Software Engineer, AI Developer Productivity (MTS)

Software Engineer, AI Developer Productivity (MTS)
Buenos Aires | Hybrid

JOB DESCRIPTION

The AI Solutions team in Technology & Product builds internal products and platforms that help Salesforce engineers adopt AI-assisted development safely, effectively, and measurably. Our work spans developer experiences, AI usage and outcome intelligence, governance, telemetry, and integrations with coding tools and engineering systems.

As a Member of Technical Staff, you will build and operate full-stack product and platform capabilities across desktop applications, backend services, data pipelines, dashboards, and developer-facing integrations. Working with experienced engineers and product partners, you will turn product needs into reliable software, own well-defined areas end to end, and help thousands of Salesforce engineers get more value from AI every day.

What You'll Actually Be Doing

• Design, implement, test, and ship well-scoped full-stack features across internal developer-productivity products, including desktop experiences, backend services, data and analytics pipelines, dashboards, and integrations with AI coding tools.
• Build backend APIs, data models, ingestion flows, dashboards, and user experiences that connect AI tool usage to outcomes engineers and leaders can act on.
• Contribute to technical designs and turn product requirements into clear, incremental implementation plans with support from senior engineers when needed.
• Own features and services in production by improving observability, reliability, performance, security, and supportability as part of the software development lifecycle.
• Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow to deliver secure, optimized, and maintainable code.
• Build AI-enabled and agentic workflows that fit naturally into how developers work, with appropriate evaluation, safeguards, and human oversight.
• Contribute to shared system context - designs, constraints, standards, and operational knowledge - that helps both people and AI agents work accurately and reliably.
• Review human- and AI-generated code for correctness, quality, security, and performance; participate actively in design and code reviews; and share what you learn with the team.

You're Our Person If...

• 3+ years of professional experience building production software, including full-stack applications, backend services, developer tools, or internal platforms.
• Strong programming fundamentals and proficiency in at least one modern language, with the ability to work across frontend, backend, APIs, data stores, and service boundaries.
• Experience delivering well-defined features through design, implementation, testing, rollout, and production ownership.
• A practical understanding of data structures, API design, automated testing, source control, CI/CD, and secure software development practices.
• Ability to debug technical issues, use logs and metrics to understand system behavior, and make thoughtful tradeoffs with guidance when appropriate.
• Clear communication and collaboration skills, including the ability to ask good questions, give and receive feedback, and work effectively with engineers and product partners.
• A demonstrated AI-first approach to engineering - using tools such as Claude Code, GitHub Copilot, Codex, or Cursor to move faster while validating outputs and maintaining quality.
• The ability to write precise, structured prompts and contribute the context, examples, and constraints that make AI-assisted development reliable and production-ready.
• A related technical degree is required.

Even Better If...

• Experience building developer tools, AI coding tool integrations, internal platforms, telemetry systems, or enterprise desktop applications.
• Familiarity with React, TypeScript, Go, service APIs, event pipelines, analytics systems, or cloud- and Kubernetes-based services.
• Experience building self-service experiences, dashboards, plugin systems, command-line tools, or reusable integration patterns for engineering teams.
• Hands-on experience with LLM applications, agentic workflows, prompt evaluation, or safety and quality controls for AI-enabled features.
• Interest in AI developer productivity and helping engineering teams adopt AI tools in ways that are measurable, trustworthy, and scalable.