Top Machine Learning Jobs in Berlin

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.

Lead CRM lifecycle strategy and execution to maximize retention and monetization. Design algorithmic personalization, run rigorous incrementality experiments, partner with Data/Engineering on real-time event schema, and build/operate omnichannel campaigns (Email, Push, SMS, In-App) including API-triggered flows.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Design, implement, and test automation and robotic systems for machine vision and industrial applications. Develop embedded product designs and manufacturing processes, collaborate with R&D team, analyze test data, and maintain inspection and maintenance plans.

Design, build, and operate production AI systems for tax advisory: document intelligence, RAG over tax/legal knowledge, data pipelines, prediction and anomaly detection, and monitoring/evaluation. Collaborate with engineers and domain experts to deploy reliable, explainable AI that reduces manual tax work.

Lead a cross-functional team of Data Scientists, MLOps Engineers, and Product to deliver horizontal data and ML products (including LLM-based systems). Own roadmap, hiring, performance, operationalization, adoption, and production reliability. Drive stakeholder partnerships, raise production quality standards, and balance exploration with scalable, business-impacting solutions.

Own machine learning models from handoff through production, including packaging, deployment, serving, monitoring, drift detection, alerting, and incident response. Review model design and evaluation quality, identify leakage and temporal validation issues, and improve the shared ML platform. Establish engineering standards, ensure feature-store parity, and mentor or review colleagues while operating production AWS infrastructure.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

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 Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

The Consulting Manager will lead AI transformation programs, work with executives on strategy, drive business growth, represent the company in discussions, and mentor teams.

As an Enterprise AI Adoption Manager, you will guide companies in adopting AI, manage enterprise accounts, drive AI transformation, and work closely with engineering and product teams to deliver value.

The Senior Data Product Manager drives product maturity and strategic growth at Seqana by translating user needs into technical requirements, overseeing product lifecycles, and exploring market opportunities.

Lead the development of large language models for marketing technologies, optimize AI solutions, and mentor junior team members to drive innovation and quality.

The AI Business Development Specialist will identify business opportunities, build client relationships, and promote AI solutions in various sectors, with a focus on the MENA region.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Lead finance transformation and pre-sales solution architecture for Workday Financials. Design end-to-end multi-country FIN solutions, lead workshops, drive executive alignment, enable sales, and shape post-go-live roadmaps using AI/automation to modernize finance operations.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Build production-grade ML and MLOps infrastructure for cloud-native environments. Responsibilities include orchestrating Kubeflow pipelines, training and fine-tuning GPU-based models and LLMs, tracking experiments with MLflow, developing Python tooling, processing large datasets, and maintaining CI/CD, testing, security, and clean-code standards within Kubernetes.

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.

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.