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About the Role

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

• 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

Requirements

• 8+ years of software engineering experience, including 4+ years building infrastructure for ML or LLM systems in production
• 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
• 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

• PyTorch
• distributed training
• fine-tuning at scale (LoRA, SFT)
• inference engines such as vLLM or TensorRT-LLM
• eval harnesses
• regression gates
• dataset pipelines
• precision
• recall
• calibration
• Python
• containers
• CI/CD
• cloud infrastructure
• observability

Location

• New York, NY

Work Type

• hybrid

Experience Level

• 8+ years of software engineering experience
• 4+ years building infrastructure for ML or LLM systems in production

Salary/Compensations

• $200,000–$250,000 base

Benefits

• Performance bonus
• meaningful early-stage equity
• Health, dental, and vision coverage

About the Company

• 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.