Top Machine Learning Jobs in Germany

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Design AI-powered healthcare product experiences for clinical workflows, including documentation, patient summaries, search, agent workflows, multimodal interactions, uncertainty handling, and approval flows. Lead discovery, prototyping, and user validation; collaborate with ML engineers, product managers, software engineers, and clinicians. Contribute AI interaction patterns and components to the design system, establish AI design principles, and promote trustworthy, high-quality experiences for hospital users.

Lead customer onboarding projects for laboratories implementing AI-powered diagnostic software, coordinating product, ML, engineering, quality, IT, and clinical stakeholders through validation and go-live. Own project plans, risks, dependencies, and delivery dates. After launch, serve as the primary customer contact, managing support, monitoring engagement, coordinating issue resolution, and feeding customer insights into product planning. The role requires technical confidence, clinical or laboratory implementation experience, stakeholder leadership, and occasional international travel.

Develops new business for AI-driven software solutions by identifying prospects, managing the full sales cycle, presenting proposals, negotiating contracts, maintaining CRM records, and transitioning new accounts to delivery teams. The role also builds long-term customer relationships, expands professional networks, analyzes AI market opportunities, and supports revenue growth through complex proposals and tenders.

Lead redesign and development of B2B underwriting models, scorecards and decision engines; improve data analysis and processes; build multi-country credit frameworks; partner with Data Science to deploy ML credit models; enhance reporting and risk KPIs; collaborate with cross-functional teams to support B2B product expansion.

Own end-to-end delivery of specific ADAS features (e.g., TFL, High Beam Assist, Blind Spot Monitoring). Define feature scope, set performance targets, align OEM requirements with internal roadmaps, collaborate with engineering/product/OEM teams, participate in testing/validation, and mitigate risks to ensure features meet specs and production readiness.

Own and improve Babbel’s learner-personalization and mastery-tracking systems end to end. Responsibilities include ML research, benchmarking, production deployment, monitoring, incident response, experimentation, and subsystem ownership. The role requires shipping and operating real-time models under user load, evaluating offline and online performance, and using TypeScript or Python with AWS, Terraform, and CI/CD. Experience with probabilistic modeling, Bayesian inference, recommendation or ranking systems, and rigorous A/B testing is required.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead and develop a team of Data Scientists and ML Engineers building production machine-learning systems for logistics. Drive AI adoption, roadmap prioritization, agile execution, and measurable business outcomes. Partner with Product Managers, Staff ML Engineers, and the MLOps platform team to deploy scalable solutions. The role requires deep hands-on experience with production ML, recommender systems, pricing algorithms, statistical models, and modern data and AI technologies, alongside strong people leadership and executive communication skills.

Deliver operations-focused strategy consulting engagements across procurement, supply chain, manufacturing, and digital operations. Lead workstreams, perform advanced analytics and ML-driven solutions, synthesize recommendations, manage client relationships, and coach junior team members to drive sustainable performance improvements.

Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

As an R&D Engineer, you will design and optimize AI pipelines, integrate LLMs into testing workflows, and analyze semiconductor data.

Deliver client engagements in operations-focused performance transformation across procurement, supply chain, manufacturing, and digital operations. Lead major workstreams, perform rigorous analysis, synthesize recommendations, coach junior staff, collaborate with clients, and apply advanced analytics and digital solutions to improve operational performance.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Analyze and validate private markets investment data (PE, debt, real estate, infrastructure); compute performance metrics; build data models, reports, and Power BI dashboards; query large datasets with SQL; manage client onboarding, data requirements, and escalations; explore AI/automation for data workflows and mentor junior staff (Senior).

Develop and optimize distributed machine learning training and inference systems for very large models. Collaborate with research scientists, diagnose performance bottlenecks, profile and debug experiment code, build orchestration tools, optimize compute resources, and establish best practices for large-scale ML workflows.

Develop and optimize machine learning research workflows for large-scale model training and inference. Collaborate with research scientists to implement novel models and simulated environments, build tools that improve computing efficiency, and establish best practices for ML development. The role requires advanced ML expertise, deep learning research experience, high-performance computing, cloud platforms, containerization, and data infrastructure familiarity.

Design and implement novel machine learning models for advanced AI safety research. Collaborate with mathematicians and research scientists, adapt and fine-tune frontier models, create experimental protocols and evaluation frameworks, analyze results, and optimize model performance with ML engineers. The role requires deep learning research experience, expertise with major ML libraries and distributed computing, strong Python development skills, and effective interdisciplinary communication.

Develop and scale web-scale data pipelines that transform raw text into high-quality machine learning datasets. Responsibilities include deduplication, model-based scoring, filtering, toxicity and PII removal, metadata extraction, dataset versioning, provenance tracking, compliance, leakage detection, quality monitoring, and researcher tooling. The role also involves acquiring and evaluating text corpora, collaborating with research, engineering, legal, and governance teams, and developing novel data transformations for AI training.

Develop and evaluate probabilistic inference methods, including amortized inference for high-dimensional distributions and probabilistic graphical models. Translate theoretical research into high-quality Python implementations, design evaluation strategies, analyze experiments, and collaborate with mathematicians. The role also requires communicating complex findings and contributing to research on safe-by-design, non-agentic AI systems.

Lead simulation-driven development of power modules using multiphysics (electrical, thermal, mechanical) models, digital twins, AI-augmented optimization, and automation to accelerate SiC/GaN power-electronics design and reliability prediction while collaborating with global teams.

Develop and maintain a TypeScript/JavaScript fullstack IoT platform dashboard (React, Koa/Express), collaborate on microservices, write high-quality code and tests, and contribute to product and architecture decisions in an agile team.

Develop, analyze, and implement quantum error-correcting codes and decoding algorithms for fault-tolerant quantum computing. Design fault-tolerant operations (e.g., lattice surgery), implement and optimize decoders in Python/C/C++/Rust (potential FPGA deployment), perform resource estimation and scalability analysis, collaborate with hardware and software teams, and publish research in scientific venues.

Support engineering team on machine learning projects: data processing, maintenance, reporting, troubleshooting. Analyze and optimize processes, suggest improvements, and learn industrial practices in a startup environment.

As a Machine Learning Engineer, you'll integrate intelligent systems into healthcare, build and deploy ML models on clinical datasets, and work with federated learning frameworks.