Principal Software Engineer (PMTS), Full Stack

Qualified

1d•$172,500 - $285,800•Remote

About The Position

Qualified has been acquired by Salesforce. Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Agentforce is the future of AI, and you are the future of Salesforce. Engineering at Qualified is a high-ownership, high-velocity environment where engineers ship customer-facing product to real enterprises at scale. We’re building at the frontier of AI + product: developing agentic software and embedding AI directly into our platform, using modern toolchains and processes that help engineers move faster while staying focused on high-impact problem solving. Following our acquisition by Salesforce, we continue to operate with the pace and ownership of a startup—backed by the distribution, resources, and long-term runway of a global enterprise. Engineers have true end-to-end ownership from idea to production: scoping, implementation, testing, deployment, monitoring, and iterative improvement based on real customer feedback. You’ll work alongside a high-talent-density team that cares deeply about craft, performance, reliability, and user experience—and that raises the bar through collaboration, code review, and strong engineering principles. The result is accelerated growth and impact: you’ll make architecture decisions, solve meaningful system design challenges, and see your work land quickly with customers—without layers of approvals slowing you down. As a Principal Engineer on the Product Team, you’ll set technical direction for broad, business-critical areas of the product and drive execution across multiple teams and stakeholders. You’ll take the most ambiguous, highest-leverage problems—where the right approach is not obvious—and turn them into durable systems, scalable architectures, and repeatable delivery patterns. You’ll partner with product and design leadership to shape strategy and roadmap, and you’ll translate goals into clear technical plans, milestones, and success metrics. You’ll also help us translate emerging AI capabilities into practical, reliable product experiences—choosing the right techniques for the job, designing clean interfaces between AI components and the rest of the system, and ensuring the resulting features are observable, testable, and maintainable.

Requirements

Deep experience shipping and operating production full-stack web applications at scale
Demonstrated ability to set technical direction and drive alignment across multiple teams or workstreams
Strong track record delivering large, complex initiatives with meaningful dependencies and cross-functional stakeholders
Excellent systems thinking: you can reason about architecture, performance, reliability, and developer experience as a cohesive whole
Hands-on experience applying AI/ML in product (LLMs, embeddings, or similar), with strong judgment about evaluation, safety/reliability, and user trust
Comfortable operating in ambiguity and creating clarity: framing problems, defining approaches, and making decisions that stick
Strong communicator: you can write and speak clearly to different audiences and drive decisions through influence
Product-minded: you care about UX and customer outcomes, not just implementation
Ownership mindset and bias for action
Experience with modern web frameworks (Rails/React/Postgres/AWS is a plus; adaptability matters most)

Nice To Haves

Experience with enterprise SaaS (especially B2B marketing or sales tech)
Experience with real-time systems (chat, messaging, event-driven)
Familiarity with Salesforce/CRM integrations
Experience building agentic systems or autonomous tool-using workflows
Experience defining reliability practices (SLIs/SLOs), incident response improvements, or observability standards
Startup experience and rapid shipping

Responsibilities

Define and drive technical strategy for major product areas, ensuring long-term scalability, reliability, and developer velocity
Lead large, cross-team initiatives from concept through launch and iteration (including sequencing, dependency management, and stakeholder alignment)
Architect and evolve complex systems spanning frontend, backend services, and data models; make pragmatic tradeoffs that optimize for customer outcomes and operational excellence
Establish and evolve engineering standards (architecture patterns, testing strategy, performance budgets, reliability/observability practices) that scale across teams
Build AI-powered experiences using LLMs, embeddings, and related techniques where they create real user value, with strong engineering around evaluation, observability, and iteration
Proactively identify and eliminate systemic risks: reliability gaps, performance bottlenecks, scaling constraints, and operational toil
Mentor and develop engineers across levels; amplify impact by coaching technical leaders and raising the bar for design and execution
Communicate clearly and influence effectively across engineering, product, design, and leadership—especially during high-ambiguity decisions