Skyflow is a data privacy vault company built to radically simplify how companies isolate, protect, and govern their customers’ most sensitive data. With its global network of data privacy vaults, Skyflow is also a comprehensive solution for companies around the world looking to meet complex data localization requirements. Skyflow currently supports a diverse customer base that spans verticals like fintech, retail, travel, and healthtech.

Skyflow is headquartered in Palo Alto, California and was founded in 2019. For more information, visit www.skyflow.com or follow on X and LinkedIn.

About the role:

We’re looking for a 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.

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:

• 3+ years of experience in Machine Learning.

• Proficient in one of the programming languages either Go or Python

• Strong fundamentals in machine learning, statistics, and data structures

• Having experience in implementing AI-driven software systems with a focus on NLP, NER, agentic systems and generative AI.

• Hands-on experience with Python and ML libraries (NumPy, Pandas, Scikit-learn).

• Experience with deep learning frameworks like TensorFlow or PyTorch

• 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

• 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

• Optimize models for performance, scalability, and reliability

• Contribute to performance engineering efforts and ensure low-latency and high-throughput transactions at scale.

• Participate in building and implementing effective test strategies and developing software with high agility and zero downtime.

• Collaborate with security and privacy engineers to deliver state-of-the-art privacy solutions.

Benefits:

• Work from home expense

• Excellent Health Insurance Options

• Very generous PTO

• Flexible Hours

At Skyflow, we believe that diverse teams are the strongest teams. We invite applicants of all genders, races, ethnicities, nationalities, ages, religions, sexual orientations, disability statuses, educational experiences, family situations, and socio-economic backgrounds.