Data Scientist

Sciforium•San Francisco, CA

About The Position

Sciforium is seeking a highly analytical and systems-aware Data Scientist to design, develop, and refine the next-generation AI models that leverage our large-scale compute clusters. In this role, you will bridge the gap between theoretical research and production-grade performance. You will not only build state-of-the-art LLMs and generative models but also ensure they are architecturally optimized for distributed training environments. This position is ideal for a scientist who thinks deeply about algorithmic efficiency, convergence stability, and how model architecture impacts hardware utilization. You will play a pivotal role in defining the intelligence that powers Sciforium’s core offerings.

Requirements

5+ years of industry experience in Data Science, Machine Learning Research, or a closely related field, with a strong emphasis on deep learning.
Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or another quantitative discipline.
Expert-level Python skills with deep proficiency in PyTorch or JAX.
Demonstrated experience training and deploying large-scale models (e.g., LLMs, diffusion models) in distributed production environments.
Deep understanding of distributed training paradigms, including data parallelism, pipeline parallelism, and tensor parallelism.
Strong mathematical foundation in linear algebra, calculus, and optimization, particularly as applied to neural network training and convergence.
Experience working with data-at-scale tooling, such as Spark, Ray, or high-throughput data loading frameworks.

Nice To Haves

PhD in a relevant field, with publications at top-tier conferences (e.g., NeurIPS, ICML, ICLR).
Hands-on experience with Mixture-of-Experts (MoE) architectures, including routing and load-balancing challenges.
Familiarity with RLHF workflows, including PPO and DPO fine-tuning pipelines.
Knowledge of model quantization techniques (e.g., FP8, INT8, AWQ) and their impact on training stability and inference performance.
Contributions to open-source ML libraries or involvement in high-profile LLM releases.

Responsibilities

Model Architecture Design: Develop and experiment with novel architectures for LLMs and generative AI, focusing on maximizing performance-per-watt and training throughput.
Large-Scale Training Execution: Lead the end-to-end training runs of foundation models, monitoring loss curves, stability, and convergence across massive multi-node clusters.
Optimization & Scaling Laws: Apply scaling laws to predict model performance and optimize hyperparameters, tokenization strategies, and objective functions for trillion-parameter regimes.
Data Engineering & Curation: Build and maintain sophisticated data pipelines that handle petabyte-scale pre-training datasets, ensuring high-quality signal through advanced filtering and deduplication.
Algorithmic Profiling: Collaborate with the Training Engineering team to profile how specific model layers (e.g., Attention mechanisms, MoE layers) interact with GPU/accelerator memory and interconnects.
Evaluation & Benchmarking: Design robust evaluation frameworks to measure model capability across reasoning, coding, and creative tasks, ensuring alignment with safety and performance standards.
Cross-Functional Collaboration: Partner with Infrastructure and Kernel engineers to co-design features that improve training efficiency and model FLOPs utilization (MFU).