Staff Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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 i n 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:
Responsibilities
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Design, develop, and deploy machine learning models for classification, prediction, anomaly detection, and intelligent automation.
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Build and maintain scalable data pipelines for model training, evaluation, and real time /batch inference.
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Optimize ML models and pipelines for performance, scalability, reliability, and cost efficiency.
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Collaborate with cross functional teams to integrate ML solutions into core platform features and services.
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Conduct model experimentation, evaluation, and iteration using quantitative metrics and A/B testing as needed.
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Implement model observability, monitoring, and drift detection to ensure production reliability.
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Stay current with advancements in machine learning, AI, and LLM technologies, and apply them to product use cases.
Requirements
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5+ years of experience in machine learning engineering or data science roles.
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Strong proficiency in Python and ML frameworks (e.g., PyTorch , TensorFlow, Scikit learn ).
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Experience building and operating data processing workflows (batch or streaming) and working with cloud platforms (AWS, Azure, or GCP).
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Solid understanding of machine learning algorithms, statistics, and model evaluation techniques.
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Familiarity with containerization and orchestration technologies (Docker, Kubernetes).
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Hands on experience with Large Language Models (LLMs), including fine tuning, prompt engineering, and deployment.
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Knowledge of text embedding models, and vector databases for Retrieval Augmented Generation (RAG) systems
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Strong problem-solving skills and the ability to collaborate effectively in Agile teams.
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Highly motivated, adaptable, and eager to learn new technologies .
Preferred Skills
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Experience with distributed computing frameworks (e.g., Spark, Ray).
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Experience with orchestration frameworks (e.g., LangChain / LangGraph) to build AI agents and multi-agent systems.
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Experience building feature stores or working with vector databases.
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Knowledge of real-time inference architectures and model monitoring systems.
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Experience developing scalable ML services via REST/ gRPC., Education: Bachelor's Degree preferred, or equivalent combination of education and relevant professional experience.
Benefits & conditions
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.
