Top Machine Learning Jobs in Valencia

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

As a Machine Learning Engineer, you'll apply state-of-the-art techniques to projects, enhance customer experiences, and communicate results effectively.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Senior Java/Python developer to design and implement production-grade backend microservices, integrate machine learning models, improve build processes, follow secure coding and Scrum practices, write technical documentation, and work with Git-based workflows. Role is full-remote with one-week onboarding in The Hague and requires a valid work permit.

Design, develop, and maintain NLP/ML/AI software and architectures. Train and optimize ML/DL models, perform data cleaning and mining, build anomaly detection, improve machine translation, interact with data stewards, and coordinate implementation using agile practices.

Design, develop, and deploy cognitive search solutions using NLP and ML. Integrate search with existing systems and bots, build and maintain data pipelines, optimize relevance and ranking, monitor performance, and apply semantic search, embeddings, and vector databases to improve user experience.

The Senior AI & Computer Vision Software Engineer will work on innovative software solutions, bridge research and production, and mentor others while working in an Agile environment.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

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.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

As a Senior Product Manager, you will define product vision, analyze market trends, manage product KPIs, and collaborate with teams to deliver innovative solutions.

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.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Own and ship core ML subsystems for a proactive long-horizon AI product: data prep, training, evaluation, inference, and reliable production deployment. Debug model and system failures using production signals, optimize for latency/cost/reliability/safety, iterate rapidly, and mentor other ML engineers while collaborating cross-functionally to deliver measurable user impact.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.