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About the Role

This is a hands-on, principal-level individual contributor role focused on building and maintaining the systems that turn ML research into a reliable, production-grade product. You will own the pipelines that train models, the evaluation infrastructure, and the serving stack that runs them at scale, making ML industrial-grade.

Responsibilities

• 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

Requirements

• 8+ years of software engineering experience, including 4+ years building infrastructure for ML or LLM systems in production
• Proven track record owning model serving under real latency, reliability, and cost constraints
• 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

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
• productionizing small or specialized language models
• structured-output serving
• constrained decoding in production

Location

• New York, NY

Work Type

• Hybrid

Experience Level

• Principal

Salary/Compensations

• $200,000–$250,000 base salary

Benefits

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

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.