Kubernetes AI Jobs

View detail

The Platform Incubation Engineer role within Forward Deployed Engineering (FDE) involves architecting and building new platform capabilities by turning frontier customer signals into concrete designs, implementations, and APIs. Responsibilities include incubating platform bets end-to-end by forming hypotheses, shipping initial capabilities, and iterating based on real usage feedback. The engineer will embed with design partners to conduct technical discovery and translate needs into product and platform requirements, partner with customer-tagged FDEs to deploy, debug, capture repeatable patterns, and improve the platform based on field learnings. They will also design and run pilot programs, collaborate closely with core product and engineering teams to align architecture and production efforts, and drive adoption outcomes by measuring usage, identifying blockers and failure modes, and prioritizing platform increments to unlock repeatable value.

View detail

Design and deliver production-grade AI agents for healthcare that handle sensitive patient and member interactions while maintaining strict HIPAA compliance. Drive the Agent Development Life Cycle with ownership from pilot through deployment and iteration. Partner with healthcare leaders to understand challenges and build AI agents that address operational needs. Develop expertise in healthcare systems, workflows, and data/privacy standards to create trustworthy AI experiences. Guide and contribute to the evolution of Sierra's core platform based on customer feedback. Examples of projects include building AI agents for insurance networks, providers, primary and urgent care clinics, and healthcare financial platforms, as well as experimenting with voice models for secure interactions.

View detail

Design and deliver production-grade AI agents for healthcare that handle sensitive patient and member interactions while maintaining strict HIPAA compliance. Build and ship highly performant, reliable, and empathetic AI agents that help with understanding coverage, finding providers, scheduling appointments, navigating billing, and more. Have complete ownership and autonomy over the Agent Development Life Cycle from pilot through deployment and continuous iteration, building, tuning, and evolving AI agents in production environments serving healthcare payers, providers, and platforms. Work directly with healthcare leaders including executives and technical teams at health plans, provider networks, and healthcare technology companies to understand and solve their most pressing challenges. Develop deep expertise in healthcare systems and workflows, including integrations across EHR and patient access platforms, payer and provider operations, and healthcare data and interoperability standards. Translate complex healthcare knowledge into trustworthy AI experiences. Use customer insights to guide the evolution of Sierra's core platform by surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape the future of AI agent development in healthcare.

View detail

Set technical strategy and oversee development of a high scale, reliable data platform to manage, visualize and serve large-scale datasets for machine learning model training and validation. Build the data lakehouse for autonomous driving scene datasets, including sensor data, calibration data, and annotation data. Drive development of the Autonomous Driving Data SDK, including scene data search, datasets preparation, and dataset loading. Investigate and resolve performance bottlenecks throughout the data processing pipelines, including data processing latency, data search latency, and Test Procedure coverage. Bootstrap and maintain infrastructure for Data Platform components such as Data Processing Pipeline, Database, Data Lakehouse, and Data Serving. Collaborate with cross-functional teams including ML algorithm, ML application, and Cloud Infrastructure to align ML Platforms with overall Autonomous Driving System Architecture.

View detail

Build and operate backend systems that serve AI-powered features in production; design inference pipelines, orchestration layers, and service boundaries around models; own production concerns including monitoring, logging, alerting, and incident response; optimize latency and throughput across inference, caching, batching, and streaming.

View detail

Build and operate backend systems that serve AI-powered features in production. Design inference pipelines, orchestration layers, and service boundaries around models. Own production concerns including monitoring, logging, alerting, and incident response. Optimize latency and throughput across inference, caching, batching, and streaming.

View detail

The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They design multi-agent systems using different patterns, implement agent logic with frameworks like langchain/langgraph, design evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. The role involves leading technical maturity assessments, working directly with enterprise customers to understand requirements and provide recommendations, and partnering with Engagement Managers and Product/Engineering teams. Responsibilities combine software development, infrastructure/platform engineering, and customer-facing skills focusing on Kubernetes cluster design to multi-agent system architecture to solve real business problems.

View detail

Own customer outcomes end to end by working directly with customers, design partners, and internal stakeholders to define technical scope, success criteria, and delivery milestones, then building and shipping the solution. Design and implement features across the full stack with a focus on solving real problems observed in production environments. Integrate deeply with customer environments by working hands-on with cloud platforms, observability systems, CI/CD pipelines, and incident response workflows to ensure product fit. Diagnose and resolve complex issues across customer deployments, turning support interactions into product insights and durable fixes. Build evaluations and feedback loops to quantify customer value and ensure new capabilities are genuinely impactful. Write clean, maintainable, well-tested code, lead design discussions and code reviews, and help shape the technical direction of the product and the engineering culture of the team.

View detail

The Engineering Manager, Product at Harvey is responsible for owning end-to-end delivery of core product initiatives, from technical design through execution and iteration, while managing a high-performing fullstack engineering team. This includes setting technical direction for large-scale, AI-powered systems such as retrieval over petabyte-scale document collections, product interfaces for AI collaboration, long-horizon planning agents for critical workflows, government-grade security for sensitive data, evaluation of LLMs across extensive taxonomies, and internet-scale data collection across multiple jurisdictions. They must translate product vision into architecture balancing speed, quality, and scalability, lead hands-on contributions to design, code, and architecture reviews, and actively engage in implementation to unblock the team or solve difficult problems. Additionally, they build and grow the team by hiring engineers, setting technical and behavioral standards, and mentoring for career development. The role involves close partnership with Product, Design, and AI teams to identify opportunities and deliver intuitive user experiences, establishing an engineering culture focused on simplicity, ownership, craftsmanship, and continuous improvement, and aligning execution with company goals to support product strategy and long-term impact.

View detail

You will develop ML/AI that leverage and extend the latest state-of-the-art methods and architectures, design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios, collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in production systems, and work on AI-based capabilities and enabling infrastructure to allow semi-autonomous platforms to localise, navigate, and perceive the world in real time.