Machine Learning Engineer

Healthcare professionals spend too much valuable time on administrative tasks while they should be using that time delivering great care. Attendi enables healthcare professionals to report through speech. Aside from saving time, it removes the screen between client and caregiver, allowing more personal and effective care. Burdened by an aging population, the healthcare sector is in desperate need of innovators like Attendi.

We provide an extraordinary work environment: getting paid well to work with smart people on things that actually have a positive impact on society.

Attendi has already built a strong user base through API-first speech-to-text services and SDK integrations with electronic health record (EHR) vendors. In 2025 and 2026, we have expanded beyond integrations and shaping a more comprehensive product experience. Our new Attendi App (web and mobile) is a one-stop solution for AI-driven administration in healthcare. It enables healthcare professionals to access real-time insights, structured documentation, and voice-driven workflows - anytime, anywhere.

Machine learning is central to this vision. From conversational speech recognition and diarization to LLM-based note taking, and chat-based assistance grounded in clinical guidelines, Attendi's ML stack powers the core value we deliver to caregivers.

To accelerate our roadmap, we are hiring a generalist ML Engineer who loves operationalizing ML in real-world production systems, thrives in small teams, and enjoys owning the full lifecycle - from prototyping to training, serving, monitoring, and continuously improving models in the cloud.

As a ML Engineer at Attendi, you will:

• Own multiple parts of the machine learning lifecycle: data pipelines, model training, evaluation, deployment, monitoring, and iteration.

• Work on scalable ML systems in the cloud (Azure).

• Work closely with product, backend, and mobile engineers to bring ML-powered features
  -from transcription to note taking to chat-based agentic assistance - into the hands of healthcare professionals.

• Participate in early-stage exploration of new ML features and transform promising prototypes into reliable production services.

• Wear multiple hats in a pragmatic way: from exploring LLM observability frameworks to debugging infrastructure to improving annotation workflows- whatever moves the product forward.

You might be a good fit if you hit most or all of the following:

• Have 3+ years of industry experience in machine learning engineering or applied ML roles.

• Have hands on experience deploying, scaling, and operating ML systems in production.

• Are comfortable with cloud-native infrastructure (Azure preferred, but AWS/GCP experience is also valuable).

• Are fluent in Python and have strong engineering fundamentals, including testing, monitoring, and performance considerations.

• Enjoy collaborating cross-functionally and contributing beyond your specialty - this is a small team where ownership is encouraged.

• Are pragmatic, product-focused, and motivated by solving real user problems in complex environments like healthcare.

• Have experience with containerizing ML inference and/or training.

• Have experience in one or more of the following:

  • Speech technologies (ASR, VAD, diarization)

  • Evaluating, training or fine tuning LLMs

  • ML platform-level frameworks, such as Ray, Dagster, Flyte, MLFlow, CometML

  • Data engineering

• Understand the unique constraints of healthcare environments: security, privacy, and safe clinical decision support.

• Have experience with web app backends in Python and management of databases.

• Have experience with annotation, ML observability, or other platform level frameworks for ML.

• Be familiar with RAG systems, vector databases,…