Machine Learning Engineer (Product)

Machine Learning Engineer – (Product)

A fantastic opportunity for a driven Machine Learning Engineer to join a leading Quantum AI company, who work on cutting-edge solutions that make AI faster, greener, and more accessible. You’ll be working alongside world-leading experts in quantum computing and AI, developing solutions that deliver real-world impact for global clients.

This is initially a Fixed Term Contract until July 2026 with scope to extend –  *Hybrid working from Zaragoza. 

Responsibilities

• Build data and model pipelines end to end: create, source, augment, and validate datasets; stand up training/fine?tuning/evaluation flows; and ship models that meet product and customer requirements.
• Design rigorous evaluation frameworks to verify task competence and alignment; implement statistical testing, reliability checks, and continuous evaluation.
• Scale training and inference: make effective use of distributed compute, optimize throughput/latency, and identify opportunities for algorithmic or systems level speedups.

• Improve models post training: apply SFT and preference based or reinforcement learning methods to enhance helpfulness, safety, and reasoning.

• Optimize and specialize models: apply compression techniques to meet performance and footprint targets.

• Collaborate across research and engineering: partner with ML engineers, researchers, and software engineers on data curation, evaluation design, training runs, model serving, and observability.

• Contribute to our shared codebase: write clean, well?tested Python; document decisions and artifacts; uphold engineering standards.

Qualifications

• This role requires a Bachelor’s degree in Computer Science, Math, Physics, Physics, Data Science, Operations Research, or related field.

• Strong programming skills in Python and the modern ML stack (e.g., PyTorch), plus fluency with data tooling (NumPy/Pandas) and basic software practices (git, unit tests, CI).

• Solid grounding in language modelling concepts around training, evaluation, model architecture, and data.

• Comfort working with datasets at scale: collection, cleaning, filtering, labelling/annotation strategies, and quality controls.

• Experience using GPU resources and familiarity with containerized workflows (e.g., Docker) and job schedulers or cloud orchestration.

• Ability to read research papers, prototype ideas quickly, and turn them into reproducible, production?ready code.

• Clear, pragmatic communication and a collaborative mindset.