ML Infrastructure Engineer (Staff/Senior)

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 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.

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, rel