Description
Salesforce AI Labs is seeking a senior individual contributor to join their team. The successful candidate will combine software engineering, applied AI, solution architecture, and storytelling to build and demonstrate how capabilities across the Salesforce AI Labs portfolio can work together to deliver measurable customer outcomes faster.
Responsibilities:
- Build connected solutions: Create working prototypes, API integrations, and agent workflows spanning multiple products and enterprise systems.
- Start with the business problem: Work with customers, portfolio leaders, and go-to-market teams to identify use cases where combining capabilities produces meaningful additional value.
- Create compelling demonstrations: Develop reliable, repeatable experiences that make complex technology understandable and show a clear connection between customer needs, system actions, and business results.
- Tell the story at every level: Present to executives, customers, developers, and sales teams, adapting the depth while preserving technical credibility.
- Explore the next opportunity: Use rapid experimentation to test new agentic experiences and architectures.
- Make the work reusable: Package reference architectures, integration components, demo environments, and playbooks that other teams can adopt.
- Measure value and speed: Establish baselines and evaluate outcomes such as implementation time, process cycle time, cost to serve, service resolution, and customer experience.
- Connect learning to product decisions: Surface integration gaps, customer demand, and technical constraints to product and engineering leaders, with evidence from what you build.
Requirements:
- A track record of personally building customer-facing software, integrations, or technical prototypes that solve real business problems.
- Strong coding skills in Python, TypeScript/JavaScript, or comparable languages.
- Fluency in modern API architectures, including authentication, webhooks, event-driven integration, data contracts, and reliable execution across systems.
- Practical experience building with models and tools from providers such as OpenAI and Anthropic, alongside relevant open-source agent frameworks and harnesses.
- Understanding of tool calling, retrieval, orchestration, context management, evaluations, observability, and human approval within agent workflows.
- Sound judgment about model limitations, reliability, latency, cost, data access, and the requirements for moving from prototype to customer deployment.
- Strong storytelling and presentation skills, including the ability to deliver a live demonstration and explain its business significance.
Nice to Have:
- Forward-deployed engineering, applied AI engineering, solutions engineering, or customer-facing solution architecture experience.
- Building demonstrations or proofs of value involving multiple enterprise products.
- Process intelligence, workflow automation, customer research, customer service, or digital experience platforms.
- Creating reusable technical assets that support several customers or go-to-market teams.
- Startup or founding-engineer experience with direct customer engagement.
What Success Could Look Like in the First 90 Days:
- First 30 days: Understand the portfolio, map integration opportunities, and select two high-value customer scenarios with business sponsors and measurable outcomes.
- By 60 days: Deliver one working demonstration across multiple products, with a clear story, documented dependencies, and an initial evaluation of reliability and value.
- By 90 days: Validate the scenario with customer or field teams; package it for reuse; and propose the next build priorities based on evidence.
Benefits:
- Time off programs
- Medical, dental, vision
- Mental health support
- Paid parental leave
- Life and disability insurance
- 401(k)
- Employee stock purchasing program