Top Artificial Intelligence Jobs in Valencia

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

Designs enemies and NPC behaviors for a high-mobility multiplayer action game. Creates design specifications and technical documentation, collaborates with designers and engineers on scripting and behavior trees, analyzes player data and feedback to improve AI, coordinates dependencies with producers, and pitches new AI concepts to leadership.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

Own lifecycle marketing campaigns across email, push and in-app channels: write copy, build automated customer journeys and behavioral triggers, run A/B tests, analyze performance with product analytics, and work with Product and Tech to optimize activation, retention and monetization.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Partner with government customers and partners to design, prototype, and deploy AI solutions (computer vision, LLMs, generative/agentic AI). Create technical collateral, lead hands-on trials and deployments using NVIDIA hardware/software, and advocate product requirements across product, sales, and engineering teams.

Lead product direction and execution for major areas of an AI Neobank app. Define roadmaps, priorities, standards and metrics across squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement across payments, savings, investing, insurance and travel features.

Own product areas in BJAK's AI neobank app: define problems, scope, metrics, roadmaps; work with engineering, design, data, compliance; launch features, monitor performance, iterate, and balance speed, UX, business impact, and operational complexity.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Lead development and evaluation of speech language models and automated scoring systems for learner and hiring applications. Build, fine-tune, and monitor acoustic, ASR, speech-to-speech, and LLM-based models; design experiments and validity analyses; perform error analysis and rater reliability studies; integrate with product and UX; apply responsible AI practices and support production monitoring and governance for high-stakes assessments.

Lead R&D and implementation of ML and statistical learning capabilities for English language learning products. Design, build, and monitor production-ready models (NLP, speech, psychometrics), document and present results, mentor junior colleagues, collaborate with engineering, and publish research.

As a Senior AI/ML Engineer, you'll design and develop AI solutions, build production-level ML models, and implement MLOps practices. You'll work with a multidisciplinary team on cutting-edge projects, focusing on AI-powered software and deep learning techniques.

Design, implement, and evaluate applied research projects to improve alignment, safety, and evaluation of LLM-based guardrails. Work on experiments (e.g., synthetic data quality metrics, explanations for blocked content), deliver results with mentor guidance, and present findings to the team to shape product capabilities.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.