Senior Machine Learning Engineer

About The Role 💻

We are looking for a Senior Machine Learning Engineer to build and operate production Machine Learning (ML), Large Language Models (LLMs), and agent systems that deliver transparent, accurate sustainability data to our customers. You will own the model lifecycle—data pipelines and features, model training and evaluation, deployment, and monitoring—while driving MLOps best practices. You’ll work closely within the Data Science and with the Engineering team to turn prototypes into scalable, reliable products.

In this role, you will design and run data pipelines and feature stores; train, evaluate, deploy, and monitor models at scale; and establish a strong MLOps foundation with CI/CD, containers, orchestration, experiment tracking, and a model registry. You will ship model capabilities with rigorous evaluation and guardrails, ensuring robust observability and quality. You’ll collaborate across teams to translate business needs into scalable solutions, optimize cost and performance, raise engineering standards, mentor others, influence platform roadmaps, and explore emerging technologies.

What You’ll Be Doing 🚀

Working as a Senior Machine Learning Engineer, you will be responsible for:

• Designing, implementing, and scaling data pipelines and feature stores to power ML and LLM use cases.
• Building, training, and rigorously evaluating models using offline tests and online experiments.
• Deploying models and services with CI/CD, containers, and orchestration frameworks.
• Operating and monitoring production ML/LLM systems with strong observability and alerting.
• Ensuring transparency, accuracy, and auditability of sustainability data through validation and lineage.
  Mentor teammates and implement best practices to accelerate the ML lifecycle.
• Stay current with ML/LLM/agents research and translate it into pragmatic production solutions.

Location 🌍

The role is based in Madrid, Spain

Way of Working: Remote

What You’ll Need 👀

• 3+ years of industry experience building and operating ML systems in production.
• Strong Python and software engineering fundamentals (testing, code review, design, performance).
• Proven experience deploying and operating ML models on cloud with containers and CI/CD. Experience with AWS and/or Kubernetes is a plus.
• Hands-on MLOps experience: orchestration (e.g., Airflow), experiment tracking (e.g., MLflow), monitoring (e.g., LangFuse), and model registry.
• LLM/Agent production experience: prompt/retrieval design, vector databases, tool use/agents, evaluation and safety/guardrails. Exposure to frameworks (e.g., LangChain/LangGraph/LlamaIndex) and providers (OpenAI/Anthropic/Hugging Face).
• System design and architecture: deep understanding of tradeoffs (e.g., latency/throughput vs cost, micro-services vs monoliths, centralization vs federation, etc.).
• Data engineering skills: SQL, data modeling, batch/stream processing; building reliable ETL/ELT pipelines.
• University degree in CS/EE/STEM or related field
• Self-starter, able to take ownership and initiative, with high energy and stamina
• Decisive and action-oriented, able to make rapid decisions even when they are short of information
• Highly motivated, independent and deeply passionate about sustainability and impact
• Excellent oral and written English communication skills (minimum C1 level-proficient user)

Nice To Have ✨

• 5+ years of industry experience building and operating ML systems in production.
• Experience in a start-up
• MSc/PhD degree in CS/EE/STEM or related field
• Advanced LLM patterns: function/tool calling, multi-agent workflows, structured output, constrained decoding, evaluation harnesses, hallucination mitigation.
• Experience with fintech/data products or sustainability/ESG domains.

What We Offer 🥁

• Competitive compensation, both in terms of base salary as well as equity plans that enable to you to share in our success
• Flexibility in ways of working both in terms of your schedule as well as your location, whether you prefer to work from home, the office, or abroad with access to a global network of co-working spaces
• Generous paid time off schemes, including vacation, sabbatical, religious observance and compensation days
• Meaningful benefits including private healthcare coverage, fitness and wellness programs covered through Wellhub, working-from-home allowances to help you set up your home office and cover monthly expenses
• Professional development with annual training budget for conferences, courses, certifications and access to top market e-learning platforms
• Collaborative environment with multiple offices around the globe, regular team activities and events as well as employee-led resource groups