Top Machine Learning Jobs in Spain

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams to enhance user experiences, and implement AI techniques to improve app performance.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, develop end-to-end ML pipelines, and implement MLOps practices while collaborating with product and engineering teams to enhance AI products.

Develop, train, and validate state-of-the-art computer vision models (detection, segmentation, classification). Write production-ready Python/PyTorch code, optimize models (quantization, pruning) for inference with TensorRT/OpenVINO/ONNX, manage large datasets and augmentations, build CI/CD (GitHub Actions), containerize with Docker, collaborate on research and open-source, and produce documentation and community support.

Design, develop, deploy, and maintain scalable AI/ML systems and pipelines. Apply MLOps/LLMOps/AgentOps best practices, run large-scale experiments, build engineering infrastructure for batch and real-time workflows, and integrate models into business applications with monitoring and APIs. Collaborate with scientists, engineers, and product teams to deliver end-to-end solutions using Python, MLflow, Azure, and Databricks.

Senior backend developer building production-grade microservices in Java and Python, integrating machine learning models (e.g., TensorFlow), working with cloud, Docker/Kubernetes, Git, and Agile/Scrum. Remote role with initial onboarding in The Hague; requires work permit and fluency in English.

Remote AI/ML Architect to assess current AI/ML landscape, document requirements, and propose SDLC, governance, and deployment approaches. Provide recommendations to architecture and ICT management. Onboarding in the Netherlands (first week). Candidates must be based in Europe with a valid work permit.

Design and document the current AI/ML landscape, capture requirements, propose SDLC approaches and tooling, and advise architecture and ICT management on AI/ML solution design, governance, and deployment. Remote role with first-week onboarding in the Netherlands.

As a Senior AI/ML Engineer, you will develop and deploy AI initiatives, focusing on ML solutions and ensuring robust software integration. Responsibilities include coding, MLOps implementation, Agile delivery, and collaboration with stakeholders to translate business needs into technical solutions.

As an AI Machine Learning Engineer, you'll develop and deploy AI solutions, implement MLOps workflows, and partner with stakeholders to translate business challenges into technical requirements.

Design, build, and deploy AI/ML features for conversation intelligence: develop end-to-end pipelines, implement model development and production inference, instrument services with metrics and telemetry, participate in on-call rotations and progressive rollouts, and collaborate across product and engineering to maintain model performance and reliability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Design, develop, and deploy ML-powered data products; own full ML model lifecycle from framing and experimentation to production deployment, monitoring, and iteration; collaborate with Data Analysts and Data Engineers to ensure robust pipelines, feature availability, and measurable business impact.

Build production-grade machine learning systems that connect data pipelines to user-facing software. Design robust system architecture, implement model inference, ship resilient full-stack/cloud services, and collaborate on UX-driven solutions using Python, APIs, and AWS.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

Develop and ship features for Junie across CLI and model integrations, experiment with model setups and ensembling for cost-quality tradeoffs, build and test autonomous agent behaviors for long-term maintenance, and own projects from research to production while using Junie daily to iterate on quality and UX.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.