Staff Machine Learning Engineer

Omnissa•Mountain View, CA

15h•$162,512 - $342,750•Hybrid

About The Position

Our platform manages millions of devices across multiple operating systems, requiring exceptional performance, scalability, availability, and resilience. You will join the AI Platform Team, the group responsible for building foundational AI capabilities across the Omnissa product ecosystem. As a Staff Machine Learning Engineer, you will design, build, and deploy machine learning systems that power predictive analytics, personalization, automation, and intelligent platform behaviors. You’ll work closely with engineering and product teams to operationalize models across our cloud scale environment while driving best in class ML engineering practices. You will own engineering initiatives end to end and help foster a culture of high ownership, continuous improvement, and engineering excellence.

Requirements

5+ years of experience in machine learning engineering or data science roles.
Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikitlearn).
Experience building and operating data processing workflows (batch or streaming) and working with cloud platforms (AWS, Azure, or GCP).
Solid understanding of machine learning algorithms, statistics, and model evaluation techniques.
Familiarity with containerization and orchestration technologies (Docker, Kubernetes).
Handson experience with Large Language Models (LLMs), including finetuning, prompt engineering, and deployment.
Knowledge of text embedding models, and vector databases for Retrieval Augmented Generation (RAG) systems
Strong problem-solving skills and the ability to collaborate effectively in Agile teams.
Highly motivated, adaptable, and eager to learn new technologies.

Nice To Haves

Experience with distributed computing frameworks (e.g., Spark, Ray).
Experience with orchestration frameworks (e.g., LangChain/LangGraph) to build AI agents and multi-agent systems.
Experience building feature stores or working with vector databases.
Knowledge of real-time inference architectures and model monitoring systems.
Experience developing scalable ML services via REST/gRPC.

Responsibilities

Design, develop, and deploy machine learning models for classification, prediction, anomaly detection, and intelligent automation.
Build and maintain scalable data pipelines for model training, evaluation, and real time/batch inference.
Optimize ML models and pipelines for performance, scalability, reliability, and cost efficiency.
Collaborate with cross functional teams to integrate ML solutions into core platform features and services.
Conduct model experimentation, evaluation, and iteration using quantitative metrics and A/B testing as needed.
Implement model observability, monitoring, and drift detection to ensure production reliability.
Stay current with advancements in machine learning, AI, and LLM technologies, and apply them to product use cases.