LangChain

LangChain

Solutions Engineer (Chicago)

Chicago, IL · Senior

Sponsorship not specified$200k-$300kDetected 59 days ago
AWSGCPAzureCloud PlatformsKubernetesLLMsAgentic AILangGraphSalesHRISResearchLeadershipCommunication

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odds of building a lasting career here

61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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Employer immigration record

from this employer's Department of Labor filings

Files H-1B transfers

9 transfer filings in the last year, covering 9 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • You will work on some of the hardest problems in applied AI, in front of customers, on a clock.
  • Not demos, not research: systems real teams depend on in production.
  • The feedback loop is fast, the impact is measurable in closed deals and live deployments, and the work directly shapes how AI agents get built in the real world.

Responsibilities

  • Own the technical win. Partner with AEs to scope evaluations, run technical discovery, and design POCs that map to the customer's real use case rather than a canned demo
  • Co-architect and co-build production AI agents with customer engineering teams, from prototype through rollout
  • Surface field feedback to product and build reusable POC assets, cookbooks, and example code that scale across accounts
  • Ability to explain technical tradeoffs clearly and build trust with developer audiences, then translate that into a decision the customer is ready to make
  • Own the technical win.

Requirements

  • Experience with LLM evaluation, observability, or guardrails
  • Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts

Compensation

  • Final compensation will depend on experience, skills, and location.
  • We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks.
  • We are hiring for this role across a range of scope, and the compensation band reflects that.
  • Annual OTE range:
  • $200,000-$300,000 USD.

Benefits

  • Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
  • Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.

Company info

  • 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.
  • 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.
  • The Deployed Engineering team is the technical front line of our go-to-market motion.
  • We partner with account executives from the first technical conversation through production rollout, helping companies evaluate LangChain, prove it out on their hardest use case, and get agents running reliably at scale.
  • This is a hands-on, highly technical team.
  • Solutions Engineers own the technical win:

This listing is sourced directly from LangChain's careers page and normalized into a canonical job model.