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Remote role at a reputable startup, popular ML title increases applicant density despite senior experience requirement.
Core ML engineering skills are transferable, though privacy and LLM specialization increase domain bias.
Explicit 8+ years, required ML specializations and tech stack make filters strict.
Own end-to-end design, development, deployment, and optimization of ML models and pipelines, including data ingestion, training, evaluation, and monitoring for production use.
Proactively improve ML model reliability, observability, and performance, including GPU performance optimization and risk mitigation for LLMs and agentic systems.
Build and maintain privacy APIs, backend infrastructure for large-scale data and privacy workflows, and develop SDKs, templates, and documentation to mainstream ML model shipping.
8+ years of machine learning experience.
Proficiency in Go or Python programming language.
Hands-on experience with ML libraries (NumPy, Pandas, Scikit-learn), deep learning frameworks (TensorFlow or PyTorch), and designing production ML systems, especially NLP/NER focused.
Work Experience Required: 8+ years in Machine Learning.
Experienced in building high-throughput, low-latency ML systems and comfortable working on scalable distributed systems.
Strong background in advanced ML architectures with focus on language models and practical deployment challenges including performance engineering.
Capable of developing privacy-sensitive ML infrastructures and observability tooling for ML lifecycle monitoring.