Principal Machine Learning Engineer

You will own the ML infrastructure that turns research into reliable, real-time compliance enforcement systems, driving model training, evaluation, and production serving. You will partner closely with research stakeholders and engineering peers to ship reproducible pipelines and low-latency serving; the role is Hybrid (3 days onsite) in the New York City Metro area.

Here’s How You’ll Make an Impact on the Team

• Build and own training pipelines: data preparation, 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
• Create repeatable workflows to adapt models to new domains and customer needs
• Turn expert labels and reviewer feedback into clean training and evaluation data
• Set the engineering 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 experience with the modern LLM stack: PyTorch, distributed training, fine-tuning at scale (e.g., LoRA, SFT), and inference engines such as vLLM or TensorRT-LLM
• Experience building eval harnesses, regression gates, or dataset pipelines; strong understanding of precision, recall, and calibration
• Proven ownership of production model serving with real latency, reliability, and cost constraints
• Strong fundamentals in Python, containers, CI/CD, cloud infrastructure, and observability
• Ability to scope work, ship frequently, and make pragmatic build-vs-buy decisions
• Experience collaborating tightly with research partners and defining clear interfaces

Here’s What Else Might Help You Out

• Experience productionizing small or specialized language models
• Experience with structured-output serving or constrained decoding in production
• Prior work in regulated or high-stakes domains (fintech, healthcare, legal, trust and safety)
• Experience deploying models into customer-controlled environments