Principal Machine Learning Engineer
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The role
Lead the end-to-end ML lifecycle—from data and training to scalable inference and deployment.
Fine-tune and adapt large-scale models using LoRA, QLoRA, SFT, DPO, and distillation.
Design robust, production-grade inference pipelines that balance latency, cost, and reliability.
Build data systems for high-quality synthetic and real-world training data; develop evaluation tooling for performance, safety, and bias.
Own production readiness, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
Collaborate with backend, mobile, and desktop teams to ship practical ML-powered features under real production constraints.
Fine-tune and adapt large-scale models using LoRA, QLoRA, SFT, DPO, and distillation.
Design robust, production-grade inference pipelines that balance latency, cost, and reliability.
Build data systems for high-quality synthetic and real-world training data; develop evaluation tooling for performance, safety, and bias.
Own production readiness, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
Collaborate with backend, mobile, and desktop teams to ship practical ML-powered features under real production constraints.
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