Job Title: Senior Machine Learning Engineer (or Senior ML Engineer – LLMs & Agentic AI / Scientific & Engineering Data / Computer Vision & Anomaly Detection) Location: Barcelona, Spain (hybrid or onsite; some roles with relocation support) Company Type: Multinational tech/engineering firm (e.g., Keysight Technologies), research center (e.g., BSC-CNS), media/data company, or AI startup Employment Type: Full-time.
About the Role We are seeking a Senior Machine Learning Engineer to lead the design, development, and deployment of state-of-the-art ML solutions that drive innovation and business impact. You will own end-to-end ML workflows from data ingestion and model training to production scaling and monitoring while tackling complex challenges in areas like generative AI, LLMs, agentic systems, anomaly detection, computer vision, physics-informed ML, or scalable NLP/language technologies. This senior role involves technical leadership, mentoring, and ensuring ML systems are robust, efficient, and production ready.
Key Responsibilities
- Architect and implement advanced ML models and pipelines (e.g., fine-tuning LLMs, building agentic AI, computer vision for anomaly detection, or physics-driven optimization).
- Develop scalable data processing pipelines for high-volume or unstructured data (e.g., images, text, sensor/measurement data).
- Optimize models for performance, efficiency, and cost (e.g., quantization, distributed training, inference acceleration).
- Deploy and maintain ML systems in production using MLOps best practices (e.g., CI/CD for ML, monitoring, retraining, A/B testing; tools like MLflow, Kubeflow, or cloud-native services).
- Collaborate with domain experts (e.g., simulation/measurement engineers, researchers), product teams, and software developers to integrate ML into real-world applications and decision-making.
- Lead evaluations, benchmarking, and experimentation to validate model quality and iterate on improvements.
- Mentor junior ML engineers and data scientists; establish engineering standards, code reviews, and best practices for ML development.
- Troubleshoot production issues, ensure reliability/safety, and contribute to ML lifecycle management (including green IT practices where applicable).
Required Qualifications
- 5–8+ years of hands-on experience in machine learning engineering, with proven production deployment of ML systems (senior roles often require 3+ years in production ML).
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers); experience with C++/Java for performance-critical components a plus.
- Deep expertise in modern ML techniques (e.g., LLMs, generative AI, agentic systems, NLP, computer vision, anomaly detection, or domain-adapted models).
- Experience with MLOps tools, cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and scalable infrastructure.
- Solid software engineering fundamentals: clean code, testing, version control, APIs, observability, and distributed systems.
- Bachelor’s/Master’s/PhD in Computer Science, Machine Learning, AI, Engineering, or related quantitative field (advanced degree preferred for research-oriented roles).
- Strong analytical/problem-solving skills and ability to handle ambiguity in complex domains.
Preferred Skills / Nice-to-Haves
- Domain knowledge in scientific/engineering data, measurement/simulation, semiconductors, Earth sciences, language technologies, or industrial applications.
- Experience with physics-informed ML, scalable training on HPC clusters, or edge/hybrid deployments.
- Familiarity with evaluation frameworks, synthetic data, or influencing model development.
- Open-source contributions, publications, or portfolio of production ML projects.
What We Offer
- Work on high-impact ML projects with access to advanced infrastructure (e.g., supercomputers at BSC or engineering datasets at Keysight).
- Collaborative, innovative culture in Barcelona’s tech scene, with opportunities for growth and events.
- Competitive compensation package, flexible hybrid work, professional development, and potential visa/relocation support.
How to Apply Submit your CV, cover letter, and relevant portfolio (e.g., GitHub, publications) via the company careers page, LinkedIn, or official job links. Proactive applications are welcome highlight your production ML experience and domain interests.
