Sales Engineer (Bay Area)
$315k+Application analysis
Sales Engineer (Bay Area) at LangChain is a forward deployed llm tooling role for engineers who want customer-facing technical work with real deployment ownership.
Reading this role
This Solutions / Sales Engineering role centers on technical discovery, demos, POCs, and helping buyers build internal consensus. Key signals from the listing: Kubernetes / containers, AWS, Azure, GCP.
About LangChain
LangChain builds frameworks and products for production LLM applications with a deeply technical user base. The role is anchored in San Francisco, CA, so expect on-site customer work in that region.
Signals in the listing
Stand out for Sales Engineer (Bay Area)
- Lead your application with one paragraph tying Sales Engineer (Bay Area) to a specific customer or deployment you owned — LangChain hires for shipped outcomes, not tool lists.
- Bring a tight demo narrative: customer pain, live workflow, and measurable before/after — treat the interview like a compressed POC.
- Show how you qualify opportunities and say no to bad-fit pilots; LangChain values technical judgment in customer-facing roles.
- For Kubernetes-heavy roles, describe a cluster you operated: upgrades, networking, secrets, observability, and how you debugged a production outage.
- The posting mentions multiple languages — pick the one most relevant to this team and show production code, not tutorial exercises.
- This role is tied to San Francisco, CA — mention willingness to work on-site with customers and any existing ties to that market (clients, partners, or domain knowledge).
Stand out at LangChain
- Demonstrate LLM app architecture with a concrete example — LangChain will probe for specifics, not buzzwords.
- Demonstrate open-source community fluency with a concrete example — LangChain will probe for specifics, not buzzwords.
- Contributions to LangChain/LangGraph or a public agent project is high-signal here.
- Read recent engineering blog posts, product launches, and customer stories on langchain.com — reference one explicitly in your outreach to show genuine interest.
Tailored from this listing’s title, company, and description. Always verify requirements on the company careers page before applying.
Role description
About Us
At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.
About The Team
This team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Sales Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform.
About The Role
You'll work on some of the hardest problems in applied AI alongside customers. This is not demos or research, but helping teams build systems they rely on in production. The feedback loop is fast, the impact is visible, and your work directly shapes how AI agents are built in the real world.
What You’ll Do
- Co-architect and co-build production AI agents with customer engineering teams
- Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
- Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
- Advise customers post-sale on architecture, best practices, and roadmap-level decisions
- Run technical demos, trainings, and workshops for developer audiences
- Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
- Occasionally contribute code upstream when it meaningfully improves customer outcomes
- This role requires 40% travel to customer sites to support deployment, onboarding, and ongoing technical engagement
What You’ll Bring
- 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
- Strong Python, JavaScript and systems fundamentals
- Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
- Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
- Can explain technical tradeoffs clearly and build trust with developer audiences
- Take responsibility for outcomes, not just recommendations
- Have a bias toward action and enjoy figuring things out as you go
- Are excited about operating AI agents in production, not just building demos
Nice To Have’s:
- You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks
- Worked with LLM evaluation, observability, or guardrails
- Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
- Have shipped and operated production software and are comfortable owning systems under real-world constraints
Compensation
- Annual OTE range: $185,000–$315,000 USD
Compensation Philosophy:
We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.
Benefits
Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.