Senior Ml Engineer (Token Factory)

Jobgether; Málaga; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

Jobgether is listing on behalf of a partner company. A Senior Machine Learning Engineer (Token Factory) based in Spain will join a team building large-scale AI systems and high-performance infrastructure for training and serving foundation models at scale.

You will optimize inference, training pipelines, and GPU efficiency on massive GPU fleets, collaborating with ML, systems, and infrastructure experts in a fast-paced research environment.

Formación

• Strong ML fundamentals, transformer and LLM knowledge.
• Experience profiling and optimizing GPU workloads with Nsight or PyTorch Profiler.
• Deep GPU architecture understanding, memory hierarchy and compute vs memory trade-offs.
• Familiar with attention, KV-cache, Flash Attention, quantization.
• Experience with distributed training, sharding, and custom kernels.
• Proficient in Python and modern ML frameworks.
• Knowledge of CI/CD, version control, and testing.
• Excellent cross-functional collaboration.

Responsabilidades

• Drive inference optimization and latency reduction across diverse LLMs.
• Design and evolve inference engines, including KV-cache and dense/MoE models support.
• Develop low-precision training/inference pipelines (e.g., FP8) for large GPU clusters.
• Profile GPU workloads to identify bottlenecks and guide architecture decisions.
• Collaborate on scalable distributed training/inference systems with sharding and custom kernels.
• Contribute to engineering best practices: testing, CI/CD, production-grade ML systems.

Conocimientos

ML fundamentals

GPU profiling

GPU architecture

LLM concepts

Distributed training

Python programming

CI/CD

Communication skills

Team collaboration

Herramientas

Nsight

PyTorch Profiler

CUDA

Descripción del empleo

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer (Token Factory) based in Spain.

This role sits at the intersection of large-scale AI systems and high-performance infrastructure, focusing on optimizing how foundation models are trained and served at scale.
You will contribute to a cutting-edge inference and fine-tuning platform designed to push modern LLMs to their performance limits across massive GPU fleets.
The work directly impacts throughput, latency, and cost efficiency for next-generation AI workloads used in production environments.
You will collaborate with highly specialized engineers across ML, systems, and infrastructure domains in a fast-moving, research-driven environment.
The role combines deep ML expertise with systems-level engineering, requiring strong understanding of both model architecture and hardware behavior.
You will help design and improve critical components such as inference engines, training pipelines, and GPU optimization strategies.

Accountabilities:

• Drive inference optimization efforts by identifying bottlenecks and implementing performance improvements across diverse LLM architectures, improving throughput and reducing latency and cost per token.
• Contribute to the design and evolution of inference engines, including techniques such as speculative decoding, KV-cache optimization, and support for dense and MoE models.
• Develop and productionize low-precision training and inference pipelines (e.g., FP8, MXFP4) to maximize efficiency on large GPU clusters.
• Profile and analyze GPU workloads using modern tooling to identify performance constraints and guide architectural improvements.
• Collaborate on scalable distributed training and inference systems, including sharding strategies, custom kernels, and hardware-aware optimizations.
• Contribute to engineering best practices including testing, CI/CD, and maintainable production-grade ML systems.

Requirements:

• Strong understanding of machine learning fundamentals, particularly transformer architectures and large language models.
• Hands-on experience profiling and optimizing GPU workloads using tools such as Nsight or PyTorch Profiler.
• Deep knowledge of GPU architecture, including memory hierarchy and compute vs. memory trade-offs.
• Familiarity with key LLM concepts such as attention mechanisms, RoPE, KV-cache, Flash Attention, and quantization techniques.
• Experience with large-scale deep learning training, including distributed systems, sharding strategies, and custom kernel development.
• Strong software engineering skills, with advanced proficiency in Python and modern ML frameworks.
• Solid understanding of software engineering practices such as version control, CI/CD pipelines, and unit testing.
• Strong communication skills with the ability to collaborate effectively in highly technical, cross-functional teams.
• Competitive compensation package
• Strong career development and continuous learning opportunities
• Flexible work environment with high autonomy and ownership
• Opportunity to work on frontier AI systems at massive scale
• International, highly skilled, and diverse team environment

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.