Top Software Engineer Jobs in Baltimore, MD

Build full-stack prospect demo solutions for ecommerce search and discovery products. Develop JavaScript data collection tools, front-end integrations, ETL workflows, dashboards, alerts, and open-source UI libraries and REST clients. Validate prospect data, support integrations, answer technical questions, collaborate with sales and product teams, and contribute to automated testing, code reviews, and demo infrastructure.

Develop and maintain Salesforce Commerce Cloud ecommerce websites, integrations, feeds, and platform features. Design architecture, troubleshoot performance and caching, manage domains and repositories, ensure platform compliance, mitigate project risks, and optimize site infrastructure. Collaborate with agencies and cross-functional teams while developing and mentoring junior engineers.

Build and improve Grafana’s plugin ecosystem, including Marketplace features, plugin discovery, installation, delivery, review, developer tooling, and portal experiences. Develop across Grafana’s frontend, Go services, and grafana.com; improve reliability and operations in Grafana Cloud; collaborate with plugin developers, partners, product managers, and internal teams to shape the roadmap.

Hands-on Delivery Engineer supporting customer implementations, integrations, technical demonstrations, production events, and issue resolution. The role translates customer and contractual requirements into technical solutions, investigates system behavior across APIs, configurations, logs, and data flows, and collaborates with engineering, product, implementation, operations, security, and infrastructure teams. It is designed for early-career technical talent developing broad expertise across customer delivery, technology, product, and support.

Develop and maintain scalable Java/Spring Boot backends and Angular frontends. Build REST APIs, messaging and caching solutions, write automated tests, conduct code reviews, troubleshoot issues, and participate throughout the software development lifecycle. Collaborate with analysts and QA teams while ensuring performance, accessibility, security, and code quality. The role also requires cloud, containerization, Agile, database, and AI-assisted development experience.

Lead architecture and technical direction across multiple engineering teams. Drive large-scale refactoring, domain decomposition, cross-team technical decisions, AI-native engineering practices, production coding, testing, observability, reliability, technical planning, and engineering standards. Mentor senior technical staff, influence hiring and leveling, and partner with Product, Design, and engineering leadership to deliver foundational platform improvements.

Develop, test, maintain, and support enterprise applications and services using C#, .NET, ASP.NET Core, Web API, Entity Framework Core, and SQL Server. Responsibilities include API and database integration, code reviews, automated testing, debugging, performance improvements, release readiness, production troubleshooting, and collaboration with product, architecture, QA, and operations teams.

Lead a cross-functional advertising engineering squad of backend and iOS engineers. Partner with Product and Design to discover, prioritize, and deliver consumer-facing ad experiences through experimentation. Manage backlog health, technical debt, delivery quality, and team ceremonies while coaching engineers and fostering an empowered, high-trust team. Support scalable, maintainable mobile applications and navigate privacy, consent, and advertising requirements.

Develop and modify 2D games with Defold and Lua, including game mechanics, player controls, UI, animations, debugging, and performance optimization. Complete advanced game development tasks, document workflows step by step, and create clear technical instructions and prompts for AI training. The role also requires screen recording and clear English communication.

Design and operate production infrastructure for AI agent systems, including high-performance Rust services for model inference, orchestration, and execution. Build scalable distributed systems, MLOps capabilities, observability, guardrails, and failure recovery for millions of users. Optimize latency, throughput, reliability, and cost while partnering with agent systems and infrastructure teams to harden experimental prototypes for production.