Top Senior Software Engineer Jobs in Kansas City, MO

Design, build, operate, and maintain highly available distributed telemetry systems for Grafana Cloud and open-source projects. Deliver features end to end, improve reliability and performance, review code and designs, manage releases, participate in follow-the-sun on-call rotations, and collaborate remotely with teammates and customers. Work primarily with Go across Kubernetes-based cloud infrastructure and observability systems such as Mimir, Loki, Tempo, and Pyroscope.

The Senior Fullstack Software Engineer will design, implement, and maintain features for Conveyor's SaaS platform, involving both backend and frontend work, while collaborating closely with team members and mentoring juniors.

Design, build, and maintain AI-powered B2B SaaS products across frontend, backend, and AI. Own projects end-to-end, collaborate with product and design, ensure maintainability, mentor teammates, and inform roadmap and implementation decisions.

Leads performance engineering across the organization by designing load-testing and data-generation frameworks, defining benchmarks and standards, analyzing application, database, and system behavior, and guiding teams in performance optimization. Builds reusable tooling and best practices using k6, JavaScript, Grafana, and InfluxDB while addressing scalability, latency, throughput, and reliability challenges in distributed cloud-native systems.

Develop and maintain Intelerad’s Unified Storage Layer and cloud storage services for medical imaging applications. Responsibilities include designing software features, translating support and stakeholder requirements into solutions, debugging cloud infrastructure, managing Kubernetes, Terraform, AWS, and CI/CD environments, reviewing code, documenting systems, and delivering work within an Agile/Scrum team.

Build and maintain authentication and fine-grained authorization systems using Auth0 and OpenFGA. Own OAuth 2.0 and OpenID Connect implementations, identity federation, token flows, and permissions for AI agents. Lead cross-team technical projects, set engineering standards, review code, and mentor engineers. The role requires strong backend development skills and experience with identity providers, ReBAC systems, and complex multi-tenant authorization models.

Build and own OpenRouter’s financial infrastructure, including flexible pricing, quote-to-cash automation, usage metering, billing, payments, collections, double-entry ledgering, provider settlements, and reconciliation. Ensure financial correctness through idempotency, auditability, anomaly detection, and access controls. Partner with Provider, Trust & Safety, Sales, Customer Success, and Finance teams to deliver scalable usage-based financial systems.

Develop and scale backend Java and Kotlin software powering patch discovery, packaging, and deployment across millions of endpoints. Design scalable systems, APIs, data structures, caching solutions, and relational databases while maintaining high testing and code-quality standards. Collaborate with AI workflow, server, platform, design, and product teams. Occasional React or Angular work may support patching workflows and dashboards.

Designs and evolves AWS infrastructure as code using CDK, develops backend software primarily in TypeScript, and applies secure engineering practices for regulated environments. The role contributes to architecture, code reviews, CI/CD pipelines, production support, observability, and on-call operations. It collaborates cross-functionally, builds scalable and reusable systems, supports SOC 2 compliance, and uses AI coding assistants to improve development, testing, and documentation.

Develop and maintain ImmPort backend services, data integration pipelines, and operational systems supporting NIH. Responsibilities include software development with Python, Bash, Java, and Spring; database operations across relational and NoSQL platforms; deployments, release management, incident response, troubleshooting, security, CI/CD, and performance optimization. Collaborate with NIH stakeholders, document architecture, support production operations, attend on-site meetings, and maintain required NIH badge access.

Own and deliver complex full-stack software features for custom labs and related applications. Design, build, test, document, and maintain APIs, databases, user interfaces, infrastructure, and libraries using C# or JavaScript/TypeScript. Lead AI-assisted development workflows, review generated code, ensure security and quality, collaborate with cross-functional Scrum teams, estimate scope, and participate in on-call support.

Design, develop, and modernize scalable front-end UI applications using Angular and TypeScript. Lead large UI projects, enforce coding standards, write technical specifications, optimize performance, mentor engineers, collaborate full-stack, and support CI/CD and Agile practices to deliver user-centric features.

Build and operate production Web3 products across backend, frontend, and smart contracts. Develop Go and TypeScript services, React dApps, SDKs, tokenization infrastructure, smart-wallet integrations, and blockchain event processing. Own on-chain/off-chain architecture, security, testing, observability, deployments, and on-call operations. Review code and configure AI-assisted engineering tools for the repository.

Develop and maintain C#/.NET software for electric and gas distribution engineering tools. Implement features, fixes, integrations, tests, and CI/CD automation while configuring enterprise systems for customer needs. Collaborate in Scrum, participate in reviews, reduce technical debt, document processes, and mentor teammates. The role may involve GIS or Enterprise Asset Management integrations and requires strong software development experience in utility or energy infrastructure environments.

Build and own mobile SDKs for identity and fraud prevention products, focusing on Android development, device-level signal collection, authentication, and emerging agentic identity use cases. Partner with product and engineering leaders on architecture and roadmap decisions, improve observability and production resilience, mentor engineers, and drive technical initiatives across Twilio and Stytch.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The engineer will own projects from discovery through implementation and ongoing improvement, using strong backend engineering skills in Python, Bash, and Linux environments.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, and proficiency in Python, Bash, and Linux, with preferred experience in web crawling, distributed processing, GCP, Terraform, and audio or ML training data.

Build large-scale data pipelines and processing systems that produce high-quality audio datasets for AI model training. Discover data sources, optimize throughput, quality, and cost, operate production systems, and collaborate with AI scientists on next-generation models. The role requires strong backend software engineering, independent problem-solving, and ownership of projects from discovery through continuous improvement.

Build and operate large-scale data pipelines that collect audio data for AI training. Responsibilities include discovering data sources, optimizing data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale data pipelines for AI training datasets, including discovering audio sources, processing data efficiently, optimizing quality and cost, and collaborating with AI scientists. Own engineering projects from discovery through implementation and continuous improvement. The role requires strong backend software engineering, production systems experience, Python and Bash proficiency, and independent problem-solving in ambiguous environments.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role owns engineering projects end to end and requires strong backend software engineering skills, Python and Bash proficiency, production systems experience, and independent problem-solving.

Build and optimize petabyte-scale data pipelines for AI model training, including audio data acquisition, large-scale processing, and dataset quality improvements. Collaborate with AI scientists, operate production systems, and independently manage engineering projects from discovery through continuous improvement. The role emphasizes backend software engineering, scalability, throughput, cost efficiency, and reliable infrastructure.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing data-processing systems, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.