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Machine Learning Systems Engineer (Staff/Senior) - job 1 of 3

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

Abridge is on the lookout for a passionate and skilled Machine Learning Systems Engineer (Staff/Senior) to join our innovative team in San Francisco. Since our founding in 2018, we’ve been dedicated to revolutionizing healthcare with our AI-powered platform, which enhances medical conversations and streamlines clinical documentation. As a key player in this mission, you’ll be responsible for architecting, designing, and implementing robust ML software systems that are crucial for scaling and deploying our AI models. You will collaborate closely with talented ML researchers and engineers, ensuring seamless integration of models into our systems while fostering a culture of continuous improvement and excellence. Your experience will be invaluable in optimizing our infrastructure to handle increasing traffic demands and support real-time applications. If you love pushing the boundaries of technology while making a real impact in healthcare, this role at Abridge might just be your perfect fit. With a competitive salary ranging from $200,000 to $265,000 and a whole suite of benefits including equity, flexible PTO, and a supportive team culture, you'll not only thrive professionally but personally as well. Dive into a role where your expertise will help us empower patients and clinicians alike, making healthcare more understandable and efficient. Join us, and let’s drive the future of health 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?

The primary responsibilities of a Machine Learning Systems Engineer at Abridge encompass architecting and implementing software systems for deploying and managing machine learning models effectively. This role involves collaborating with cross-functional teams to ensure seamless integration of models that support our AI-powered healthcare platform. Additionally, engineers will optimize infrastructure for high availability and scalability, troubleshot production issues, and maintain the performance of ML systems.

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What qualifications do I need to apply for the Machine Learning Systems Engineer position at Abridge?

To qualify for the Machine Learning Systems Engineer position at Abridge, candidates should ideally have over 5 years of experience in ML model deployment with a strong focus on production-quality software. Proficiency in Python and Kubernetes is crucial, alongside a background in computer science, statistics, or mathematics. Experience with fault-tolerant systems and cloud environments is highly valuable.

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How does Abridge support the professional growth of a Machine Learning Systems Engineer?

Abridge places significant importance on learning and development for its Machine Learning Systems Engineers. The company offers a generous annual budget of $3,000 specifically for coaching, courses, workshops, and conferences, ensuring that team members have opportunities to enhance their skills and stay updated on the latest industry advancements.

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What is the work environment like for a Machine Learning Systems Engineer at Abridge?

The work environment at Abridge for a Machine Learning Systems Engineer is dynamic and collaborative. Employees are encouraged to share ideas, engage in knowledge-sharing activities, and participate in team retreats that foster team bonding and innovation. The company prides itself on having a supportive culture driven by a commitment to making a meaningful impact in the healthcare industry.

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What is the salary range for the Machine Learning Systems Engineer position at Abridge?

The salary range for the Machine Learning Systems Engineer role at Abridge is between $200,000 and $265,000 per year, depending on factors like experience, skills, and location. The position also includes the exciting opportunity to participate in a company stock option plan, enhancing the overall compensation package.

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

When answering this question, highlight specific projects where you successfully deployed ML models. Discuss the technologies used, challenges faced, how you addressed them, and the outcomes achieved. Highlight your proficiency in using deployment tools like Kubernetes and AWS for scalability.

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How do you approach troubleshooting production issues in machine learning systems?

An effective response should demonstrate your systematic approach to troubleshooting. Discuss methods you use for logging, monitoring, and diagnosing system performance issues. Mention any tools or frameworks that aid you in quickly identifying and resolving issues while ensuring minimal downtime.

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What experience do you have with ML frameworks such as PyTorch or TensorFlow?

In your answer, specify the projects where you've used PyTorch or TensorFlow effectively. Outline how you utilized these frameworks to build and refine models, mentioning any key performance metrics or successes achieved in your projects.

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How do you ensure model performance and fault tolerance in ML systems?

Describe your strategies for model performance evaluation, continuous integration/testing, and monitoring systems after deployment. Talk about implementing redundancy and failover strategies to ensure high availability and robustness in production.

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Describe a time you collaborated with cross-functional teams on a machine learning project.

Provide an example that showcases your teamwork skills. Focus on how you engaged with engineers, product managers, or data scientists, what challenges were encountered, and how collective brainstorming led to innovation and problem solving.

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What strategies do you use to keep abreast of new advancements in machine learning?

Discuss your commitment to lifelong learning. Mention following leading AI research papers, participating in online forums, attending workshops, or completing relevant online courses. Show that you are proactive about integrating new skills and knowledge into your work.

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How would you optimize an ML model for better performance?

Talk about techniques such as hyperparameter tuning, feature engineering, and model selection strategies you've used to enhance model performance. Emphasize the importance of evaluating trade-offs between accuracy and computational efficiency.

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What factors do you consider when designing infrastructure for ML deployments?

Explain the key factors including scalability, fault tolerance, security, and ease of use. Discuss the need for infrastructure that enables rapid iterations while allowing teams to manage data efficiently across various ML projects.

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How do you manage and prioritize multiple ML projects?

Focus on your time management skills. Describe the methodologies you use, such as Agile or Kanban, and how to set deadlines, prioritize tasks, and communicate effectively with stakeholders to keep projects on track.

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What is your approach to integrating machine learning models with existing systems?

In responding, highlight your technical expertise in APIs and service integration. Discuss the importance of understanding system architecture and the need for thorough testing and validation to ensure seamless model integration within existing workflows.

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

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Full-time, hybrid
DATE POSTED
December 14, 2024

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