Top Artificial Intelligence Jobs in Cologne

Deliver live workshops and applied labs on AI-assisted visual, video, document, and interactive content production. Demonstrate generative AI workflows, guide learners through brand-aligned assets, automate recurring tasks, and support vibe-coded interactive projects. Provide structured feedback, collaborate on course materials, and continuously improve content based on learner feedback and evolving AI tools.

Supports Account Managers and client teams by coordinating delegated tasks, tracking deadlines and follow-ups, preparing updates, managing workflows, communicating in German and English, reviewing AI-generated work, and improving repeatable account processes. The role requires strong client judgment, attention to detail, prioritization, technical adaptability, and hands-on use of AI tools and agents in a remote, async environment.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

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.

Own the technical design and production delivery of multi-component AI systems. Build evaluation frameworks for nondeterministic behavior, data pipelines, feedback loops, model-tool boundaries, and agent harnesses. Optimize latency and cost through streaming, caching, routing, and prompt design. Develop internal tooling, assess external models and datasets, support APIs and SDKs, mentor engineers, and participate in 24/7 on-call operations.

Own the AI cost visibility product end to end, developing Go backend services, APIs, data processing, and TypeScript/React interfaces. Translate AI spending data into clear FinOps experiences, work closely with customers and product needs, and use AI throughout the engineering workflow. Lead feature design, implementation, release, monitoring, and iteration while setting engineering standards, mentoring peers, and building scalable cloud-based SaaS capabilities.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend and usage data into clear FinOps experiences, while driving architecture, reliability, monitoring, and iteration. Collaborate closely with customers and product stakeholders, use AI throughout the engineering workflow, mentor peers, and shape product direction in an ambiguous market.

Own AI cost visibility features end-to-end, from customer problem discovery and system design through implementation, release, monitoring, and iteration. Build scalable Go backend services, APIs, and TypeScript/React interfaces that process and present cloud AI spend data. Apply AI throughout the engineering workflow, contribute to product direction, understand FinOps and AI economics, mentor peers, and uphold reliable engineering practices.

Owns the end-to-end data processing and dataset lifecycle for robotics AI/ML systems. Designs scalable pipelines that convert raw sensor recordings into reliable training, validation, benchmarking, and regression datasets. Implements data quality checks, versioning, lineage, reproducibility, distribution, and infrastructure integration across cloud and compute environments. Develops dashboards monitoring data quality, scenario coverage, labeling, and ML readiness while collaborating with AI/ML, perception, validation, and platform engineering teams.

Build and operate production AI platforms, including model deployment, inference services, monitoring, observability, release automation, lifecycle management, and cost optimization. Troubleshoot application, infrastructure, and deployment issues while developing reliable, scalable patterns for batch and real-time AI workloads. Partner with AI, cloud, infrastructure, and application teams to move models from prototypes into secure production environments.

Build and operate Typeform’s internal AI Operations capability for R&D. Identify and prioritize AI opportunities, discover workflows, design and deliver AI-native solutions, and manage their lifecycle after launch. Responsibilities include improving enterprise AI tools, coordinating access and governance, evaluating reliability and security, supporting automations and agents, measuring adoption and value, and partnering with Product, Engineering, IT, InfoSec, Finance, and other teams.

Citrine Informatics invites applicants to submit resumes for general opportunities. They review applications periodically for potential new roles in AI-driven materials and chemicals.

Leads the deployment, optimization, validation, and scaling of AI/ML workloads on embedded and edge platforms. Develops high-performance C/C++ software and reusable pipelines for computer vision, sensor fusion, and real-time decision-making. Optimizes inference across heterogeneous compute architectures while managing latency, memory, power, reliability, and safety constraints. Establishes benchmarking, testing, CI/CD, and production-readiness workflows, collaborating with hardware, robotics, safety, systems, and quality teams.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.

Design, build, and operate AI agents, LLM integrations, retrieval pipelines, MCP servers, APIs, and workflow automations connecting Salesforce and other go-to-market systems. Deploy production-grade AI capabilities on GCP with validation, evaluations, access controls, observability, audit logging, and human escalation. Partner with revenue teams to identify automation opportunities, deliver reliable solutions, document workflows, mentor teammates, and enable business users to operate automations independently.

Lead and grow a distributed AI engineering team to build and operate scalable production AI platforms, model-serving, inference and training environments, CI/CD, observability, and integrations. Drive architecture, delivery, vendor evaluation, and operational readiness while partnering with global stakeholders to adopt AI across media and localization workflows.

Contribute to AI data projects through language data annotation, evaluation, and instruction creation. Work remotely with flexible hours to help develop more inclusive and capable AI systems. Applicants must be native or near-native Simplified Chinese speakers living in China, Canada, the United States, or Malaysia, with fluent English, comfort using digital tools, strong attention to detail, and curiosity.

Join Welo Data’s remote contributor network for future AI data projects involving text, image, or audio annotation; search, ad, and chatbot evaluation; prompt creation; and cultural or linguistic accuracy review. Contributors must have native or near-native Twi fluency, English proficiency, digital tool comfort, and strong attention to detail. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Join Welo Data’s Arabic contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. Projects vary in duration and complexity, with scope, timelines, and compensation provided before participation. Candidates need native or near-native Arabic fluency, English proficiency, digital tool familiarity, and strong attention to detail.

Join a remote Albanian-language AI contributor network supporting flexible annotation, evaluation, and prompt-creation projects. Work may include labeling text, images, or audio; assessing search results, advertisements, and chatbot responses; creating prompts for large language models; and ensuring cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Freelance remote contributors support AI language-data projects through annotation, evaluation, prompt creation, and Korean-to-Chinese machine-transliterated lyric review. Responsibilities include assessing meaning, fluency, naturalness, and accuracy; rating quality against defined criteria; correcting errors; and providing feedback to improve AI systems. Projects are task-based, flexible, and dependent on availability.