Top Machine Learning Jobs in Berlin

The Security Engineer will support detection and response through incident response leadership, threat hunting, detection engineering, threat intelligence, and complex investigations. Responsibilities include building automated security tooling, AI-driven alert triage, security intelligence pipelines, detection-as-code, cloud security capabilities, and autonomous security solutions. The role also involves on-call incident command, attacker-focused research, security education, and cross-functional collaboration.

Guide customers through Zscaler’s Zero Trust adoption as a post-sales technical contact. Build stakeholder relationships, provide technical guidance and best practices, develop integration strategies, coordinate complex escalations, identify adoption risks, and collaborate on mitigation plans. The role also contributes to customer success playbooks and best-practice resources while advising on cloud, networking, security, and AI-powered solutions.

Build and own scalable machine learning and generative AI systems for customer-facing and operational healthcare use cases. Develop end-to-end pipelines for training, evaluation, deployment, monitoring, and continuous improvement. Collaborate with product, engineering, data, and business teams; define AI technical roadmaps; and ensure governance, model quality, versioning, observability, and compliance in a regulated environment.

Own the roadmap and execution of demand management and customer choice products that balance fleet capacity with customer ordering. Partner with Engineering, Data Science, Operations, and regional teams to define requirements, prioritize backlogs, launch features, evaluate experiments, and improve conversion, cancellations, and fleet saturation. The role involves AI-driven configurations, marketplace logistics, operational alignment, and managing complex product initiatives from concept through delivery.

Customer Data Scientist responsible for tuning AML and fraud detection models using live customer transaction data. Investigates detection performance, alert quality, missed cases, and pattern drift; validates model changes through backtesting and sample review; and presents findings to compliance, risk, and data science stakeholders. The role translates model performance into business value, documents regulatory defensibility, partners on account strategy, and feeds customer insights into product and model improvements.

Own customer onboarding from initial execution through post-launch iteration. Validate camera and input data quality, identify edge cases, maintain datasets, document process variance, track success metrics, and communicate progress and blockers. Improve onboarding workflows through checklists, documentation, templates, and tool feedback. Coordinate with installation teams to ensure camera setups support reliable, consistent AI onboarding across stations.

Own customer AI onboarding from initial execution through go-live and continuous improvement. Validate camera and input data quality, identify edge cases, maintain datasets and SOPs, track success metrics, communicate blockers, and coordinate installation teams to ensure reliable, consistent deployments. Improve onboarding workflows through documentation, checklists, templates, and tool enhancement recommendations. The role requires structured execution, strong communication, process discipline, technical systems comfort, and preferably manufacturing or process experience.

Design and deploy AI and machine learning models, integrate LLMs and generative AI into existing architectures, analyze and optimize large datasets, and collaborate with product management, data science, and software engineering teams.

Design and deploy AI and machine learning models, integrate LLMs and generative AI into existing architectures, and analyze large datasets to optimize model performance. Collaborate with product management, data science, and software engineering teams. The role requires strong hands-on machine learning, deep learning, or NLP experience, Python and modern ML framework proficiency, quantitative education, and business fluency in English and German.

Lead data architecture, modeling, and engineering for high-volume event and analytics datasets. Build reliable batch and streaming pipelines, standardize data products, and partner with Applied Scientists to productionize statistical and machine learning models. Establish data quality, testing, monitoring, and performance standards while optimizing reliability and cost. Mentor engineers and drive large-scale architecture initiatives across multidisciplinary teams.

Build and deploy statistical, predictive, and AI models for pricing, matching, growth, and product decisions. Develop LLM and RAG features, evaluate AI quality, design experiments, analyze user funnels, and improve supply-demand matching. Partner with performance marketing on targeting and attribution, define KPIs, create AWS QuickSight dashboards, document work in Jira, and communicate recommendations through implementation.

Lead the Perception and Autonomous Systems team while remaining hands-on in C++ development. Set architecture and engineering standards for real-time software, ROS2 nodes, computer vision pipelines, and autonomous vehicle systems. Guide machine learning model integration, inference optimization, data pipelines, and deployment workflows. Mentor engineers, coordinate across teams, support SOTIF considerations, and participate in field testing and experimental validation.

Own end-to-end client projects measuring soil carbon, including scoping, sampling design, spatial analysis, estimation, reporting, and presentations. Develop reproducible Python and geospatial workflows, improve shared codebases and methodologies, validate models, assess uncertainty, and translate project learnings into product improvements. Collaborate with business development, data science, engineering, and clients while applying rigorous science to real agricultural and environmental data.

Build and ship core AI features for an AI meeting assistant, including code, prompts, and optimized AI/ML pipelines. Use user feedback and analytics to improve product metrics and experience. Collaborate closely with founders and the team, experiment rapidly, and ensure reliable, high-quality product releases in a fast-paced startup environment.

Owns the end-to-end development of machine learning solutions for audience targeting, real-time bidding, campaign optimization, budget allocation, forecasting, and experimentation. Designs and deploys production models, establishes monitoring and governance, collaborates with engineering and product teams, influences technical roadmaps, and mentors data scientists within a large-scale advertising platform.

Evaluates and develops AI and computational science manuscripts for publication, makes editorial decisions, shapes open-science policies, and advises authors on clarity and reporting standards. The editor recruits and supports external editors, commissions reviews and commentary, attends conferences and research meetings, visits institutions, and promotes published work through press releases and research highlights. The role requires PhD-level machine learning or related research experience, strong scientific judgment, broad interdisciplinary knowledge, and excellent English communication skills.

Build and deploy machine learning models predicting user value, engagement, and reward behavior. Run A/B tests on offers, rewards, and ranking; develop and maintain supporting data pipelines; and take ambiguous problems from ideation through production. The role requires strong Python and SQL skills, independent problem-solving, analytical judgment, and a demonstrated drive to exceed expectations. Kaggle, hackathon, or self-built production-system experience is a bonus.

Build and operate production data pipelines for patent, research, and company data using Python and Dagster. Model messy real-world data, develop agent evaluation harnesses, implement data quality monitoring, and deliver reliable data to product stores and APIs. Collaborate with product and ML teams, evaluate new data sources, troubleshoot source failures, and define measurable standards for data quality. The role involves working with large, noisy datasets and shaping how AI agents support data processing.

Lead SAP’s AI technology strategy and enterprise architecture for AI-native systems. Shape architecture standards, reviews, executive communications, and responsible adoption of LLMs, RAG, and multi-agent systems. Establish practices for agentic orchestration, context engineering, evaluation, benchmarking, AI quality assurance, CI/CD, MLOps, and secure architecture. Mentor senior architects, align technology and application teams, and drive AI adoption across SAP product lines.

Leads LawZero’s independent Evaluations Team, defining evaluation strategy, building benchmarks, datasets, environments, tooling, and red-teaming programs for Scientist AI and frontier LLM guardrails. Oversees capability, safety, explainability, honesty, adversarial robustness, and causal-mechanism evaluations. Builds an 8–10-person team, scales evaluation infrastructure, communicates results through technical reports and publications, and represents LawZero with AI safety institutes, collaborators, funders, and policymakers.

Develop and maintain high-performance Rust and Python simulation and validation frameworks for autonomous air systems. Build Software-in-the-Loop and Hardware-in-the-Loop environments, automated testing, fault injection, regression coverage, and deployment infrastructure. Collaborate with autonomy and systems teams, optimize real-time simulation performance, maintain documentation and benchmarks, review code, mentor colleagues, and improve reliability, safety, and operational performance.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Leads Cafeyn’s customer data and data science strategy, including analytics, AI-powered product innovation, data monetization, predictive modeling, experimentation, governance, and scalable data foundations. Manages multidisciplinary data teams, partners with executive, product, marketing, business, and engineering stakeholders, and drives customer engagement, retention, revenue growth, and data adoption.

Lead scientific delivery for partner materials-discovery projects, from scoping through results. Apply simulation, machine learning, data-driven optimization, or experiments; design discovery campaigns; validate models against experiments; and translate between experimental, computational, and ML teams. Build reliable automated workflows, improve data pipelines, prepare technical reports, and relay partner feedback to technical teams. The role requires deep expertise in materials science, chemistry, physics, chemical engineering, or related fields, along with Python and Git proficiency.

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.