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# Junior Machine Learning Engineer

- **Company:** Multiverse Computing
- **Location:** Zaragoza, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Unit Testing, Data Compression, Computer Programming, Software Debugging, Distributed Computing Environment, Job Scheduling, Python (Programming Language), Machine Learning, Network Attached Storage (Server Appliance), Model Compression, Large Language Models, Model Validation, Git, Discretization, Information Technology, Production Code, Model Architecture, Docker
- **Published:** October 11, 2026
- **Apply:** https://www.recruit.net/job/machine-learning-engineer-jobs/BDDE5849AC4DC14B

## About the Role

* This role requires a Bachelor's degree in ML, Computer Science, Math, Physics, or related field.
 * Upto 2 years of hands-on experience. Strong programming skills in Python and the modern ML stack and basic software practices (git, unit tests, CI).
 * Solid grounding in language modelling concepts around training, evaluation, model architecture, and data.
 * 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.
 * Familiarity with AI / agentic workflows, and task automation.

Preferred Qualifications

 * PhD in ML, Computer Science, Math, Physics, Data Science, or related field, or equivalent industry experience in machine learning, data science, or related roles, with demonstrated experience with NLP or LLMs.
 * Experience building foundational LLMs from the ground up.

Preferred qualifications by focus area:

 * Model Evaluation: track record building task-grounded evals for LLMs, implementing or extending evaluation harnesses; deep understanding of LLM capabilities and quirks, and their ties to architecture and training dynamics.
 * Distributed Training: Hands-on experience debugging multi-node training, profiling/optimizing throughput and memory, and extending training frameworks like to new architectures or optimizers; comfort diagnosing flaky cluster issues.
 * Model Compression: Strong mathematical background and experience with pruning, quantization, and NAS; ability to formulate and solve constrained optimization problems for accuracy/latency/footprint trade-offs and to integrate results into production.
 * Post-Training: Theoretical and practical familiarity with RL theory, frameworks, and monitoring.

## Description

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As a Machine Learning Engineer, you will

 * In general: 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 reinforcement learning methods to enhance model capabilities and safety.
 * Optimize and specialize models: apply compression techniques to meet performance and footprint targets.
 * Collaborate across research and engineering and contribute to our shared codebase; uphold engineering standards.