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
