Machine Learning Engineer (Product) (9-month contract with bonus incenti...

Hyperproof; Barcelona; Híbrido; EUR 40.000 - 70.000; Jornada completa

Descripción de la vacante

A leading deep-tech company in Barcelona is seeking a Machine Learning Engineer to build and optimize AI models. This hybrid role requires strong programming skills in Python and experience with ML tools like PyTorch. Candidates must have a Bachelor's degree and solid experience in data handling and model evaluation. The position offers a fast, transparent application process with bonus incentives.

Formación

• Experience working with datasets at scale.
• Ability to read research papers and prototype ideas.
• Experience with GPU resources.

Responsabilidades

• Build data and model pipelines end-to-end.
• Scale training and inference for ML models.
• Collaborate with engineers and researchers.

Conocimientos

Python programming

Data analysis with NumPy/Pandas

Strong communication skills

ML model evaluation

Collaboration across teams

Educación

Bachelor's degree in a relevant field

Herramientas

PyTorch

Docker

Descripción del empleo

Overview

Machine Learning Engineer (Product) — 9-month contract with bonus incentives. Location: Madrid, Barcelona. We are hiring immediately and reviewing applications daily for a fast, transparent process with quick feedback.

We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is transforming how AI is deployed worldwide, including compression of large language models by up to 95% without loss of accuracy and reductions in inference costs by 50–80%.

Responsibilities

• Build data and model pipelines end-to-end: create, source, augment, and validate datasets; stand up training, fine-tuning, and 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: utilize 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.

Required Qualifications

• Bachelor's degree in Computer Science, Math, 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 modeling concepts around training, evaluation, model architecture, and data.
• Experience working with datasets at scale: collection, cleaning, filtering, labeling/annotation strategies, and quality controls.
• Experience with 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.

Preferred Qualifications

• PhD in Computer Science, Math, Physics, Data Science, Operations Research, or related field, or equivalent industry experience in ML/data science with demonstrated NLP/LLM experience.
• Experience building foundational LLMs from the ground up.

Preferred focus areas

• Model Evaluation: track record building task-grounded evals for LLMs, implementing or extending evaluation harnesses, and generating synthetic data for evaluation and training. Deep understanding of LLM quirks and training dynamics.
• Distributed Training: hands-on experience debugging multi-node training, profiling/optimizing throughput and memory, and extending training frameworks to new architectures or optimizers; comfortable diagnosing flaky cluster issues.
• Model Compression: strong mathematical background with pruning, quantization, and NAS; ability to solve constrained optimization problems for accuracy/latency/footprint trade-offs and to deploy results to production.
• Post-Training: familiarity with post-training and alignment techniques; experience with SFT and preference/RL-based methods (e.g., DPO/GRPO, RLHF).

About Multiverse Computing

Founded in 2019, we are a well-funded, fast-growing deep-tech company with a global team. Recognized by CB Insights (2023 & 2025) as one of the Top 100 most promising AI companies globally, we are the largest quantum software company in the EU.

Our flagship products address critical industry needs:

• CompactifAI — a groundbreaking compression tool for foundational AI models, reducing size by up to 95% while maintaining accuracy, enabling portability across devices from cloud to mobile.
• Singularity — a quantum and quantum-inspired optimization platform used by blue-chip companies to solve complex challenges with immediate performance gains.

You’ll be working alongside world-leading experts in quantum computing and AI, delivering real-world impact for global clients. We are committed to an inclusive, ethics-driven culture that values sustainability, diversity, and collaboration.

As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.

Job Details

• Department: Technical
• Role: Machine Learning Engineer
• Locations: Madrid, Barcelona
• Employment type: Contract
• Workplace type: Hybrid
• Seniority level: Associate