Principal ML Engineer
Salary$200k – $250k
LocationNYC Headquarters, New York
Work modehybrid
Typefull-time
SeniorityLead
Experience8+ yrs
DepartmentEngineering
EquityIncluded
Company size4+ people
PostedJul 12, 2026 · 3mo ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
8+ years of software engineering experience
4+ years building infrastructure for ML or LLM systems in production
Hands-on depth with PyTorch, distributed training, fine-tuning at scale (LoRA, SFT)
Experience with inference engines such as vLLM or TensorRT-LLM
Built eval harnesses, regression gates, or dataset pipelines
Understanding of precision, recall, and calibration
Production mindset - owned model serving with latency, reliability, and cost constraints
Strong fundamentals in Python, containers, CI/CD, cloud infrastructure, observability
High ownership on a small team
Enjoys being the engineering counterpart to a research partner
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
Skills
Python
PyTorch
vLLM
TensorRT-LLM
containers
CI/CD
cloud infrastructure
Benefits
Health insurance
Dental coverage
Vision coverage
Equity
Performance bonus
About ZeroDrift
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
Legal
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.
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