Top Software Engineer Jobs

Develops and sustains cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, installation, enhancement, maintenance, and issue resolution. The role works with distributed NoSQL systems, big-data technologies, cloud scalability, machine learning, large language models, RAG, agentic workflows, microservices, Kubernetes, observability, and infrastructure-as-code tools.

Leads and mentors cross-functional engineering teams building and operating distributed, API-driven, cloud-native platforms. Owns technical strategy, architecture, delivery outcomes, engineering excellence, operational reliability, and API lifecycle governance. Drives Agile, DevOps, cloud migration, observability, and incident management practices. Establishes responsible AI adoption across software development and guides delivery of AI-enabled products, including agentic solutions, with appropriate evaluation and controls.

Develop and support compliance technology solutions for regulatory, surveillance, supervision, and risk-control functions. Responsibilities include Java backend development, distributed systems architecture, API and event-driven integrations, cloud and big-data solutions, testing, monitoring, incident support, and operational administration. The role partners with Compliance, Risk, Data, Architecture, and business teams to deliver secure, scalable, auditable systems aligned with regulatory requirements and enterprise standards.

Develop and maintain web applications for a government customer within an Agile team. Responsibilities include researching, designing, coding, testing, troubleshooting, implementing business logic, developing web APIs and conversion routines, supporting production applications, conducting impact analysis, and collaborating with analysts, UI/UX developers, testers, project leads, and customers.

Develop and deploy end-to-end applications supporting intelligence mission workflows. Responsibilities include gathering requirements, building user-facing applications, developing RESTful APIs, integrating front-end and back-end services, creating data visualizations, working with cloud environments and data repositories, and establishing secure CI/CD pipelines. The role collaborates with mission stakeholders, data scientists, and analysts to deliver operational solutions.

Leads Unlearn’s engineering organization across core engineering, applications, ML/AI Ops, infrastructure, and forward deployment. Owns system quality, reliability, compliance, security, execution, hiring, team design, and engineering roadmap delivery. Partners with Product, R&D, Delivery, and company leadership to translate strategy into shipped AI-powered platforms, tools, and services in a regulated environment. Establishes an AI-centric culture and ensures engineering commitments align with business objectives.

Own architecture and direction for Suno’s AI Operations Engineering team. Build intelligent agents, workflows, internal tools, APIs, authentication patterns, and self-service deployment capabilities that help non-engineering teams scale. Evaluate third-party tools, integrate customer experience systems such as Zendesk, and partner with legal, finance, people, support, and content operations to identify and solve workflow challenges.

Build AI agents, workflows, internal tools, APIs, authentication patterns, and customer experience integrations that improve operations across support, legal, finance, people, and content teams. Evaluate build-versus-buy decisions, support self-service app deployment, and partner with stakeholders to identify automation opportunities. The role requires full-stack engineering experience and comfort working in an early-stage, rapidly evolving environment.

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.

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 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.

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.

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.

Own the architecture and technical direction of a heterogeneous AI SoC runtime spanning sensor ingest, preprocessing, inference, postprocessing, and application delivery. Define execution, memory, synchronization, driver, firmware, compiler-runtime, observability, resilience, and validation architectures. Lead cross-functional engineers while contributing hands-on to production systems software, performance optimization, APIs, and low-level interfaces for robotics and edge AI workloads.

Leads the architecture and development of customer-facing SDKs, APIs, CLI tools, examples, documentation, developer workflows, and portal integrations for an AI SoC platform. Partners across AI, multimedia, robotics, tools, release, and SQA teams to deliver reliable installation, deployment, compatibility, and release experiences. The role also hires, mentors, and leads engineers focused on developer tools, documentation systems, APIs, and sample applications.

Define and build the software architecture underlying Velaura’s Physical AI platform, including SDKs, runtimes, drivers, firmware, compilers, developer tools, and workload orchestration. Lead hardware/software co-design, performance optimization, profiling, and integration with ROS2, DDS, robotics, drones, and autonomous systems. Establish engineering processes and help develop a high-performing platform software team enabling efficient AI deployment across heterogeneous computing systems.

Lead architecture and implementation of LangSmith platform features across Go, Python, and TypeScript. Collaborate with product and design teams, mentor engineers, drive project timelines, and deliver clean, tested software. Build and scale APIs and data-intensive applications using backend and frontend technologies, PostgreSQL, and Redis while engaging with enterprise customers and internal stakeholders.