Software Engineer AI

About the Team

At Dice, we believe that delivering an exceptional candidate experience — the end-to-end journey a job seeker takes to discover, evaluate, and land their next role — is essential to the success of our platform. This team plays a critical role in building the next generation of that experience, contributing across a modern full-stack environment. We value engineers who are curious, collaborative, and committed to making an impact for thousands of job seekers.

We are an AI-native team. We actively use AI coding assistants (e.g., Claude Code, Codex, Gemini CLI) to accelerate development, sharpen code quality, and focus our energy on higher-impact engineering work.

About the Role

As a Software Engineer at Dice, you’ll contribute to the design, development, and delivery of high-impact product features in a modern, cloud-native stack. As a Software Engineer, you’ll be expected to provide technical support within the team by adhering to architectural decisions, working collaboratively teammates, and helping maintain a high standard of quality and maintainability. You’ll contribute cross-functionally with Product, Design, and Data partners to achieve ongoing outcomes and implement sound technical solutions. 

Being AI-native is central to how we work. We expect engineers in this role to go beyond using AI as a simple chat assistant, embracing tools like Claude Code as core parts of the development workflow to increase productivity such as automating repetitive tasks by generating skills and leveraging subagents, and apply best practices through prompt engineering to get the most out of the AI tools we use at every stage of the software lifecycle. 

Why We’re Hiring for This Role

As we scale and modernize our platform, we’re looking for engineers who can take ownership of complex systems, communicate effectively with collaborators and team members, deploy AI-powered features, and contribute to product and architectural decisions. This role is ideal for someone who enjoys working across the stack, solving real user problems, working with AI, and influencing both code and culture.

In the Short Term, You Will:

Ramp Up on Systems and Codebases

Gain familiarity with our applications, services, tools, and infrastructure. Develop an understanding of how your team’s work fits into the broader product and architecture.

Establish Technical Ownership

Take responsibility for features and systems, provide thoughtful code reviews, and contribute to team design discussions. You’ll be trusted to make solid technical decisions and defend them clearly when needed while staying open to feedback.

Collaborate and Align

Work closely with engineers, product managers, designers, and stakeholders to align on priorities and deliver incremental value. Help improve the team's planning and execution rhythm.

In the Long Term, You Will:

Drive Technical Excellence

Help evolve our systems through well-designed, scalable AI architecture. Reduce tech debt, improve performance, and contribute to shared engineering standards.

Deliver Meaningful Business Impact

Own end-to-end delivery of product features that support business goals and enhance user experience. Influence the roadmap by advocating for technical investments that align with long-term value.

What You Bring to the Team:

• Proven experience building and deploying production-grade applications with a track record of delivering value through practical, maintainable solutions

• Strong technical skills and experience in cloud-based technologies

• Exposure to the development and deployment of ML and AI features into a Production environment.

• Ability to navigate ambiguity, make sound decisions, and adapt to changing priorities

• A collaborative, team-first mindset — you know the best outcomes come from working well together, and you actively contribute to a supportive, inclusive team environment

• Technical depth and experience that allows you to contribute to design discussions, assist in breaking down complex tasks into meaningful deliverables, and ensure a high standard of engineering quality

• A sense of urgency, accountability, and ownership, you take pride in your work and team achievements.
• Strong communication, problem-solving, and collaboration skills, with the confidence to advocate for ideas while being open to alternative perspectives

Required:

• Proven experience building and maintaining business-critical applications using Node.js and TypeScript in a production setting

• Hands-on experience developing scalable, maintainable RESTful APIs in production environments

• Experience with front-end frameworks such as NextJS or React

• Demonstrated experience building highly available applications on AWS infrastructure

• Experience implementing and working with CI/CD pipelines to support automated testing and deployments

• Ability to apply quality best practices and implement automated testing using tools such as Playwright

• Solid understanding of architectural patterns (event-driven, serverless, SOA, and others)

• Practical understanding of how to implement traditional Machine Learning models and LLMs in production, including knowledge of model selection criteria and architectural trade-offs

• Familiarity with AI/ML tooling such as LangChain, LangGraph, LangSmith, Vercel AI SDK, LiteLLM,or equivalent frameworks

• Hands-on experience deploying LLM-powered features into production, such as simple LLM API calls, RAG (Retrieval-Augmented Generation) systems, Vector Stores, and MCP Servers
• Familiarity with LLM guardrails and safeguards, monitoring, and observability practices for production AI systems

Preferred:

• Understanding of different programming paradigms such as OOP and Functional

• Experience with highly available, highly scalable SaaS applications

• Working knowledge of logging and observability best practices

• Familiarity with DevOps principles and the ability to work closely with platform teams

• Knowledge of performance tuning, cost optimization, or scalability in a cloud-native environment

• Comfort navigating ambiguity to define and implement technical solutions

• Experience collaborating with cross-functional partners (Product, Design, Data)

• Familiarity with GraphQL APIs

• Working knowledge of container-based technologies (Docker, Fargate, Kubernetes, or other)

• Experience working as an AI-native engineer — including creating agent skill files, configuring and orchestrating subagents, or building custom AI tooling to accelerate engineering workflows