Principal MLE @ A16Z Backed Security Startup | $200 - $250k + Equity

United Statesfull-timelead

OtherPrincipal

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Quick Summary

Key Responsibilities

data preparation, reproducible fine-tuning runs, experiment tracking, and release automation Build the evaluation infrastructure: automated eval runs, regression gates, dashboards,

Technical Tools

OtherPrincipal

Location: New York, NY (hybrid — 3 days/week in office) Compensation: $200,000–$250,000 base, plus performance bonus and meaningful early-stage equity Company: Confidential — details shared with qualified candidates

We're building the AI-native compliance enforcement infrastructure for enterprise communication — 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 increasingly communicates 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. We enforce compliance before send — a category that didn't exist until we created it.

We're backed by top-tier venture investors and built by a team with backgrounds at leading tech and financial firms, led by a repeat AI founder. We recently closed an oversubscribed seed round, and we're moving fast.

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.

Responsibilities

~1 min read

→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

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
Experience building eval harnesses, regression gates, or dataset pipelines, with a solid understanding of precision, recall, and calibration
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

~1 min read

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