Senior Machine Learning Engineer (100% remote-friendly within Spain)

Docplanner Tech; Barcelona; A distancia; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

A technology healthcare company in Barcelona seeks a Senior Machine Learning Engineer to lead ML initiatives and develop AI-driven solutions. The role requires 5+ years of experience in ML and MLOps and a strong proficiency in Python, Kubernetes, and FastAPI. Collaborate with cross-functional teams to deliver impactful solutions in a fast-paced environment, utilizing cutting-edge technology to enhance healthcare services.

Formación

• 5+ years of professional experience as an ML[Ops] Engineer in a tech environment.
• Proven track record in high-scale, cross-functional initiatives.
• Expertise in production-grade MLOps, Kubernetes, and FastAPI.

Responsabilidades

• Lead ML initiatives with scientists and engineers.
• Design and deploy ML services for various data types.
• Architect data pipelines for training-set curation.

Conocimientos

Machine Learning

MLOps

Python

Deep Learning

Kubernetes

FastAPI

Descripción del empleo

Area of Work

You will join our global Machine Learning and Data Science unit - a core team of machine learning scientists (LLM, ASR), engineers and an expanding group of country-specific linguists - embedded in the ever growing Noa product organization used daily by doctors in Brazil, Mexico, Poland, Italy, Spain and Germany. Noa.ai ’s mission is to broaden access to top-quality healthcare first by lifting administrative burdens from clinicians and, in the near future, by assuming selected medical tasks. Our most famous product is Noa Notes, which records consultations, transcribes the audio and produces a structured draft of the medical record. By design, we operate cross-functionally, moving ideas fromotyping and rapid validation to full-scale production with state-of-the-art ML frameworks and modern MLOps practices. We begin with rapid validation sprints when engineers, product managers, and product developers work side by side on early experiments, then centralise and consolidate proven capabilities, standardizing validation methods and engineering best practices through shared tools, frameworks, and lifecycles.

Role

As a Senior Machine Learning Engineer in the Noa ML team you will take ownership of end-to-end ML capabilities within a product area in our Noa organisation. You will be expected to drive technical excellence, champion best practices across the team, and actively shape our evolving ML tech stack and workflows. You will work alongside other machine learning professionals at various seniority levels and report directly into the Head of Machine Learning & Data Science.

Backed by a strong engineering culture, we pair industry-leading ML rigor with pragmatic delivery, making smart trade-offs to ship value quickly and iterate fast. In this role, you’ll work alongside exceptional engineers, scientists and other cross-functional leaders on one of Docplanner’s most strategic initiatives, leveraging a cutting-edge tech stack to push the boundaries of AI in healthcare.

Our tech stack includes Python, Pytorch, Whisper, FastAPI - among the others - running on Kubernetes in AWS.

What you will be doing

Take technical leadership of ML initiatives, working closely with scientists, engineers, and product stakeholders to deliver AI-driven solutions that directly support strategic business objectives.

Design, deploy and iterate over ML services for diverse data types (e.g., audio, text), while proactively anticipating performance bottlenecks driving continuous improvements.

Brainstorm and design technical roadmaps in partnership with the AI Platform team, identifying and addressing platform and MLOps bottlenecks, and designing scalable GPU optimization strategies that balance performance, cost, and reliability.

Research, architect, and deploy LLM-powered information retrieval solutions (eg. RAG) to deliver accurate and scalable results in complex, multilingual product environments; champion industry-leading frameworks and evangelize their adoption across the organization.

Lead efforts to improve team effectiveness by evolving internal frameworks, optimizing workflows, and fostering a culture of operational excellence in collaboration with the AI Platform team.

Architect, deploy, and maintain high-throughput, reliable data pipelines to support training-set curation and data-annotation tooling.

Qualifications

5+ years of professional experience as an ML[Ops] Engineer in a fast-paced, product-driven tech environment.

Proven track record of delivering impactful ML initiatives in high-scale, cross-functional, and high-performance environments.

Demonstrated expertise in production-grade MLOps, leveraging, for example, orchestration with Kubernetes, model serving via FastAPI, NVIDIA Triton and KServe, Apache Airflow for data pipelines.

Good understanding and proficiency in deep learning frameworks such as PyTorch or TensorFlow.

Proven ability to integrate, deploy, and optimize large language models in production-grade industry environments, ensuring scalability and robust. Knowledgeable in prompt engineering, basis of agent‑based workflows, and the generation and manipulation of embeddings.

Problem-solving mindset and adaptability in dynamic environments with a focus on delivering business value to end customers.

Strong collaboration and communication skills, with a track record of influencing cross-functional stakeholders and aligning diverse teams around shared goals.

Proven ability to manage timelines, prioritize tasks, and deliver results under tight deadlines.

Experienced in mentoring and guiding other engineers, fostering technical growth and promoting a high-performance team culture.

Curiosity and eagerness to collaborate with cross-functional teams (e.g., product, marketing, engineering)

Additional Information

Let’s talk money

• A salary adequate to your experience and skills. The range is broad so that we can accommodate our roles for all levels of experience, but we will show you the career ladder to explain where we see your skills and impact within the company