Senior Applied Research Engineer

Fundamental; Barcelona; On-site; EUR 90,000 - 120,000; Full time

Benefits offered by this job

Salary + equity

Health coverage

Parental leave

Relocation support

Diversity/collaboration culture

Job summary

Fundamental is seeking a senior ML engineer to advance distributed training and large-scale model optimization in Barcelona. You will profile runs, improve infrastructure, and collaborate with researchers to push the efficiency of Nexus.

The role emphasizes Python, multi-GPU training, and memory scaling, with opportunities to influence architecture and productionization of models. Relocation support and equity are offered.

Qualifications

• Strong understanding of modern ML architectures and large-scale training pipelines.
• Experience running distributed training jobs on multi-GPU systems.
• Advanced profiling and debugging skills across CPU, GPU, memory usage, latency, and inter-GPU communication.
• Strong programming skills in Python.
• Experience with model scaling and parallelization strategies, including tensor and pipeline parallelism.

Responsibilities

• Profile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication
• Contribute to architectural decisions that improve the efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed
• Design and implement model scaling, parallelization, and memory optimization techniques for training workloads with very large context sizes
• Collaborate closely with ML Researchers to diagnose architectural inefficiencies, ensure new research ideas scale efficiently in practice, and spread internal knowledge about model efficiency and optimization
• Drive the productionization and serving of our models from the research side, including improving inference efficiency through techniques such as quantization

Skills

Distributed training

Python

ML architectures

Profiling

Model scaling

Tools

NCCL

MPI

PyTorch

Triton

CUDA

C++

Job description

About Fundamental

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.

About Fundamental

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.

At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.

Key Responsibilities

• Profile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication
• Contribute to architectural decisions that improve the efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed
• Design and implement model scaling, parallelization, and memory optimization techniques for training workloads with very large context sizes
• Collaborate closely with ML Researchers to diagnose architectural inefficiencies, ensure new research ideas scale efficiently in practice, and spread internal knowledge about model efficiency and optimization
• Drive the productionization and serving of our models from the research side, including improving inference efficiency through techniques such as quantization
  
  

Must have

• Strong understanding of modern ML architectures and large-scale training pipelines
• Experience running distributed training jobs on multi-GPU systems
• Advanced profiling and debugging skills across CPU, GPU, memory usage, latency, and inter-GPU communication
• Strong programming skills in Python
• Experience with model scaling and parallelization strategies, including tensor and pipeline parallelism
  
  

Nice to have

• Familiarity with NCCL, MPI, and distributed communication primitives
• Knowledge of PyTorch and Triton internals
• Programming experience with C++ and CUDA
  
  

Benefits

• Competitive compensation with salary and equity
• Comprehensive health coverage for you and your dependents
• Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
• Relocation support for employees moving to join the team in one of our office locations
• A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action