Top Machine Learning Jobs in Berlin

Manage full-cycle technical recruiting for AI, machine learning, and AI safety teams. Responsibilities include sourcing niche and passive talent, market mapping, talent-pool development, candidate engagement, stakeholder collaboration, offer negotiation, candidate experience, and recruitment-process improvement. The role requires strong knowledge of AI research recruiting, bilingual German and English communication, and the ability to operate effectively in a fast-paced startup environment.

Provides proactive executive support to the CEO through agenda and travel management, time and priority optimization, stakeholder communication, project coordination, logistics, and problem-solving. The role involves frequent international travel, varying time-zone hours, confidential work, and ad hoc tasks. Candidates should be organized, adaptable, tech-savvy, communicative, fluent in English and German, authorized to work in Germany, and able to travel internationally on short notice.

Build and operate production systems for optimization, forecasting, machine learning, and generative AI. Design scalable data and ML pipelines, enable model training and evaluation, productionize deployments, and implement monitoring, dashboards, alerts, and runbooks. Partner with scientists, product managers, engineers, and commercial stakeholders to create reliable systems while strengthening engineering standards for testing, CI/CD, observability, and operational ownership.

Build scalable data warehouses, cloud infrastructure, AI workflows, and data-driven applications. Integrate data from multiple systems, ensure accessible and consistent data, evaluate AI use cases, and collaborate with analytics, product, and software engineering teams in an agile environment.

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 and mentor a team of Applied Scientists and AI/ML Engineers to design and deliver production-grade AI solutions across structured CRM and unstructured data. Drive technical strategy for agentic workflows, RAG/vector DB systems, and real-time reasoning. Partner with AI Platform on LLMOps and cloud infrastructure, act as a technical authority, and contribute as a high-impact individual contributor to ship customer-focused AI products.

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.

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.

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 data strategy, architecture, governance, quality, compliance, and technology adoption. Translate stakeholder needs into scalable data initiatives, support advanced analytics and AI/ML, promote data literacy, and guide cross-functional teams toward data-driven decision-making.

Build quantum sensing and magnetometry capabilities using NV centres, optically pumped magnetometry, modelling, experimentation, and machine learning. Characterise sensing systems, develop digital twins, improve calibration, investigate biomagnetic and industrial applications, support patents and commercialisation, publish research, pursue grants, collaborate with faculty, and supervise student projects. The role combines hands-on experimental physics with modelling and applied outcomes.

Serve as a senior pharmacovigilance domain expert bridging case-processing operations, product strategy, and AI automation. Guide safety workflow design, evaluate AI outputs, define future PV operating models, engage early-adopter customers, and create complex scenarios for platform stress testing. Collaborate with product, engineering, and executive customer teams to ensure accurate, compliant, and effective safety automation.

Lead the architecture, development, deployment, and operation of production-grade machine learning systems for product recommendations. Build pipelines for candidate generation, ranking, personalization, evaluation, monitoring, and continuous improvement. Translate ambiguous product needs into scalable solutions while balancing model quality, latency, reliability, and maintainability. Provide technical leadership through architectural decisions, mentoring, design reviews, and engineering standards, while collaborating with data, product, engineering, and business stakeholders.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Develop scalable forecasting and planning software for Zalando’s logistics network. Build maintainable Python systems that optimize capacity allocation, support warehouse and transport operations, and manage the full development lifecycle from design through maintenance. Collaborate with subject matter experts and cross-functional teams to resolve operational challenges, gather requirements, and deliver milestones. The role also involves machine learning, SQL, data warehouses, ETL, pipeline orchestration, Databricks, testing, documentation, and agentic development practices.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Conduct advanced machine learning research and translate state-of-the-art generative AI, recommendation, embedding, and ranking methods into high-throughput production systems. Build multi-stage recommendation pipelines, optimize large models for sub-millisecond latency, and develop user and ad embeddings using PyTorch and GPU infrastructure. Lead architectural discussions, mentor junior scientists, and guide research direction across systems serving hundreds of millions of users.

Develop and productionize reinforcement learning, optimization, and control-theory algorithms for real-time bidding, auctions, budget pacing, and allocation. Translate research into sub-millisecond, high-throughput systems using large-scale Bayesian inference and behavioral data. Lead technical architecture, define mathematical objectives with product and engineering teams, mentor junior scientists, and guide research direction across adtech products serving hundreds of millions of users.

Owns the operational lifecycle of LLM, GenAI, and RAG systems, including deployment, monitoring, evaluation, observability, versioning, cost optimization, and production reliability. The role builds ML platform capabilities for regulated clinical workflows, ships agentic or RAG pipelines, establishes human-feedback evaluation loops, and partners closely with the existing MLOps team.

Leads machine learning research and engineering teams focused on AI safety and the Scientist AI approach. Responsibilities include managing and mentoring researchers and engineers, hiring and onboarding technical talent, directing complex cross-functional projects, optimizing reproducible research workflows, advancing foundation-model research, and translating scientific concepts into large-scale engineering execution.

Conduct foundational AI safety research with Yoshua Bengio and the LawZero team. Develop theoretical frameworks, safe AI architectures, and probabilistic safety guarantees under finite data and compute constraints. Analyze existing research, formalize safety requirements, guide empirical experiments, publish scientific work, and communicate complex ideas to technical and non-technical audiences. The role focuses on building Scientist AI systems that are capable, transparent, and safe by design.

Design and build scalable ML platform tooling for feature engineering, model training, experimentation, monitoring, and serving. Simplify data scientist workflows, standardize ML infrastructure, and create reliable self-service components. Partner with data scientists and data platform teams to translate workflow challenges into platform improvements, while mentoring users and driving adoption across the organization.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Designs, develops, and deploys production-grade machine learning systems focused on LLMs, Generative AI, and deep learning. Responsibilities include building scalable ML libraries and research workflows, optimizing distributed training and inference, improving experiment tracking and model lifecycle management, and bridging research prototypes with robust implementations. The role requires collaboration with data science and platform teams, strong software engineering practices, cloud and MLOps expertise, and mentoring across the technical team. Biomedical, clinical, or omics data experience is preferred.