{"id":2188761,"url":"https://alion.io/job/fundamental-senior-applied-research-engineer","title":"Senior Applied Research Engineer","company":{"id":1790897,"name":"Fundamental","domain":"fundamental.tech","url":"https://alion.io/company/fundamental-tech","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":62000,"max_usd":132000,"period":"year","method":"role_seniority_country_cell","sample_n":8},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Triton","optional":false},{"name":"C++","optional":true},{"name":"NCCL","optional":true},{"name":"PyTorch","optional":true},{"name":"PyTorch C++","optional":true}],"status":"live","first_seen_at":"2026-10-09T16:58:59Z","employer_posted_date":null,"last_verified_at":"2026-10-09T16:58:59Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"About Fundamental\nFundamental 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.\nAt 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.\nKey responsibilities\nProfile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication\n\nContribute to architectural decisions that improve the efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed\n\nDesign and implement model scaling, parallelization, and memory optimization techniques for training workloads with very large context sizes\n\nCollaborate 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\n\nDrive the productionization and serving of our models from the research side, including improving inference efficiency through techniques such as quantization\n\nMust have\nStrong understanding of modern ML architectures and large-scale training pipelines\n\nExperience running distributed training jobs on multi-GPU systems\n\nAdvanced profiling and debugging skills across CPU, GPU, memory usage, latency, and inter-GPU communication\n\nStrong programming skills in Python\n\nExperience with model scaling and parallelization strategies, including tensor and pipeline parallelism\n\nNice to have\nFamiliarity with NCCL, MPI, and distributed communication primitives\n\nKnowledge of PyTorch and Triton internals\n\nProgramming experience with C++ and CUDA\n\nBenefits\nCompetitive compensation with salary and equity\n\nComprehensive health coverage for you and your dependents\n\nPaid parental leave for all new parents, inclusive of adoptive and surrogate journeys\n\nRelocation support for employees moving to join the team in one of our office locations\n\nA mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward 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