Software Engineering SMTS / LMTS - AI Core (REMOTE)

Salesforce•San Francisco, CA

2d•$172,500 - $285,800•Remote

About The Position

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.

Requirements

Proven experience shipping production AI or backend systems in a high ownership environment, with a recent focus on LLM-powered applications.
Expertise building retrieval systems using RAG and hybrid search, including chunking and indexing strategy, embeddings, vector and lexical search, reranking, and measuring retrieval quality.
Expertise building developer tooling that lets developers build production agents, which can include frameworks for multi-agent orchestration, an agent and tool registry, and shared infrastructure for tracing and evaluation.
Experience owning platform initiatives from design through adoption, across systems such as retrieval and indexing, LLM provider integrations, and agent orchestration, including driving adoption and evolving the platform as usage grows.
Demonstrated technical leadership: you lead significant projects, design solutions for non-trivial problems by weighing tradeoffs, and influence architecture and execution beyond your immediate workstream.
Strong track record delivering complex projects with multiple stakeholders, dependencies, or phases.
Strong backend engineering fundamentals and proficiency in languages such as Ruby on Rails, Python, JavaScript, or Go, with SQL and RDBMS experience (PostgreSQL preferred).
Hands-on experience with the AI application lifecycle: experiment setup, dataset curation, offline evaluation, error analysis, deployment, and online evaluation.
Comfortable operating in ambiguity and driving clarity: you can frame problems, propose solutions, and align others.
Strong communicator across functions, able to explain technical ideas to both technical and non-technical audiences and to translate complex concepts into business impact.
Ownership mindset and bias for action. You raise the bar for the team through mentoring, code review, and your own work.
Curious and continuously learning, with an agent-forward approach to how you build software.

Nice To Haves

Experience with pgvector or other vector indexes, and with query performance at scale.
Experience with content ingestion from third-party systems (document stores, knowledge bases, CRM knowledge objects) and the lifecycle problems that follow: sync state, dedupe, source conflict.
Experience processing conversation or call transcripts, including handling PII and consent constraints.
Experience designing evaluation suites that score qualities such as thoroughness and faithfulness, not only correctness.
Experience with voice or real-time conversational agents.
Experience with enterprise SaaS (especially B2B marketing or sales tech) or Salesforce/CRM integrations.
Startup experience and rapid shipping.

Responsibilities

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 the systems your team owns. Participate in on-call, lead incident resolution for your area, and communicate clearly with the team and stakeholders during incidents.
Use agentic development to raise leverage: Use coding agents by default for implementation, testing, refactoring, and documentation while holding a high validation bar. Create reusable workflows, patterns, and tooling that raise the effectiveness of your team beyond your own output.
Partner across functions: Work with product, design, and engineering leadership to shape roadmap, tradeoffs, and requirements, and to translate strategy into concrete execution plans.
Raise team effectiveness: Mentor engineers, give high-quality code review and technical guidance, and improve standards, tooling, and processes.