Principal ML Engineer — Real-Time LLM Infra for Compliance

ZeroDrift; New York (NY); Hybride; USD 200 000 - 250 000; Plein temps

Avantages offerts par ce poste

Equity

Health, dental, and vision

Hybrid work in NY

Résumé du poste

ZeroDrift is building an AI-native compliance enforcement platform for enterprise use. We are seeking an experienced ML infrastructure engineer who will own production pipelines across training, evaluation, and serving for live AI traffic.

This is a hands-on principal contributor role on a small, senior team, focused on scalable systems rather than pure research. You will implement reproducible training pipelines, automated evaluation gates, model versioning, canarying, drift monitoring, and

Qualifications

• 8+ years of software engineering experience with 4+ years building ML/LLM infra in production.
• Hands-on experience with PyTorch, distributed training, fine-tuning (LoRA/SFT), and fast inference engines.
• Strong production mindset with focus on latency, reliability, and cost.

Responsabilités

• Build and own ML training pipelines: data prep, reproducible fine-tuning runs, experiment tracking, and release automation.
• Build the evaluation infrastructure: automated eval runs, regression gates, dashboards, and dataset versioning.
• Own model serving in production: low-latency inference, batching, optimization, autoscaling, and cost
• Ship model updates safely with versioning, canarying, rollback, and drift monitoring
• Build repeatable workflows for adapting models to new domains and customer needs
• Turn expert labels and reviewer feedback into clean training and evaluation data

Connaissances

Software engineering

ML infrastructure

Production systems

Python

CI/CD

Outils

PyTorch

LoRA

SFT

vLLM

TensorRT-LLM

Distributed training

Docker

Description du poste

ZeroDrift is building an AI-native compliance enforcement platform for enterprise use. We are seeking an experienced ML infrastructure engineer who will own production pipelines across training, evaluation, and serving for live AI traffic.

This is a hands-on principal contributor role on a small, senior team, focused on scalable systems rather than pure research. You will implement reproducible training pipelines, automated evaluation gates, model versioning, canarying, drift monitoring, and