Top Software Engineer Jobs

Develop and maintain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, cloud integration, distributed systems, RESTful services, testing, validation, troubleshooting, and sustainment. The role also involves Kubernetes, infrastructure as code, CI/CD, Linux scripting, Hadoop-based big-data technologies, microservices, LLMs, RAG, and security-conscious cloud engineering.

Lead software engineering for a digital marketing platform, defining technical vision and multi-year product roadmaps. Manage and mentor a team, oversee scalable architectures and API integrations, conduct code reviews, and collaborate with product, design, data, and technology teams. The role focuses on AWS-based systems, AI-driven automation, data unification, agile delivery, and building high-quality software solutions.

Design, develop, deploy, and operate distributed backend systems supporting large-scale, low-latency online model inference. The role owns inference platform technical direction, scalability, reliability, observability, and cloud infrastructure on GCP and Kubernetes. Responsibilities include partnering with ML engineers, optimizing high-throughput services, implementing DevOps and SRE practices, and monitoring production systems with Prometheus and Grafana.

Develop and maintain full-stack web applications, integrate third-party systems and APIs, automate operational processes, troubleshoot live systems, and contribute to AI-driven features. The role requires independent problem-solving, ownership of delivered software, and effective collaboration in a fast-paced environment. This is a hybrid position based in Denver, Colorado, or Rockville, Maryland.

As a Founding AI Backend Engineer at Pakket, you'll enhance backend systems, debug the CV pipeline, and deploy services across warehouses, ensuring operational reliability and rapid feature deployment.

Lead the Content Foundations engineering team and evolve Scribd’s high-scale document ingestion platform into a real-time, AI-native system. Responsibilities include setting technical strategy, integrating AI and ML capabilities, overseeing distributed data infrastructure, ensuring security and operational reliability, managing the roadmap, hiring and developing backend and AI data engineers, and partnering with product and engineering leaders across the organization.

Design, build, test, and launch reliable, scalable business insurance products and AI-powered customer experiences. Partner with product, design, and cross-functional teams to iterate using feedback and metrics. Lead multi-quarter projects from scoping through delivery, communicate technical tradeoffs, and mentor engineers through pairing, code review, and knowledge sharing.

Builds customer-facing features for an AI-powered revenue platform. Responsibilities span intelligent CRM integrations, autonomous outreach workflows, and end-to-end revenue engine development, with a focus on making complex AI capabilities easy to use for businesses in the physical economy.

Build and lead end-to-end AI products for enterprise clients as an individual contributor. Responsibilities include setting technical direction, writing production-ready code, applying software engineering practices to AI systems, collaborating with small engineering teams, engaging directly with senior client stakeholders, and delivering multiple applied AI products across diverse domains.

Hands-on fellowship for university students and recent graduates interested in AI, backend engineering, cloud infrastructure, and software delivery. Fellows may research technology trends, build internal tools and automation workflows, explore LLM-powered agents, prototype backend or cloud solutions, and prepare technical documentation and client-readiness materials. The role emphasizes ownership, reliability, clear technical communication, and practical project experience.

Design, develop, test, deploy, and operate large-scale distributed systems and AI infrastructure services. Build resource management, workload orchestration, capacity management, and control-plane capabilities using C# on Kubernetes and AKS. Troubleshoot production issues, improve scalability and reliability, collaborate cross-functionally, participate in architecture and design reviews, and provide operational support and on-call ownership.

Build full-stack software, backend services, APIs, frontend experiences, and shared integration capabilities supporting AI initiatives across Keeper’s products. Integrate LLMs, agents, and tool-calling workflows into secure production environments while maintaining zero-knowledge principles. Troubleshoot cross-product systems, improve reliability and observability, contribute to architecture and code reviews, automate testing and CI/CD, and collaborate with AI, product, and engineering teams.

Architect and build high-performance distributed infrastructure for batch and streaming data across global regions. Optimize state management, checkpointing, JVM performance, and exactly-once processing. Develop AI infrastructure including vector indexing, agentic workflows, and real-time data streaming. Own the full software development lifecycle, contribute to shared tooling and SDKs, and mentor engineers through technical leadership and design reviews.

Leads enterprise software engineering enablement, AI and innovation strategy, and large-scale technology transformation. Drives adoption of modern engineering practices, cloud technologies, automation, AI solutions, governance, and modernization initiatives. Advises executives, establishes technology standards and transformation frameworks, partners across business and technology teams, manages strategic vendors, and builds high-performing engineering organizations. The role requires delivering measurable outcomes in a complex, regulated banking environment.

Develop, deploy, and support full-stack applications using React, JavaScript or TypeScript, C#, .NET, relational databases, REST APIs, and Azure. Partner with business users to modernize prototypes into secure, scalable production applications. Apply enterprise architecture, authentication, authorization, security, performance, and vulnerability-management practices. Build integrations, troubleshoot production issues, and collaborate with infrastructure, security, data, and architecture teams. Use AI-assisted development tools such as Claude Code and Codex extensively.

Develop secure, scalable full-stack applications using Java, JavaScript, RESTful APIs, databases, and modern front-end frameworks. Apply AI-assisted development practices including code assistance, automated refactoring, intelligent testing, troubleshooting, security scanning, and infrastructure automation. The role involves backend, frontend, and automation development, system modernization, performance improvement, and collaboration with product and engineering teams. An active Top Secret clearance is required, with SCI preferred.

Leads the strategy, architecture, and delivery of Cognex’s Applied AI platform for industrial automation. Directs applied AI research, cloud and edge platform engineering, production reliability, security, scalability, observability, and cost efficiency. Partners across product, hardware, UX, and R&D to ship customer-focused capabilities, drives platform adoption, and builds a high-performing engineering organization. Requires extensive software engineering and leadership experience, cloud and backend expertise, and familiarity with modern AI application architectures.

Leads a 25+ person engineering organization through two Directors, overseeing delivery quality, technical architecture, platform scalability, AI-enabled engineering practices, and new product development. Partners with Product and the Chief Scientist to align roadmaps, launch new manufacturing intelligence products, improve platform efficiency and reliability, develop engineering leaders, and represent engineering strategy and risks to executives.

Lead software engineering and developer advocacy initiatives across Barclays. Build production-grade applications, technical demonstrations, proof-of-concepts, workshops, and reference implementations while promoting cloud, automation, CI/CD, AI, and modern engineering practices. Engage engineering communities through hackathons, technical content, mentoring, and knowledge-sharing programs. Partner with platform owners and technology leaders to improve developer experience, drive adoption, influence technical strategy, and strengthen engineering quality, security, and productivity.

Develop and maintain full-stack Java applications using Java EE, RESTful microservices, Oracle, Angular, TypeScript, and Node.js. Use AI coding assistants for code generation, debugging, optimization, and prompt-driven development while validating output for accuracy, performance, and security. Apply JVM performance profiling, software engineering best practices, and potentially CI/CD, DevOps, Docker, Unix scripting, caching, and SAML technologies. The role requires four days onsite weekly in Lansing or Farmington Hills, Michigan.

Develop immersive augmented and virtual reality experiences using Unity and Unreal Engine. Build custom software connecting AR/VR applications with advanced analytics and AI, collaborate with designers and data scientists on quantitative visualizations, and create technical documentation and design artifacts. The role requires solution planning, cross-functional collaboration, and development aligned with project objectives.

Supports the CTO with technology research, AWS and AI exploration, backend development, cloud-native prototypes, REST API experiments, system design, technical documentation, and architecture planning. The intern will build proof-of-concept solutions using C#, Python, .NET, AWS services, and machine learning technologies while evaluating vendors and emerging tools.

The Software Engineer - AI Systems will design and implement systems to coordinate LLMs, build testable software stacks, conduct research, and collaborate with team members to create AI-powered security solutions.

Set technical direction for an AI-native software engineering team building security workflow tools. Lead complex architectural decisions, guide engineers, review designs and code, and collaborate with technical leaders across teams. The role requires extensive hands-on software engineering expertise, strong architectural judgment, mentoring ability, and experience making decisions for scalable systems. Preferred experience includes security tooling, AI agent APIs, MCP servers, and AI-native engineering practices.

Debug, profile, and optimize AI cluster networking and GPU performance across Linux kernels, PCIe, SmartNICs, RDMA/RoCEv2, NCCL, and application communication. Investigate boot, driver, latency, throughput, congestion, and hardware trace issues; analyze packets, logs, profiles, and observability data; and collaborate with hardware and software teams to remove end-to-end bottlenecks.