ML Platform Engineer: Data Pipelines & Deployment

RaySearch Laboratories; Stockholms kommun; On-site; SEK 700,000 - 900,000; Full time

Benefits offered by this job

In-house gym

Lunch buffet

Rooftop terrace with view

Job summary

RaySearch in Stockholm is seeking a Machine Learning Platform Engineer to help build the full model lifecycle platform, enabling ML teams to curate, train and validate models.

This office-first role focuses on robust APIs, reliable services and scalable data and infrastructure, working with ML engineers, software, infra and medical physics teams to deploy and monitor production models.

Qualifications

• M.Sc. in Computer Science, Software Engineering, ML or equivalent with 5+ years of professional software engineering.
• Strong Python skills and backend/API design experience.
• Solid knowledge of relational databases and data modeling (PostgreSQL).
• Experience with containers (Docker) and running production services.
• Good engineering practices: automated testing, code review, CI/CD and observability.
• Experience contributing to architecture decisions in large, shared codebases.
• Excellent spoken and written English.

Responsibilities

• Develop and maintain the model development platform across data, training, validation and deployment.
• Build scalable APIs and services used by ML engineers and other teams.
• Implement data curation, ETL, validation reporting and data lineage.
• Enable production deployment and inference optimization for clinical models.
• Ensure reliability, observability and operational monitoring of platforms.

Skills

Python

API design

Backend services

Data modeling

CI/CD

Observability

Architecture decisions

English communication

Education

M.Sc. in Computer Science or related

Tools

Flyte

Docker

Kubernetes

PostgreSQL

Git

Job description

RaySearch in Stockholm is seeking a Machine Learning Platform Engineer to help build the full model lifecycle platform, enabling ML teams to curate, train and validate models.

This office-first role focuses on robust APIs, reliable services and scalable data and infrastructure, working with ML engineers, software, infra and medical physics teams to deploy and monitor production models.