Top Machine Learning Jobs in Germany

Supports development and validation of cloud-native telecommunications software and AI solutions for software engineering, knowledge management, automation, and developer productivity. Responsibilities include building scripts, tools, dashboards, prototypes, and proof-of-concept implementations; analyzing engineering data; supporting documentation and knowledge repositories; and participating in AI pilots, workshops, hackathons, and stakeholder collaboration.

Leads and develops a team supporting robotics and AI initiatives, including hiring, mentoring, workflow improvement, and teleoperation program coordination. Owns cross-functional planning, timelines, resources, stakeholder communication, risk mitigation, project delivery, and strategic roadmap development for humanoid and dual-arm robotics solutions.

Lead AI strategy and adoption initiatives by aligning technical teams, business stakeholders, and senior leadership. Translate business needs into actionable requirements, manage complex delivery risks and dependencies, communicate technical trade-offs, and support AI solutions from proof of concept through production. Drive change management, stakeholder alignment, adoption, and long-term business value across ambiguous, cross-functional environments.

Own full-cycle recruiting across Mindpeak, focusing on senior, leadership, and hard-to-fill roles in Product, Technology, and Business. Partner with founders and hiring managers to define requirements, source and assess candidates, run rigorous interviews, close hires, and manage external search partners. Improve hiring quality through structured role profiles, scorecards, interview processes, pipeline transparency, and evidence-based talent decisions.

Own the strategy, roadmap, metrics, and adoption of JetBrains Context, a managed retrieval layer for AI coding agents. Partner with engineering, ML, platform, IDE, marketing, sales, and customer success teams to improve indexing, retrieval quality, integrations, reliability, cost, and latency. Define product direction, evaluate competitors, establish quality metrics, communicate with customers and developer communities, and drive enterprise adoption.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Lead applied science strategy for Search, build and manage teams of applied scientists and ML engineers, develop state-of-the-art query understanding, retrieval, ranking, and personalization models, drive ML platform and experimentation, establish model governance and evaluation, and align technical roadmap with business outcomes.

Build and maintain machine learning and streaming infrastructure for real-time risk detection, assessment, and mitigation. Develop scalable backend systems while working with an experienced engineering team and receiving mentorship. The role is focused on foundational software engineering, backend development, and learning ML infrastructure.

Own backend and infrastructure for AI shopping agents, including live checkout automation, large-scale product ingestion, search and ranking, deployment, monitoring, databases, observability, and on-call operations. Build reliable systems that handle changing retailer environments, real payments, and continuously evolving catalogs. Partner directly with the CTO and improve failure recovery, correctness, and scalability. Experience with AI agents, LLMs, retrieval, ranking, e-commerce, payments, or fraud infrastructure is valued.

Own backend and infrastructure for AI shopping agents, including live checkout automation, large-scale product ingestion, search and ranking, deployments, monitoring, databases, and on-call operations. Build reliable systems that handle changing retailer environments, real payments, and continuously evolving catalogs. Work closely with the CTO in a fast-moving environment.

Lead operations-focused consulting engagements end-to-end, drive problem-solving and recommendations, manage teams, apply analytics and digital solutions, build client relationships, and support sustainable operational transformation.

Own the vision, roadmap, backlog, and delivery of an AI-driven reservoir simulation and optimization product. Translate reservoir engineering and production optimization needs into prioritized requirements, user stories, and acceptance criteria. Coordinate reservoir engineers, data scientists, software and DevOps teams, while monitoring model accuracy, performance, and business KPIs. Facilitate agile ceremonies, manage stakeholders, and guide features spanning data pipelines, model training, history matching, forecasting, and reservoir optimization.

Conduct research on failure management in optical transport networks using machine learning. Review existing research, investigate algorithms for data generation and failure prediction, present findings, and prepare a final report. The three-month internship offers mentorship, collaboration with optical networking experts, and potential continuation into a master’s thesis.

Drive enterprise SaaS revenue growth across Central Europe, focusing 90% on new business. Prospect, develop, manage, and close complex opportunities with B2B organizations; build territory, account, and opportunity plans; exceed quotas; and sell Conga’s revenue management solutions. Collaborate with sales engineering, marketing, customer success, partners, and professional services while presenting tailored solutions, managing the full sales cycle, and cultivating strategic relationships.

Lead neuroscience R&D for EEG-based BCI: develop signal-processing and classification algorithms, design/execute human-subject experiments, integrate neural data with hardware/software, validate and ensure regulatory and ethical compliance, and mentor junior researchers.

Design and lead large-scale biomolecular experimental data-generation campaigns for Microsoft’s BioEmu models. Responsibilities include selecting assays and systems, directing CROs and collaborators, reviewing data quality, diagnosing experimental artifacts, building traceable machine-learning datasets, and coordinating computational and experimental research. The role requires independent research ownership, strong quantitative and biophysical reasoning, cross-disciplinary communication, and experience with biological, structural, or biophysical datasets.

Own and improve software requirements for semiconductor manufacturing solutions. Shape product features, translate customer and stakeholder needs into clear specifications, facilitate discussions across customers, engineers, and developers, and support collaboration between global teams. Build expertise in semiconductor manufacturing and software development while applying AI tools, agents, and workflow automation to improve requirements engineering and documentation.

Build and improve Firefox WebCompat internal dashboards, reporting tools, and broken-site screenshot capture pipelines. The role involves creating reliable data views for triage and metrics, automating screenshot workflows, collaborating with WebCompat and QA teams, and translating reporting needs into effective tooling. Familiarity with WebCompat workflows, Bugzilla, screenshot automation, and machine learning is preferred.

Lead frontier research in on-device AI inference, model routing, autonomous research pipelines, and real-world evaluations. Design and execute experiments involving speculative decoding, quantization, distillation, reinforcement learning, and other efficiency techniques. Own the research agenda, influence technical direction, document reproducible findings, and translate novel insights into practical performance improvements under device constraints.

Research and develop GPU-accelerated techniques for AI, deep learning, machine learning, and HPC workloads. Analyze and optimize complex algorithms across CPU and GPU architectures, perform low-level performance tuning, publish optimization techniques, and collaborate with hardware, software, research, and developer communities to influence future architectures and programming models.

Lead the Production Inference team of research scientists and ML engineers. Own people development, technical direction, research and development roadmaps, architecture decisions, and model-serving reliability, efficiency, latency, and cost performance. Partner with research, infrastructure, product, and commercial stakeholders; guide production deployment and lifecycle improvements; and recruit technical talent. The role emphasizes leadership and technical reviews rather than hands-on coding.

Develop advanced AI and NLP solutions for e-commerce query understanding, including intent detection, entity resolution, classification, query expansion, and recommendation. Build ML training pipelines and real-time inference services, collaborate with researchers and engineers, shape technical direction, apply software engineering best practices, and mentor junior colleagues.

Develop advanced AI and NLP solutions for e-commerce query understanding, including query rewriting, intent detection, entity resolution, classification, expansion, and filter recommendation. Build ML training and validation pipelines, deploy real-time inference services, collaborate with researchers and engineers, and contribute to the technical vision for search query science. Apply software engineering best practices and work independently on large-scale search challenges.

Leads Tide’s engineering organization for fraud, financial crime, and ongoing monitoring platforms. Owns engineering strategy, decisioning infrastructure, model lifecycle, agentic AI deployment, and global platform scalability. Partners with risk, compliance, operations, product, and data science leaders to improve fraud detection, reduce losses and monitoring costs, automate workflows, and meet regulatory requirements across multiple markets.