Software Engineer, Backend/Systems (All Levels)

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. We have offices located in the SoDo neighborhood of New York, the Mission District in San Francisco, and Lawrenceville in Pittsburgh.

The Role

Abridge is hiring Software Engineers with a strong background building backend infrastructure and distributed systems to join our team! These engineers will play a critical role in building and optimizing the backend infrastructure that powers our AI-Driven healthcare products. Your work will be instrumental in ensuring the scalability, reliability, and performance of our systems, enabling us to deliver actionable insights from clinical conversations.

Collaborating closely with machine learning engineers, data scientists, and product teams, you’ll design and implement high-performance, distributed systems that support model serving, data processing, and integration with healthcare applications. We’re looking for individuals with a passion for backend engineering and distributed systems, who are ready to make a meaningful impact in healthcare!

What You’ll Do

• Design, develop, and maintain backend infrastructure to support machine learning models and AI-powered healthcare applications.

• Work with distributed systems, ensuring high availability, scalability, and performance of the platform.

• Optimize model serving, including throughput, latency, and resource utilization for high-performance computing.

• Collaborate with ML and data teams to integrate AI models into production environments, supporting both training and inference workflows.

• Build and maintain APIs and services for data access and machine learning model interaction.

• Troubleshoot, debug, and optimize backend systems to improve reliability and system performance.

• Help scale the team by providing mentorship to junior engineers and collaborating with leadership on architectural decisions.

What You’ll Bring

• 5+ years of backend engineering experience, with a focus on distributed systems and high-performance computing.

• Strong experience in building and maintaining scalable, performant backend systems.

• Familiarity with cloud infrastructure (e.g., AWS, GCP) and the ability to design cloud-native solutions.

• Proven experience in optimizing systems for throughput, latency, and reliability, including working with model serving and deployment pipelines.

• Solid understanding of backend technologies, such as service-oriented architecture, microservices, and APIs.

• Experience working with cross-functional teams, especially in a startup environment, to align engineering efforts with product goals.

• Strong problem-solving skills and the ability to work in fast-paced, evolving technical environments.

Bonus Points If…

• Experience with machine learning model deployment and optimization, particularly in cloud environments

• Familiarity with ML infrastructure tools, such as Nvidia Triton Server for model serving and optimization

• Experience with high-performance data pipelines and numerical computing

• Prior experience with healthcare applications or systems, particularly in areas related to clinical workflows and data privacy

• Familiarity with generative AI systems or other advanced AI techniques

Base Salary: $185,000 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.

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?

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.

How we take care of Abridgers:

• Generous Time Off: 13 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees.

• Comprehensive Health Plans: Medical, Dental, and Vision plans for all full-time employees. Abridge covers 100% of the premium for you and 75% for dependents. If you choose a HSA-eligible plan, Abridge also makes monthly contributions to your HSA. 

• Paid Parental Leave: 16 weeks paid parental leave for all full-time employees.

• 401k and Matching: Contribution matching to help invest in your future.

• Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits.

• Learning and Development Budget: Yearly contributions for coaching, courses, workshops, conferences, and more.

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

• Compensation and Equity: Competitive compensation and equity grants for full time employees.

• ... and much more!

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.