Senior Machine Learning Engineer

The role

You will design, build, train, and deploy machine learning models for production use. You will work on end-to-end ML pipelines, GPU performance optimization, and privacy APIs.

What you will do

• 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

Required qualifications

• 8+ years of experience in Machine Learning
• Proficient in one of the programming languages either Go or Python
• 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 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

Nice to have

• Privacy, authorization/authentication engineering
• 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)

Benefits

• Work from home expense
• Excellent Health Insurance Options
• Very generous PTO
• Flexible Hours

About the company

Skyflow secures the flow of data across datastores, models, and agents. The company is headquartered in Palo Alto, California and was founded in 2019.