Principal Machine Learning Engineer

Hybrid in the New York City Metro area (3 days onsite). In this Principal Machine Learning Engineering role, you will own the ML infrastructure behind real-time compliance enforcement systems. Expect a position centered on building the pipelines, evaluation workflow, and production serving needed to train, measure, and deploy models reliably, with a focus on latency, reliability, and cost . Pay range: USD $200,000 - $250,000 per year. What you’ll do Build and own training pipelines including data preparation, reproducible fine-tuning runs, experiment tracking, and release automation. Develop evaluation infrastructure with automated eval runs, regression gates, dashboards, and dataset versioning. Own production model serving for low-latency inference, including batching, optimization, autoscaling, and cost management. Ship model updates safely using versioning, canarying, rollback, and drift monitoring. Create repeatable workflows to adapt models to new domains and changing customer needs. Convert expert labels and reviewer feedback into clean training and evaluation datasets. Help raise the team’s engineering bar for ML infrastructure as the organization grows. What you’ll bring 8+ years of software engineering experience, including 4+ years building ML or LLM infrastructure for production. Hands-on experience with the modern LLM stack: PyTorch , distributed training, and fine-tuning at scale (e.g., LoRA , SFT ) using inference engines such as vLLM or TensorRT-LLM . Experience building eval harnesses , regression gates, or dataset pipelines, with 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. Technologies you’ll work with PyTorch, LoRA, SFT, vLLM, TensorRT-LLM Python, containers, CI/CD, cloud infrastructure, observability Additional qualifications that may help 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. Work location: Hybrid remote in New York, NY 10001 (3 days onsite). #J-18808-Ljbffr On behalf of Next Deavor