ML Platform Engineer

At Attendi, we believe in care with attention.

Healthcare professionals and clients should have the time to get to know each other. Because good care starts with a good relationship. Technology can play a crucial role in this when it’s empathetic, supportive, and smart. We develop AI solutions that give healthcare professionals their time back: less screen, more human.

With Attendi, healthcare professionals report by simply speaking. They report by voice in the Electronic Health Record, and they can also use the Attendi App to record conversations and have them automatically converted into reports. Tens of thousands of healthcare professionals use our technology every day, resulting in hundreds of thousands of reports and more time for clients.

We are growing fast, driven by the trust of healthcare providers and professionals who rely on our solutions daily. As we continue to scale, we are looking for passionate engineers who want to make a tangible difference in healthcare, while working with cutting-edge technology in a collaborative, mission-driven environment.

The Role: ML Platform Engineer

As our ML Platform Engineer, you’ll play a key role in building and optimising the data infrastructure behind Attendi’s machine learning and speech-to-text solutions. Your work will directly impact the reliability, performance, and scalability of our products—supporting healthcare professionals who rely on our tech daily.

You’ll collaborate with ML engineers, backend developers, product teams, and annotators to ensure efficient data flows, robust ML training and serving infrastructure, high-quality monitoring, and seamless system integration.

This role is central to enabling advanced AI applications like real-time transcription, automated voice reporting, and NLP services. You’ll also contribute to research and innovation, helping expand the capabilities of our AI-driven tools.

And that’s just the beginning.

Attendi’s API-first speech-to-text solutions and client SDKs are already deeply integrated into healthcare workflows via EHR vendors. With your help, we’ll build the next generation of our Speech-to-Text Engine—expanding into new domains and reaching a broader range of healthcare professionals. In this role, you will be:

• Automation of ML Operations: Developing automated workflows across R&D processes and integrating human-in-the-loop approaches to facilitate rapid cycles of model evaluation, retraining, and deployment.

• ML Infrastructure and Service Optimisation: Setting up scalable infrastructure to meet R&D needs while improving the stability, logging, error handling and resource efficiency of our core ML services.

• Speech-to-Text Enhancements: Revamping performance and usage analytics, finalising and operationalising new models, developing transcription accuracy reports.

• Data Organisation and Utilisation: Organising, integrating, and leveraging diverse data sources effectively to maximise their value for ML applications, while ensuring data security, ethical handling, and compliance with privacy standards.

You’ll actively shape our data engineering strategy and infrastructure to ensure it supports both current and upcoming innovations, directly contributing to transformative healthcare technologies.

What you'll do:

• Build and maintain scalable data pipelines for ML and analytics applications.

• Set up and manage the necessary infrastructure for R&D and delivering solutions to production.

• Establish data versioning, experiment logging, and artifact management.

• Support the research, testing, and implementation of advanced solutions like real-time transcription, domain-specific model training, and automated transcript correction.

• Implement data monitoring, performance metrics, and evaluation frameworks using Grafana and other tools.

• Collaborate closely with engineering teams to support new ML-driven product features and improvements.

• Develop and maintain infrastructure-as-code.

What you bring:

• 5+ years of experience as an ML Platform/Data Engineer.

• Solid experience managing the ML lifecycle—from experimentation to production deployment.

• Proven expertise in data warehousing, database/storage management, and developing scalable data pipelines.

• Proficiency with technologies such as Azure/AWS, Ray/Apache Spark/Dagster/Flyte, Psycopg2/DuckDB.

• Experience in containerisation and orchestration technologies: Docker, Kubernetes.

• Experience with infrastructure as code (Terraform).

• Familiarity with monitoring and logging tools (Grafana, Sentry).

• Demonstrated ability to work autonomously, think critically, and ensure meticulous planning and validation.

• A pragmatic approach to problem-solving.

Strong candidates may also:

• Have experience using platforms like Weights & Biases (W&B), Comet ML or MLFlow for experiment tracking.

• Bring experience in performance benchmarking, automated model evaluation, and leaderboard generation.

• Have experience in the healthcare or speech technology domain.

• Thrive in dynamic environments, comfortably navigating complexity and ambiguity.

• Have an entrepreneurial mindset

What we offer

• Working on impactful solutions for problems in healthcare: our solutions directly result in time savings for healthcare professionals, time they can use to deliver better and more personal care.

• Transparent and responsible personal development: we encourage personal development by making development plans together and giving the opportunity to follow courses that you feel contribute to your development.

• Competitive compensation.

• Possibility to work from home: while we like to work together at our Amsterdam-based office, we think having the freedom to work at home when it suits you is important. Most of us spend two to three days per week at the office and the rest working from home.

• A great place to work – feel free to reach out to our team to ask what it’s like!

• We are an equal and diverse opportunity employer.

Our culture: People-oriented, curious, and proactive

At Attendi, you’ll work in a small, ambitious team that learns and builds together. We are empathetic, take initiative, dare to experiment, and stand by our work. Making mistakes is okay as long as you learn from them.

Application Process:

• Initial phone call

• First interview with a team member

• Case study + lunch to get to know each other better

• Final interview with one of the founders

• Offer and onboarding