Principal AI Researcher - New York City, NY

Role- Principal AI Researcher

Location - New York City

Duration- Long Term

Your Impact

Lead cross-functional collaboration with Product Management, ML, and Quality Engineering teams to deliver new, enterprise-grade AI security-as-a-service offerings in a timely, predictable fashion.

Tackle complex, ambiguous technical challenges across system boundaries, translating high-level product and security vision into resilient, production-ready AI detection models and backend architectures.

Design and select optimal AI architectures-from lightweight ML baselines to complex Transformers-to solve high-impact runtime security challenges.

Train, fine-tune, and align domain-specific foundation models using modern techniques (PEFT, LoRA, DPO) and distributed training frameworks.

Build scalable pipelines to filter, clean, and generate high-quality synthetic datasets for model training workflows.

Develop automated benchmarks, LLM-as-a-judge evaluations, and real-time pipelines to monitor model accuracy, and drift in production.

Develop models using techniques to minimize compute costs and meet strict, low-latency performance targets.

Preferred Qualifications:

PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields

LLM

PhD focus on NLP or Masters with 5 years of industrial NLP research experience

Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)

Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)

Publications in deep learning theory

Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR

Optimization (Training & Inference)

PhD focused on topics related to optimizing training of very large deep learning models

Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression

Experience optimizing training for a 10B+ model

Deep knowledge of deep learning algorithmic and/or optimizer design

Experience with compiler design