Kubernetes AI Jobs

Discover the latest remote and onsite Kubernetes AI roles across top active AI companies. Updated hourly.

Check out 301 new Kubernetes AI roles opportunities posted on The Homebase

Speech Software Engineer

New
Top rated
ASAPP
Full-time
Full-time
Posted

Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.

$215,000 – $235,000
Undisclosed
YEAR

(USD)

New York, United States
Maybe global
Hybrid
Python
Go
Kubernetes
Docker
AWS

Senior Staff Systems Engineer

New
Top rated
ASAPP
Full-time
Full-time
Posted

Drive the architectural vision for the GenerativeAgent product by designing and building a highly scalable, multi-agent platform for real-time voice and text customer service experiences across various industries. Act as a technical authority and advisor for multiple engineering teams, develop system design and technical roadmaps, and define communication, state management, and orchestration patterns for multi-agent systems. Design and implement scalable, multi-tenant deployment architectures, own and define system-level SLOs/SLIs focusing on latency, cost-efficiency, and fault tolerance, identify systemic risks with proactive mitigation strategies, partner with Security and Compliance teams to meet regulatory and security requirements, lead post-incident analysis and improvements, and collaborate cross-functionally with Product, Customer Engineering, Site Reliability Engineering, TPMs, and Research to translate business requirements into system designs and productionize ML research. Mentor senior engineers and communicate complex technical concepts to both technical and non-technical stakeholders.

$240,000 – $265,000
Undisclosed
YEAR

(USD)

New York, United States
Maybe global
Hybrid
Python
Go
Kubernetes
AWS
GCP

Software Engineer, Backend

New
Top rated
Mashgin
Full-time
Full-time
Posted

The backend developer will own major feature development and work directly with founders on product development from end to end. Responsibilities include working with a small interdisciplinary team across hardware, software, and design to build new products from scratch; building new features and designing new architecture to address challenging problems; building backend infrastructure to perform scalable training in the cloud; rethinking and refactoring existing codebases for scale; and continuously improving and maintaining code in production. The role involves full ownership throughout the entire product lifecycle, including idea generation, design, prototyping, execution, and shipping, contributing to multiple parts of the codebase in various programming languages.

$115,000 – $210,000
Undisclosed
YEAR

(USD)

Palo Alto, United States
Maybe global
Onsite
Python
C++
Go
Java
Docker

Software Engineer, Codex Runtime

New
Top rated
OpenAI
Full-time
Full-time
Posted

The responsibilities include shaping the evolution of Codex by identifying how teams use and break AI-powered software engineering, driving changes across product, infrastructure, and model behavior to improve reliability. Building core team and enterprise primitives to enable Codex usability at scale, such as container orchestration, virtual machine provisioning/configuration, execution sandboxes, shared block storage, RBAC, admin and audit surfaces, usage and pricing controls, managed configuration and constraints, and analytics for visibility into Codex usage. Designing and owning secure, observable, full-stack systems that power Codex across web, IDEs, CLI, and CI/CD, integrating with enterprise identity and governance systems (SSO/SAML/OIDC, SCIM, policy enforcement), and developing data-access patterns that are performant, compliant, and trustworthy. Leading real-world deployments and launches by working with customers and go-to-market teams to roll out Codex across teams, using live usage and operational signals to iterate and improve the product and platform based on real-world feedback.

$255,000 – $325,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Onsite
Python
Go
Kubernetes
Docker
CI/CD

Freelance Software Developer (Kotlin) - AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As an AI Tutor in Coding specializing in Kotlin development, the responsibilities include designing high-quality technical content, examples, and explanations demonstrating best practices in Kotlin development; collaborating with engineers to ensure accuracy and consistency across code samples, tutorials, and developer guides; exploring modern Kotlin frameworks and tools to create practical, real-world examples for learning and testing; and continuously refining content based on feedback, emerging patterns, and advances in the Kotlin ecosystem. The role also involves contributing to projects aligned with skills by creating training prompts and refining model responses to help shape the future of AI while ensuring technology benefits everyone.

$80 / hour
Undisclosed
HOUR

(USD)

United States
Maybe global
Remote
Python
Docker
Kubernetes
AWS
GCP

Senior Machine Learning Engineer

New
Top rated
PhysicsX
Full-time
Full-time
Posted

Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.

Undisclosed

()

Singapore
Maybe global
Hybrid
Python
Go
MLOps
Docker
Kubernetes

Senior Forward Deployed Software Engineer

New
Top rated
PhysicsX
Full-time
Full-time
Posted

Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) empowered to make implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from Data Scientists and Simulation Engineers using the product.

Undisclosed

()

Singapore
Maybe global
Hybrid
Python
Go
Docker
Kubernetes
CI/CD

Senior CFD Engineer

New
Top rated
PhysicsX
Full-time
Full-time
Posted

Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.

Undisclosed

()

Singapore
Maybe global
Hybrid
Python
Go
Docker
Kubernetes
CI/CD

Principal Machine Learning Engineer

New
Top rated
PhysicsX
Full-time
Full-time
Posted

The role involves building a platform used by Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. The candidate will work on a focused, stream-aligned, and cross-functional team that includes back-end, front-end, and design members, empowered to make its own implementation decisions towards meeting its objectives. Responsibilities include gathering and leveraging domain knowledge and experience from the Data Scientists and Simulation Engineers using the product, taking ownership of work from implementation to production, ensuring quality, scalability, and observability at every step, which includes testing, containerization, continuous integration and delivery, authentication, authorization, telemetry, observability, and monitoring.

Undisclosed

()

Singapore
Maybe global
Hybrid
Python
Go
Docker
Kubernetes
CI/CD

Principal Forward Deployed Software Engineer

New
Top rated
PhysicsX
Full-time
Full-time
Posted

Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.

Undisclosed

()

Shoreditch, Singapore
Maybe global
Hybrid
Python
Go
Docker
Kubernetes
CI/CD

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[{"question":"What are Kubernetes AI jobs?","answer":"Kubernetes AI jobs involve orchestrating containerized machine learning applications at scale. Professionals in these roles manage container deployment for AI workloads, distribute computational tasks across nodes for model training, allocate GPU resources efficiently, and automate ML pipelines. They typically work with frameworks like TensorFlow and PyTorch while ensuring high availability for production AI systems through automated scaling and self-healing capabilities."},{"question":"What roles commonly require Kubernetes skills?","answer":"Roles requiring Kubernetes skills include Machine Learning Engineers who deploy models to production, MLOps Engineers working with platforms like Kubeflow, Data Engineers managing processing pipelines, Platform Engineers supporting agentic AI applications, DevOps/SRE professionals handling containerized deployments, and Cloud Architects designing scalable environments. These positions typically involve maintaining infrastructure that supports the complete machine learning lifecycle."},{"question":"What skills are typically required alongside Kubernetes?","answer":"Alongside Kubernetes, employers typically look for container fundamentals (especially Docker), distributed systems knowledge, CI/CD pipeline experience, and cloud platform familiarity. Programming skills are essential for deployment scripts, while experience with ML frameworks like TensorFlow or PyTorch is valuable for AI-specific implementations. Understanding storage solutions, Kubernetes operators, and automated infrastructure management rounds out the typical skill requirements."},{"question":"What experience level do Kubernetes AI jobs usually require?","answer":"Kubernetes AI jobs typically require mid to senior-level experience. Employers look for professionals who understand containerization concepts, have worked with distributed systems, and can manage complex ML workflows. Prior exposure to cloud environments where Kubernetes runs is important. Candidates should demonstrate practical experience with CI/CD pipelines and familiarity with at least one major ML framework."},{"question":"What is the salary range for Kubernetes AI jobs?","answer":"Kubernetes AI jobs command competitive salaries due to the specialized intersection of container orchestration and machine learning skills. Compensation varies based on experience level, location, and specific industry. Roles requiring both strong AI expertise and Kubernetes infrastructure management typically offer premium compensation compared to general software engineering positions, reflecting the high market value of these combined skill sets."},{"question":"Are Kubernetes AI jobs in demand?","answer":"Kubernetes AI jobs are in high demand as organizations increasingly adopt containerized applications for machine learning workloads. The growth is driven by enterprises scaling their AI operations, edge computing applications, and the need for platform-agnostic infrastructure. Companies seek professionals who can manage the complexity of distributed ML systems, particularly for high-availability production environments and automated ML pipelines."},{"question":"What is the difference between Kubernetes and Docker in AI roles?","answer":"Docker creates containerized applications while Kubernetes orchestrates those containers at scale. In AI roles, Docker is used to package ML applications with their dependencies, while Kubernetes manages deployment across clusters, automates scaling during training, and handles resource allocation for GPUs. Docker provides consistency between environments, while Kubernetes adds critical production capabilities like load balancing, self-healing, and distributed computing for AI workloads."}]