Associate Principal Scientist, Senior Machine Learning Engineer

Boehringer Ingelheim

Engineer in AI & Analytics within Boehringer Ingelheim’s Computational Innovation Unit. Focusing on ML lifecycle tooling and collaborative solutions to support scientific research.

Posted 7/24/2026full-timeVienna • 🇦🇹 AustriaSenior💰 €90,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning and Deep Learning, with a strong foundation in Python programming and MLOps practices. Capable of building and managing ML lifecycle tooling, deploying models in production, and collaborating across teams to deliver AI solutions for scientific applications.

Highest-signal resume keywords

Machine Learning Lifecycle ToolingMLOps ExpertisePython ProgrammingCloud-Based ML InfrastructureModel Deployment and Maintenance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningMLOpsExperiment TrackingModel VersioningContainerizationReproducible PipelinesModel RegistriesFine-Tuning ModelsData Engineering

Tools & Technologies

AWSSageMakerDatabricksDockerAzure

Industry Keywords

Biomedical ApplicationsPharmaceutical ApplicationsComputational InnovationCloud Platforms

Tech Stack

Tools & technologies

AWSAzureCloudDockerPython

About the role

Key responsibilities & impact

Build and own the team’s ML lifecycle tooling, including reproducible training and fine‑tuning pipelines, experiment tracking, model registries, and the packaging and deployment of deep learning models.
Drive efficient and reproducible model development by managing compute environments, GPU workloads, and large‑scale biomedical datasets on cloud platforms such as AWS/SageMaker and Databricks.
Transform prototype code into robust, reusable solutions, establishing frameworks, templates, and best practices that accelerate delivery across projects.
Collaborate closely with data engineering, platform, and AI science teams to integrate ML solutions into the broader Computational Innovation ecosystem.
Evolve from ML platform ownership toward applied ML/DL development, contributing increasingly to model adaptation, fine‑tuning, evaluation, and the delivery of AI solutions for scientific use cases as the platform matures.

Requirements

What you’ll need

Degree in Computer Science, Engineering, or a related field, with hands‑on experience as an ML Engineer, MLOps Engineer, or in a similar role.
Strong software engineering skills in Python, with proven experience deploying, serving, and maintaining ML/DL models in production environments.
Demonstrated MLOps expertise, including reproducible pipelines, experiment tracking, model versioning and registries, containerization (Docker), and cloud‑based ML infrastructure (AWS, Azure, or similar platforms).
Strong interest and aptitude in Machine Learning and Deep Learning, with hands‑on experience training and fine‑tuning models and a desire to grow further in applied ML/DL development.
Experience or interest in scientific, pharmaceutical, or biomedical applications is a strong advantage, although deep domain expertise is provided by other members of the team.

Benefits

Comp & perks

Flexible working arrangements: remote work and flexible hours depending on department and role — many options are available.
Additional days off (“bridge days”): extra time off to bridge single working days between public holidays and the weekend — without using vacation days.
Canteen & cafeteria: from coffee and croissants at breakfast to varied lunch menus and snacks — our subsidized staff restaurant & cafeteria offers options for all tastes, including vegetarian and vegan choices.
Learning & development: a range of training and development opportunities for your personal and professional growth. Because you never stop learning.
Health promotion: we offer various programs to support physical and mental well‑being.
Public transport pass: we encourage employees to use public transport for commuting. Travel costs? We cover them!