as monitoring metrics, testing, or algorithm adjustments. Relate this to how you would enhance Abridge’s healthcare AI platform specifically.

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What is your experience with productionizing new technologies in ML?

Highlight specific technologies or innovations you’ve developed or deployed. Describe the process of taking a concept from the ideation phase through to production, focusing on collaboration with research and engineering teams.

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Describe a time when you had to troubleshoot a production issue.

Provide a detailed account of a challenging production issue, the steps you took to resolve it, and what you learned from the experience. This illustrates your problem-solving skills and ability to stay calm under pressure.

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What design principles do you follow when developing ML systems?

Discuss foundational design principles such as scalability, reliability, and security. Relate these principles to the potential ML systems you would work on at Abridge, ensuring alignment with company goals.

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How do you keep up with new advancements in machine learning?

Share specific resources such as journals, conferences, or online courses you follow to stay updated on ML advancements. Highlight your commitment to continuous learning and adapting to industry changes.

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What challenges do you foresee in the role of a Machine Learning Systems Engineer?

Identify potential challenges such as rapid scaling or integrating new technologies. Talk about how your experience equips you to face these challenges and your proactive strategies for addressing them effectively.

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To encourage understanding and follow-through across every medical conversation.

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Full-time, hybrid
DATE POSTED
December 16, 2024

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