Principal Machine Learning Engineer

Principal ML Engineer New York, NY (Hybrid)About the Company We're building AI-native enforcement infrastructure for enterprise communication — technology that catches and fixes compliance issues in real time, before an AI-generated message ever reaches a customer or counterparty, across every channel where AI represents the business. Most existing tools only flag problems after the fact, once the risk is already out the door; we intervene before send. This is a new category, and we're the ones defining it.We're backed by top-tier venture capital and built by a team with backgrounds at major tech and financial firms, led by a founder who has built and scaled AI companies before.The Role Specialized language models sit at the core of our enforcement layer, making real-time decisions about whether a communication is safe to send. These models need to be accurate, fast, and dependable, since they operate directly in the path of live traffic.Our research team owns the underlying science — model behavior, training objectives, data strategy, and quality standards. You'll own the systems that turn that science into a reliable, production-grade product: the pipelines that train models reproducibly, the evaluation infrastructure that proves they work, and the serving stack that runs them at scale.This is a hands-on, principal-level individual contributor role on a small, senior team. It's a systems and infrastructure role, not a research role — ideal for someone who loves making ML industrial-grade.What You'll Do

• Build and own training pipelines: data prep, reproducible fine-tuning runs, experiment tracking, and release automation
• Build evaluation infrastructure: automated eval runs, regression gates, dashboards, and dataset versioning
• Own model serving in production: low-latency inference, batching, optimization, autoscaling, and cost management
• Ship model updates safely with versioning, canarying, rollback, and drift monitoring
• Build repeatable workflows for adapting models to new domains and customer needs
• Convert expert labels and reviewer feedback into clean training and evaluation datasets
• Set the technical 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
• Experience building eval harnesses, regression gates, or dataset pipelines, with solid understanding of precision, recall, and calibration
• Proven track record owning model serving under real latency, reliability, and cost constraints — not just in notebooks
• Strong fundamentals in Python, containers, CI/CD, cloud infrastructure, and observability
• Comfort with high ownership on a small team: scoping your own work, shipping weekly, and making pragmatic build-vs-buy calls
• Enjoyment of close collaboration with a research counterpart, with clear interfaces and no turf wars

Nice to Have

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

Compensation & Benefits

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

Compensation

The base pay range for this role is $200,000 – $250,000 per year.