Machine Learning Engineer (Product), Madrid
Empresa
Multiverse Computing
Provincia
Madrid
Ciudad
Madrid
Tipo de Contrato
Temporal
Salario
Entre 45000 Euros y 60000 Euros Bruto/año
Descripción
Machine Learning Engineer (Product)
As a Machine Learning Engineer you will
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.
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Required Qualifications
Bachelors 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 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.
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Preferred Qualifications
PhD in Computer Science, Math, Physics, Data Science, Operations Research, 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, and generating synthetic data for both evaluation and training deep understanding of LLM 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 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 post-training and alignment techniques experience with SFT and preference/RL-based methods (e.g., DPO/GRPO, RLHF).
Python, Pytorch, LLM
As a Machine Learning Engineer you will
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.
---
Required Qualifications
Bachelors 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 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.
---
Preferred Qualifications
PhD in Computer Science, Math, Physics, Data Science, Operations Research, 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, and generating synthetic data for both evaluation and training deep understanding of LLM 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 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 post-training and alignment techniques experience with SFT and preference/RL-based methods (e.g., DPO/GRPO, RLHF).
Python, Pytorch, LLM