Principal Machine Learning Engineer

Location: New York

Location Type: HYBRID

Employment Type: FULL_TIME

• 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

• 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
• Experience building eval harnesses, regression gates, or dataset pipelines, with solid understanding of precision, recall, and calibration
• Proven track record owning model serving under real latency, reliability, and cost constraints — not just in notebooks
• 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

• Experience productionizing small or specialized language models
• Experience with structured-output serving or constrained decoding in production
• Background in a regulated or high-stakes domain such as fintech, healthcare, legal, or trust and safety
• Experience deploying models into customer-controlled environments

• $200,000–$250,000 base salary, depending on experience
• Performance bonus and meaningful early-stage equity
• Health, dental, and vision coverage
• Hybrid work from a New York office