Software Engineering SMTS​/LMTS - AI Core; REMOTE

Engineering at Qualified is a high-ownership, high-velocity environment where engineers ship customer-facing product to real enterprises 're building at the frontier of AI + product: developing agentic software and embedding AI directly into our platform, using modern tool chains 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. 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 an Engineer on the AI Core team, you'll operate as a technical leader who turns ambiguous, high-stakes AI problems into durable, high-quality systems, while raising the technical bar for the broader team. AI Core builds the retrieval systems and agent infrastructure that power how our AI agents find, trust, and use knowledge, and the developer tooling that lets engineers across the company ship production agents quickly and safely.

You'll lead significant initiatives end to end: faithful answering over a customer's own knowledge, retrieval that combines RAG and hybrid search, content lifecycle controls such as freshness, deduplication and source precedence, transcript processing, and the frameworks, registries, and evaluation tooling that make building a production agent a paved path. You'll partner closely with product, design, and engineering leadership to decide what we build, how we build it, and how we measure whether answers actually got better.

Lead retrieval initiatives end to end:
Design, build, and operate retrieval systems using RAG and hybrid search (lexical plus vector, reranking, filtering) so agents answer from the right content with the right amount of context. Own delivery from scope and design through launch, monitoring, and iteration.

Build developer tooling for production agents:
Drive technical design for the frameworks and platform pieces our engineers use to build agents, including multi-agent orchestration, shared primitives for prompts, tools, tracing, and evaluation.

Own answer quality:
Diagnose why answers are thin, wrong, or inconsistent by separating retrieval problems from generation problems (prompt, token budget, model choice). Set a quality bar we can test against, and lead the work to reach it.

Design content and data models:
Define how content from documents, knowledge bases, and call transcripts is ingested, segmented, labeled, and retrieved, including the primitives behind freshness, dedupe, source precedence, and internal versus external visibility.

Make AI changes safe to ship:
Build evaluation, observability, and rollout practices (offline evals, online monitoring, regression detection) so we can change prompts, models, and retrieval with confidence. Evaluate multiple solution paths and know when to trust, redirect, or override model and agent output.

Set performance and scaling benchmarks across the platform we own:

Define and track targets for retrieval and indexing, LLM provider usage, and agent orchestration.

Own operational health:
Ensure the reliability and performance of…