Staff Machine Learning Engineer

Staff Machine Learning Engineer

We are Omnissa. The world is evolving quickly, and organizations everywhere—from global enterprises to educational institutions—are under pressure to deliver flexible, work-from-anywhere experiences. They need secure, scalable, seamless digital work environments that empower employees and customers to access applications from any device, on any cloud. That's where Omnissa comes in. The Omnissa Platform is the first AI driven digital work platform designed to deliver smart, seamless, and secure work experiences from anywhere. We uniquely integrate industry leading solutions in Unified Endpoint Management, Virtual Apps and Desktops, Digital Employee Experience, and Security & Compliance—all unified through shared data, identity, administration, and automation services. Built on the vision of autonomous workspaces—self configuring, self-healing, and self-securing—Omnissa continuously adapts to how people work, optimizing user experience, IT efficiency, security posture, and cost. As a global private company with over 4,000 employees, we're growing rapidly. If you're passionate about building AI systems that operate at a massive scale and shape the future of work, we'd love to meet you.

What is the opportunity?

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. Here is a breakdown:

• 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.

What will you bring to Omnissa?

• 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.

Preferred Skills

• 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.

Location: Mountain View, CA or Atlanta, GA Location Type: hybrid Travel Expectations: None Education: Bachelor's Degree preferred, or equivalent combination of education and relevant professional experience.

Compensation: The typical base salary for this role is between USD $162,512 – $342,750 per year and it may be eligible for participation in a corporate bonus program. Actual compensation offer may vary from posted hiring range based upon geographic location, work experience, education, skill level, or other relevant factors. In addition to competitive compensation, Omnissa offers a variety of benefits such as employee ownership, health insurance, 401k with matching contributions, disability insurance, paid-time off, growth opportunities, and more.

Omnissa is an Equal Employment Opportunity company and Prohibits Discrimination and Harassment of Any Kind: Omnissa is committed to the principle of equal employment opportunity and to providing a work environment free of discrimination and harassment. All employment decisions at Omnissa are based on business needs, job requirements and individual qualifications, without regard to race, color, religion, ancestry, ethnicity, national, social or ethnic origin, sex (including pregnancy), age, physical, mental or sensory disability, HIV status, sexual orientation, gender identity and/or expression, marital, civil union or domestic partnership status, past, present, or prospective service in the uniformed services, family medical history or genetic information, family or parental status, veteran status, or any other status protected by applicable laws or regulations in the locations where we operate. Omnissa will not tolerate discrimination or harassment based on any of these characteristics. Omnissa welcomes applicants of all ages. Omnissa will provide reasonable accommodations to applicants and employees who have protected disabilities consistent with applicable federal, state and local law.