Principal Machine Learning Engineer

ZeroDrift; New York (NY); Hybrid; USD 200,000 - 250,000; Full time

Benefits offered by this job

Equity

Health, dental, and vision

Hybrid work in NY

Job summary

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.

Responsibilities

• 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

Skills

Software engineering

ML infrastructure

Production systems

Python

CI/CD

Tools

PyTorch

LoRA

SFT

vLLM

TensorRT-LLM

Distributed training

Docker

Job description

ZeroDrift is building the AI-native compliance enforcement infrastructure for enterprise communication. We are the first platform that enforces compliance before an AI message is ever sent, fixing violations in real time across every channel where AI speaks for the business. Everything goes out clean. Nothing dangerous comes in.

As AI starts to communicate on behalf of entire organizations, the gap between what compliance requires and what companies can actually enforce is widening fast. Every existing solution monitors after send, once the risk is already out the door. ZeroDrift enforces compliance before send. Compliance enforcement did not exist as a category. We created it.

We are backed by Andreessen Horowitz (a16z) and other world-class VCs, and built by a team from Microsoft, Google, and Goldman Sachs, led by a repeat AI founder.

The Role

Compliance enforcement runs on specialized language models. They decide in real time whether a communication is safe to send, and they have to be accurate, fast, and reliable, because they sit in the path of live traffic.

Our AI research team owns the science: model behavior, training objectives, data strategy, and the quality bar. You own the systems that make the science real. You will build the pipelines that train models reproducibly, the evaluation infrastructure that proves they work, and the serving stack that runs them in production.

This is a hands-on principal individual contributor role on a small, senior team. It is a systems role, not a research role. The right candidate loves making ML industrial-grade.

What you'll do

• Build and own the training pipelines: data preparation, reproducible fine-tuning runs, experiment tracking, and release automation
• Build the evaluation infrastructure: automated eval runs, regression gates, dashboards, and dataset versioning. Research defines what good means. You build the machinery that measures it
• 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
• Set the bar for ML infrastructure as the team grows

What we're looking for

• 8+ years of software engineering experience, including 4+ years building infrastructure for ML or LLM systems in production
• Hands-on depth with the modern LLM stack: PyTorch, distributed training, fine-tuning at scale (LoRA, SFT), and inference engines such as vLLM or TensorRT-LLM
• You have built eval harnesses, regression gates, or dataset pipelines, and you understand precision, recall, and calibration well enough to build the right measurement around them
• Production mindset. You have owned model serving with real latency, reliability, and cost constraints, not just notebooks
• Strong fundamentals: Python, containers, CI/CD, cloud infrastructure, observability
• High ownership on a small team: scope your own work, ship weekly, make pragmatic build-vs-buy calls
• You enjoy being the engineering counterpart to a research partner. Tight collaboration, clear interfaces, no turf wars

Nice to have

• Experience productionizing small or specialized language models
• Experience with structured-output serving or constrained decoding in production
• Prior work in a regulated or high-stakes domain such as fintech, healthcare, legal, or trust and safety
• Experience deploying models into customer-controlled environments

Compensation and benefits

• $200,000 to $250,000 base salary, depending on experience
• Performance bonus and meaningful early-stage equity
• Health, dental, and vision coverage
• Hybrid work from our New York office

ZeroDrift is an equal opportunity employer. We evaluate candidates without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability, or any other protected characteristic.