Senior Applied Research Engineer

Benefits

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Mental health support

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

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Assessment

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Deadline

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Where they hire

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About this role

Senior Applied Research Engineer Barcelona, Spain About Fundamental Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS - the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by 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

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