Uniphore is one of the largest B2B AI-native companies—decades-proven, built-for-scale and designed for the enterprise. The company drives business outcomes, across multiple industry verticals, and enables the largest global deployments.
Uniphore infuses AI into every part of the enterprise that impacts the customer. We deliver the only multimodal architecture centered on customers that combines Generative AI, Knowledge AI, Emotion AI, workflow automation and a co-pilot to guide you. We understand better than anyone how to capture voice, video and text and how to analyze all types of data.
As AI becomes more powerful, every part of the enterprise that impacts the customer will be disrupted. We believe the future will run on the connective tissue between people, machines and data: all in the service of creating the most human processes and experiences for customers and employees.
Job Description:
Uniphore is one of the largest B2B AI-native companies—decades-proven, built-for-scale and designed for the enterprise. The company drives business outcomes across multiple industry verticals and enables some of the largest global deployments.
Uniphore infuses AI into every part of the enterprise that impacts the customer. We deliver the only multimodal architecture centered on customers that combines Generative AI, Knowledge AI, Emotion AI, workflow automation and a co-pilot to guide you. We understand better than anyone how to capture voice, video and text and how to analyze all types of data.
As AI becomes more powerful, every part of the enterprise that impacts the customer will be disrupted. We believe the future will run on the connective tissue between people, machines and data: all in the service of creating the most human processes and experiences for customers and employees.
At Uniphore, we are building the foundational platform where data, AI, and execution converge, bringing together large-scale data processing, real-time systems, and intelligent orchestration into a unified architecture.
About the Role
This role sits within our Data Layer and Marketing AI (MAI) platform, where we are converging Customer Data Platform (CDP) capabilities, distributed data processing systems, and AI-driven decisioning into a single cohesive platform.
You will work on data-intensive, distributed systems that power real-time and batch processing, customer data activation, and emerging AI-driven workflows. The platform operates at enterprise scale, processing high-volume batch and real-time data across global customers, with pipelines handling petabyte-scale data and high-throughput event streams.
This is a backend-leaning role with opportunities to work across the stack, but with a strong focus on data systems, distributed compute, and platform engineering.
Key Responsibilities
Design and build scalable distributed data systems supporting both batch and real-time workloads.
Develop and optimize streaming pipelines using technologies such as Kafka and event-driven architectures.
Implement high-throughput, low-latency backend services that power data activation and customer-facing APIs.
Improve system performance, scalability, reliability, and cost efficiency across large-scale production environments.
Contribute to data modeling strategies, storage optimization, and compute efficiency across platforms.
Enable AI-driven workflows by building robust data pipelines and execution layers that support downstream intelligence.
Collaborate with product, AI/ML, and platform teams to deliver end-to-end solutions.
Drive operational excellence through observability, monitoring, and incident response.
Mentor engineers and contribute to improving engineering standards and practices.
Define architecture across multiple systems and domains.
Drive platform-level technical strategy and long-term direction.
Solve cross-cutting scalability, reliability, and performance challenges.
Mentor engineers and elevate engineering standards across teams.
Core Technical Requirements
8–12+ years of experience building distributed systems in production environments.
Strong experience working with data-intensive systems and large-scale data pipelines.
Proficiency in Scala, Java, or Python.
Hands-on experience with Apache Spark and distributed data processing.
Experience with streaming systems such as Kafka, Pulsar, or similar technologies.
Strong understanding of data modeling and storage systems (SQL and NoSQL).
Experience with cloud platforms such as AWS, GCP, or Azure.
Strong debugging, performance tuning, and problem-solving skills.
Preferred Experience
Deep experience with Scala and Spark in production environments.
Experience designing and operating real-time and event-driven systems.
Experience with modern data warehouses such as Snowflake or BigQuery.
Familiarity with Kubernetes and containerized environments.
Experience with observability, monitoring, and system performance tuning.
Experience processing unstructured data such as documents, logs, and transcripts.
Exposure to AI/LLM systems, including RAG pipelines, embeddings, and vector search.
Experience building platform or infrastructure layers supporting multiple teams.
Hiring Range:
The specific rate will depend on the successful candidate's qualifications and prior experience.
In addition to competitive base pay, this position also includes an annual incentive opportunity based on target achievement, pre-IPO stock options, benefits including medical, dental, vision, 401(k) with a match, and more, plus generous paid time off, paid holidays, paid day off for your birthday and other paid leave policies to support employees through all phases of life.
Location preference:
Uniphore is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.
For more information on how Uniphore uses AI to unify—and humanize—every enterprise experience, please visit www.uniphore.com.
Uniphore Palo Alto, California, USA Office
1001 Page Mill Road, Palo Alto, CA, United States
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