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Skyflow is seeking a Senior Machine Learning Engineer to design, build, and deploy production ML models that power their runtime AI data control layer. The role involves end-to-end ML system development—from data ingestion and feature engineering through training, evaluation, deployment, and monitoring—with a focus on NLP, NER, and privacy-preserving AI workflows.
You'll collaborate closely with product, backend, and data teams to translate real-world problems into scalable, production-ready solutions. Key responsibilities include designing and optimizing ML pipelines, fine-tuning open-source models for NLP and vision tasks, implementing synthetic data generation and model validation, and GPU performance optimization for large-scale transformers. You'll also instrument deep observability (traces, logs, metrics, data/feature drift detection) and develop templates, SDKs, CLIs, and documentation to streamline ML deployment.
The role emphasizes hands-on experimentation, ownership, and shipping over academic perfection. You'll monitor and mitigate risks unique to LLMs and agentic systems, contribute to privacy API and backend infrastructure design, and participate in performance engineering to ensure low-latency, high-throughput transactions at scale.
Required: 8+ years ML experience, proficiency in Go or Python, hands-on production ML deployment, deep learning frameworks (TensorFlow/PyTorch), SOTA language model architectures, data pipelines/feature stores, performance engineering, CI/CD practices, and strong fundamentals in algorithms, data structures, and distributed systems. Privacy and auth/authn engineering experience is highly valued. Nice-to-have: NLP, recommendation systems, computer vision, MLOps tools (MLflow, Airflow, Kubeflow, SageMaker, Vertex AI), Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
