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ML Infrastructure Engineer (Staff/Senior) - job 1 of 2

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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CEO of Abridge
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Shivdev Rao
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Average salary estimate

$232500 / YEARLY (est.)
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$200000K
$265000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About ML Infrastructure Engineer (Staff/Senior), Abridge

As an ML Infrastructure Engineer (Staff/Senior) at Abridge, located in the heart of San Francisco, you’ll have the chance to make a real impact in the healthcare realm! Abridge is on a mission to transform medical conversations into structured clinical notes using cutting-edge AI technology, and we need your expertise to scale our machine learning models and integrate them seamlessly across diverse platforms. This role is about building robust, scalable infrastructures to handle increasing traffic and supporting both current and future deployments. You’ll be collaborating closely with a talented team of AI scientists, PhDs, and engineers, ensuring our AI-driven platform is reliable and efficient. Your responsibilities include architecting ML software systems, standing up critical models, and optimizing performance for high availability. With more than 5 years of experience in ML model deployment and a solid background in Python and Kubernetes, you'll help us push the boundaries of what's possible in healthcare technology. Plus, you’ll thrive in our dynamic culture that values learning and growth, pushing each other to excel. Abridge isn't just a place to work; it's a community that fuels innovation, patient-centered care, and team collaboration while prioritizing diversity and inclusion. If you're excited about shaping the future of healthcare through AI, this could be the role for you!

Frequently Asked Questions (FAQs) for ML Infrastructure Engineer (Staff/Senior) Role at Abridge
What are the key responsibilities of the ML Infrastructure Engineer at Abridge?

The ML Infrastructure Engineer at Abridge plays a pivotal role in architecting, designing, and implementing machine learning software systems for deploying and managing models at scale. This includes standing up models for inference, maintaining infrastructure for efficient model operations, collaborating with cross-functional teams, and optimizing ML systems for high availability and fault tolerance.

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What qualifications are required for the ML Infrastructure Engineer position at Abridge?

Candidates applying for the ML Infrastructure Engineer position at Abridge should have a Bachelor’s or Master’s Degree in Computer Science, Engineering, Statistics, Mathematics, or equivalent. Additionally, a minimum of 5 years of experience in ML model deployment, strong proficiency in Python and Kubernetes, and expertise in designing fault-tolerant systems are essential qualifications.

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What technologies should an ML Infrastructure Engineer at Abridge be familiar with?

An ML Infrastructure Engineer at Abridge should be well-versed in Python, Kubernetes, and cloud environments. Familiarity with ML platforms like Ray or Databricks and ML toolchains such as PyTorch or TensorFlow, as well as experience with Infrastructure as Code (IaC), are also beneficial for success in this role.

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Is Abridge a good place for career growth for an ML Infrastructure Engineer?

Absolutely! Abridge offers a vibrant workplace culture focused on learning and growth. With opportunities for coaching, conferences, and innovative projects, you will have the chance to expand your skills and make significant contributions to cutting-edge healthcare technology as an ML Infrastructure Engineer.

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What kind of work environment can an ML Infrastructure Engineer expect at Abridge?

The work environment at Abridge is collaborative and dynamic, where teamwork across AI scientists, engineers, and other professionals is highly encouraged. Employees enjoy flexible working hours, a hybrid model, and regular team-building retreats that foster a supportive culture aimed at shared success.

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Common Interview Questions for ML Infrastructure Engineer (Staff/Senior)
Can you describe your experience with deploying ML models in production?

When answering this question, focus on specific projects where you successfully deployed ML models. Discuss the challenges you faced, technologies you used, and how you optimized the deployment for performance and scalability. Ensure to mention any collaborative efforts with cross-functional teams.

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What strategies do you employ to ensure high availability and fault tolerance in ML systems?

Demonstrate your understanding of system design by describing techniques like load balancing, failover mechanisms, and redundancy. Also, share practical examples of how you’ve implemented these strategies in your previous roles to ensure systems handled failures gracefully.

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How do you optimize the performance of ML systems?

Discuss the methodologies you use for performance optimization, such as monitoring system metrics, profiling, or adjusting parameters. Provide examples of times when you improved system performance and the impact it had on operations.

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What’s your approach to collaborating with ML researchers and cross-functional teams?

Share your philosophy on collaboration and communication. Mention how you initiate conversations, gather requirements, and ensure all teams are aligned on goals. Highlight specific projects where effective collaboration led to successful outcomes.

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How do you troubleshoot issues in production ML systems?

Talk about your systematic approach to troubleshooting, which might include logging, monitoring alerts, and employing debugging tools. Provide an example of a particularly challenging issue you resolved and what you learned from that experience.

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What experience do you have with Infrastructure as Code (IaC) and cloud environments?

Be prepared to discuss the IaC tools you've worked with, such as Terraform or AWS CloudFormation, and how you've leveraged cloud services to enhance your ML infrastructure. Share specific instances where IaC improved deployment efficiency or scalability.

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Can you explain your experience with performance evaluation of ML models?

Speak to the metrics and benchmarks you use for evaluating ML model performance, such as accuracy, precision, recall, or F1 scores. Provide examples of how you used these insights to adjust models or inform decisions in production.

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How do you stay updated with the latest trends and advancements in ML technology?

Share your methods for keeping informed, whether it be attending conferences, participating in workshops, following the latest research papers, or engaging with the ML community online. Mention any specific sources or networks.

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What are some challenges you expect to face in this role and how will you overcome them?

Consider discussing potential challenges like managing increased traffic or integrating new models. Show your problem-solving skills by outlining strategies you would use to tackle these challenges, such as conducting thorough testing before deployment.

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Why do you want to work at Abridge as an ML Infrastructure Engineer?

Your answer should reflect your passion for healthcare innovation and how Abridge's mission aligns with your career goals. Discuss the value you see in contributing to a company that prioritizes patient-centered care and the role that cutting-edge technology plays in achieving that.

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

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Full-time, hybrid
DATE POSTED
December 13, 2024

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