Senior Applied Research Engineer
Join Fundamental as a Senior Applied Research Engineer to tackle technical challenges in AI model development for enterprise decision-making.
Posted by employer 7 months ago
First seen on Joblaze 2 months ago
Last verified on the company career page 1 day ago
AI in the day-to-day
Fundamental has developed NEXUS, a powerful Large Tabular Model for enterprise decision-making.
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the Senior Applied Research Engineer at Fundamental focuses on optimizing large-scale training processes for AI models, tackling challenges related to compute efficiency and memory management. Key skills include expertise in modern machine learning architectures, distributed training on multi-GPU systems, and proficiency in Python, with additional knowledge of C++ and CUDA being advantageous. This position is ideal for experienced engineers with a strong background in machine learning and a passion for advancing enterprise AI solutions. Fundamental's innovative environment, shaped by its DeepMind alumni founders, emphasizes collaboration and cutting-edge technology.
Joblaze insights
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: CUDA, MPI, NCCL, PyTorch, Python, Triton.
- What seniority level is this role?
- Fundamental targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Senior Applied Research Engineer role at Fundamental.
From the original posting
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
Standard company text repeated across Fundamental's postings is omitted here.