Top Artificial Intelligence Jobs in Amsterdam

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The Director of Business Development will drive revenue through creating strategies for new business, managing client relationships, and overseeing sales processes while ensuring high service standards.

Build production AI-native systems for BJAK's AI Neobank: AI agents, LLM apps, RAG systems, tool-calling, and automation to improve onboarding, KYC, support, risk review, CRM, and operations. Collaborate with product and engineering teams to ship, evaluate, and operate reliable, secure, cost-effective AI workflows that minimize hallucination and scale manual processes into dependable systems.

Research, train, fine-tune, and productionize speech-to-text models across 24+ European languages. Build reproducible experimentation infrastructure, design and run experiments, evaluate architectures and tooling, and own model roadmap from experiment to deployment on an in-house GPU cluster.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

As a Principal Forward Deployed Engineer, you'll deploy JetBrains Central in enterprise environments, implement AI governance, mentor teams, and advise customers on AI-native development best practices.

Lead company-wide AI transformation: identify high-leverage opportunities, build and ship agentic tooling and automations, treat solutions like products, prove ROI, raise company AI fluency, and sit on the Senior Leadership Team to scale and hire a team.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Teach and mentor experienced engineers transitioning to system architecture: run live sessions and office hours, review and assess architecture deliverables, run architecture review boards, provide deep technical feedback, record instructional videos, conduct mock interviews, and lead supporting instructors.

Author and iterate technical curriculum for experienced engineers transitioning to system architecture: refine syllabus, set measurable learning objectives, produce AI-assisted lessons, design hands-on projects, write rubrics and reviewer guides, and collaborate with LX designers to finalize content over a 4–6 month, part-time engagement (15–20 hrs/week).

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Review, evaluate, and annotate AI-generated text, image, audio, and video content for quality and guideline compliance. Perform quality checks, identify edge cases, contribute to dataset creation, participate in calibration activities, and provide feedback to improve annotation guidelines and workflows.

The role involves evaluating AI outputs, generating content, and ensuring cultural relevance. Strong analytical skills and local insights are essential to ensure quality in AI training projects.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.