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.
As an ML Infrastructure 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.
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.
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
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 or NYC office 2-3x/week (Relocation assistance is available for candidates willing to move to San Francisco or New York)
Strong preference for candidates who are currently in the San Francisco Bay Area or the New York Tri-State area, or are willing to relocate to these areas. This position requires a commitment to a hybrid work model, with the expectation of coming into the office 2-3 times per week.
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.
Be a part of a trailblazing, mission-driven organization that is powering deeper understanding in healthcare through AI.
Unlimited PTO for salaried team members, plus 13 paid holidays
Comprehensive and generous benefits package
Learning and Development budget of $3,000 per year for coaching, courses, workshops, conferences, etc.
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
Remote work environment
Equity for all new employees
Generous equipment budget for your home office setup ($1600)
Opportunity to work and grow with talented individuals, and have ownership and impact at a high growth startup.
Plus much more!
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.
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).
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.
To encourage understanding and follow-through across every medical conversation.
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