Senior Machine Learning Engineer

Skyflow secures the flow of data across datastores, models, and agents. Enterprises turn to Skyflow as their runtime AI data control layer to protect sensitive data, enable safe AI deployment, and unlock full value from their applications, data platforms, and AI systems. Skyflow is trusted by Fortune 500 enterprises, and leading SaaS companies in financial services, healthcare, retail, travel and hospitality. Skyflow is headquartered in Palo Alto, California and was founded in 2019. For more information, visit www.skyflow.com http://www.skyflow.com or follow on X https://twitter.com/SkyflowAPI and LinkedIn https://www.linkedin.com/company/skyflow/.   About the role: We’re looking for a Senior Machine Learning Engineer to build, deploy, and scale ML models that directly impact product and business outcomes. You’ll work closely with product, backend, and data teams to turn real-world problems into production-ready ML solutions.This is a hands-on role where experimentation, ownership, and shipping matter more than academic perfection. The ideal candidate has hands-on experience building end-to-end ML systems, deploying models to production, creating AI workflows, and improving the reliability, observability, and performance of AI-powered products. We know great software engineers come from diverse backgrounds so no single individual may have all the desired skills on day one. But if you are the kind of software engineer who would have loved to engineer solutions for Stripe or Twilio APIs, or the Slack or Zendesk app, or the Snowflake or MongoDB platform - we want to talk to you. You have: - 8+ years of experience in Machine Learning. - Proficient in one of the programming languages either Go or Python - Design, develop, and deploy production ML models and AI services. - Build end-to-end ML pipelines for data ingestion, feature engineering, training, evaluation, deployment, and monitoring. - Having experience in implementing AI-driven software systems with a focus on NLP, NER. - Hands-on experience with Python and ML libraries (NumPy, Pandas, Scikit-learn). - Experience with: - Experience with deep learning frameworks like TensorFlow or PyTorch - Deep understanding of SOTA ML model architectures, especially language models - Understanding of data pipelines, feature stores, and model lifecycle - Experience in performance engineering: developing high-throughput, low-latency systems - Experience with continuous integration, writing testable code, and test-driven development - Deep understanding of algorithms, data structures, scalability, and distributed systems - Privacy, authorization/authentication engineering is a huge plus. Good to have: - Experience with NLP, recommendation systems, or computer vision - Exposure to MLOps tools (MLflow, Airflow, Kubeflow, SageMaker, Vertex AI) - Experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure) You will: - Design, build, train, and deploy machine learning models for production use - Build end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment - Experimenting with and rapid prototyping of open source ML models. - Fine tuning open source models for NLP and Vision tasks - Synthetic data generation, model validation - GPU Performance optimisation of large scale ML models, including transformers - Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality. - Monitor, detect, and mitigate risks unique to LLMs and agentic systems. - Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking. - Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path. - Responsible for designing and developing Privacy APIs and backend infrastructure to support large-scale data and privacy workflows - O