Top Entry Level Artificial Intelligence Software Engineer Jobs

Build and ship end-to-end production features across APIs, databases, user interfaces, and testing. Work primarily with TypeScript, React, Next.js, NestJS, PostgreSQL, and related technologies. Integrate third-party services, maintain scalable systems, debug production issues, write automated tests, and make architectural improvements. The role requires ownership, proactive communication, independent execution, and effective use of AI coding tools in a remote, async-first team.

Develop and launch AI-powered applications and services from prototype through production. Build paid media integrations, work with APIs and external systems, make full-stack technical decisions, and collaborate with product and AI research teams. The role requires independently solving ambiguous problems, rapidly validating ideas, and delivering scalable, reliable software for enterprise customers.

Supports current and future mission programs across software development, systems engineering, cloud infrastructure, DevOps, cybersecurity, data engineering, networking, and mission integration. Responsibilities include developing applications and APIs, managing cloud and container platforms, designing secure architectures, supporting RMF and ATO activities, building data pipelines, integrating geospatial and mobile systems, and delivering technical documentation, training, and operational support in secure environments.

Build and improve ML and data pipelines, ship full-stack product features, and enhance Mac and Windows desktop applications. Work directly with enterprise customers to clarify requirements, deploy solutions, and turn customer learnings into repeatable product improvements. The role requires high ownership, rapid execution, strong engineering practices, and measurable customer outcomes across applied ML, AI systems, product, and infrastructure.

Build full-stack applications, infrastructure, and reusable platforms for AI researchers. Partner with research teams to understand workflows, develop experiment orchestration and visualization tools, improve data-facing interfaces, and own products from concept through delivery and iteration.

Design, develop, test, and maintain CAD/EDA software tools, automation frameworks, workflows, and deployment pipelines supporting hardware design, physical design, place and route, DRC verification, and manufacturing. Translate stakeholder requirements into scalable solutions, develop job-management tools for distributed systems, administer EDA infrastructure, and collaborate with hardware and manufacturing teams to improve engineering productivity.

Design, develop, maintain, and test simulations for custom AI inference hardware. Collaborate on hardware-software co-design, influence architecture, validate hardware documentation, build debugging tools, and support software development on simulated chips. Work includes integrating simulated chips and hosts, enabling kernel driver testing, validating multi-chip scaling, and debugging early software deliverables.

Leads and mentors application engineers while designing Java and Spring Boot microservices. Oversees application modernization, VM-to-container migration, GCP and GKE deployments, Kubernetes troubleshooting, CI/CD, testing, database integration, observability, and distributed systems. Coordinates technical dependencies across a large application portfolio and collaborates with platform, DevOps, security, and architecture teams. Uses AI-assisted engineering tools to accelerate development, modernization, testing, documentation, and automation.

Lead frontend development for a consumer trading platform using React and TypeScript. Design responsive trading and portfolio interfaces, shape product and technical direction with the founding team, collaborate on APIs and transaction workflows, and ensure strong performance, reliability, and code quality. The role is full-time and in-office in Chelsea, New York City, with preferred experience in trading, fintech, web3, or related platforms.

Develop and evaluate coding tasks for AI systems, including bug fixes, feature development, refactoring, optimization, reproducible environments, reference solutions, automated testing, and deterministic evaluation. The role also involves debugging complex codebases, reviewing contributions, documenting technical decisions, and refining evaluation criteria. Candidates should have strong software engineering fundamentals, backend or API development experience, open-source contributions, and proficiency in at least one listed programming language.