Top Software Engineer Jobs

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Leads architecture and technical strategy for the Arena SaaS platform. Responsibilities include designing distributed systems, improving scalability, reliability, security, and developer productivity; leading complex initiatives across engineering teams; driving modernization; evaluating emerging and AI-enabled technologies; mentoring senior engineers; and partnering with Engineering and Product leadership. The role requires hands-on Java expertise and strong experience with enterprise SaaS platforms, APIs, cloud-native architectures, and technical influence across multiple teams.

Define Scribd’s technical vision for agentic engineering and establish architecture, patterns, and guardrails for AI-assisted software development. Lead internal developer tools, libraries, platforms, and engineering workflow improvements across codebases, CI/CD, and infrastructure. Partner with engineering leadership on ambiguous cross-organizational initiatives, mentor senior and staff engineers, and drive adoption of coding-agent capabilities throughout the organization.

Lead a small autonomous engineering team combining experienced developers with AI coding agents. Own architecture, technical direction, software implementation, delivery, quality, security, reliability, and production operations. Establish AI-assisted engineering workflows, testing and deployment guardrails, and scalable development practices. Mentor two engineers, translate business objectives into technical solutions, resolve blockers, and continuously improve engineering productivity and software quality through hands-on leadership.

Build, test, deploy, and support production software, including product features, APIs, integrations, and internal tools. Use AI coding agents such as Claude Code and OpenAI Codex to accelerate development while reviewing generated code for correctness, security, and performance. Write automated tests, debug issues, participate in code reviews, maintain documentation, and own features through production readiness. Collaborate with engineers, clarify requirements, and continuously improve software delivery practices.

Build and operate scalable AI platform infrastructure enabling secure, reliable access to foundation models and AI agents. Responsibilities include developing model gateways, provider integrations, routing, usage controls, observability, governance, and self-service platform capabilities. The role involves architecting distributed systems, improving reliability and security, collaborating across engineering and product teams, mentoring engineers, and establishing high-quality engineering practices.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and developer trust. Provide clear, opinionated feedback, distinguish response-quality levels, and help define standards for excellent interactions with tools such as OpenAI Codex, Claude Code, and Cursor. This contract role requires staff- or principal-level software engineering experience and familiarity with modern AI-assisted development workflows.

Architect and operate Cisco’s AI Compute Platform, developing distributed control-plane services, Kubernetes platforms, GPU schedulers, APIs, controllers, and automated remediation systems. The role manages multi-cluster environments, tenant quotas, workload routing, GPU allocation, and reliability for large-scale training and inference workloads. It also leads architecture reviews, establishes engineering standards, mentors engineers, and drives Infrastructure as Code, GitOps, CI/CD, observability, incident response, and operational excellence.

Build Orpex’s AI workforce platform end-to-end as a founding engineer, member of technical staff, or applied AI engineer. Responsibilities include defining product outcomes, developing and deploying production software, speaking with users, iterating quickly, handling infrastructure and UI work, and supporting hiring. The role begins in Vienna and requires relocation to San Francisco by January 2027. Visa sponsorship and relocation support are provided.

Leads and develops an infrastructure, operations, and shared-platform engineering team supporting Microsoft Monetize’s high-scale real-time advertising platform. Owns roadmap, architecture, delivery, reliability, and operational excellence across hybrid Azure and on-premises environments. Guides low-latency C/C++ services, distributed caching, streaming pipelines, model-backed serving, traffic management, infrastructure modernization, CI/CD, observability, incident response, and developer productivity while partnering across engineering, product, machine learning, and infrastructure teams.

Lead the reliability and scaling of Horizon AI’s autonomous platform for production GPU infrastructure. Harden agent dispatch with circuit breakers, retries, health-aware routing, and observable error handling. Build caching and event-streaming capabilities, determine and implement the orchestration strategy, introduce infrastructure as code, enable concurrent agent sessions, and own service standards, testing, CI, observability, and deployment practices. Serve as technical deputy and collaborate with Systems and SRE teams.

Designs, develops, tests, troubleshoots, and maintains secure, scalable software solutions using Java, with exposure to React and AWS. Creates architecture and design artifacts, supports application resiliency and CI/CD, analyzes data to improve systems, and uses enterprise-authorized AI-assisted development tools while validating outputs for correctness, performance, and security.

Develop AI agents and secure MCP servers for IAM and enterprise applications. Build Python and Node.js backend services, REST APIs, and modern React or Vue.js interfaces. Integrate LLMs and generative AI into existing workflows while applying prompt engineering, tool integration, security, scalability, and performance best practices. Collaborate with engineering and product teams to deliver AI-powered enterprise solutions.

Build backend services, data pipelines, APIs, distributed systems, developer tools, and shared platform capabilities for AI products. Develop ML experiment tracking, artifact management, and automated training and evaluation pipelines. Improve reliability, latency, and throughput while collaborating with Product and ML teams. Own scoped projects involving implementation, testing, maintenance, and clean API design for enterprise and government customers.

Develops secure, scalable software solutions for JPMorganChase Asset Management. Responsibilities include architecture and application development, production coding, troubleshooting, database optimization, cloud deployment, event processing, infrastructure automation, and responsible use of AI-assisted development tools. The engineer also analyzes data, creates technical artifacts, supports application resiliency and security, and contributes to agile team practices and reusable engineering patterns.

Build and ship end-to-end product features, debug and resolve production issues, partner with Product on UX and direction, use AI heavily for development and prototyping, and iterate quickly across the stack to improve quality and speed.

Lead software architecture for robotics and AI systems using C++ and Python. Design real-time robotics control, planning, optimization, generative AI, operator GUIs, diagnostics, simulation, and production integrations. Establish scalable infrastructure, CI/CD, containerization, testing, and deployment practices. Integrate robotics stacks, hardware, sensors, and manufacturing systems while leading technical reviews, mentoring engineers, commissioning systems, and troubleshooting production hardware.

Develop and modernize full-stack applications using Java Spring Boot and Angular. Leverage AI coding assistants to accelerate development, generate tests, and support modernization while ensuring adherence to security, AWS, and PostgreSQL standards. Review, debug, optimize, and architect AI-generated code; collaborate on complex initiatives and mentor teams. The role requires an active Top Secret clearance and on-site work in Washington, DC.

Owns the implementation, hardening, tuning, and polish of game AI behaviors and systems for shipping quality. Collaborates with design, animation, and other disciplines to create believable enemy behavior and strong player experiences. The role requires expertise in Unreal Engine, modern game AI architecture, animation systems, and C++, along with experience shipping at least one game focused primarily on AI, preferably a AAA action or FPS title.

Design and implement GPU performance profiling tools focused on AI applications. Build features used by CUDA developers, collaborate with NVIDIA compiler and CUDA driver teams, and influence tool design. The role requires strong C++ software engineering experience, GPU hardware knowledge, and familiarity with performance tools, with preferred expertise in CUDA kernel programming, profiling, and DWARF parsing.

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 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.