LangChain
Senior Backend Software Engineer, AI Observability & Evals Platform (LangSmith)
Boston, MA · Senior
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odds of building a lasting career here
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.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
$16,160 more — $186,160 — moves this role to Level IV and 4 lottery entries. That figure is inside the range the employer already advertised.
Based on the DOL prevailing wage for this occupation and worksite, a base salary of $186,160 would place this position at wage Level IV. That figure is within the posted range, and I'd like to target it. This role classifies under "Software Developers" for prevailing-wage purposes.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · Boston-Cambridge-Newton, MA-NH. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
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from this employer's Department of Labor filings
Files H-1B transfers
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
- This role is based onsite in Boston, MA.
- We are looking for a Senior Backend Engineer to join our growing engineering team in Boston.
- You will work on the core services that allow our customer's developer teams to monitor and evaluate their AI applications at scale.
Responsibilities
- Design, develop, and maintain backend services and APIs to support LangSmith's tracing, monitoring, and evaluation workflows.
- Collaborate on architectural decisions to ensure systems are performant and maintainable.
- Optimize storage and query performance for high-volume observability and evaluation data.
- Create and maintain technical documentation, including system design and API references.
- Demonstrated ability to optimize backend services for performance and reliability.
- Strong communication skills, with the ability to collaborate cross-functionally.
Requirements
- Proficiency in one or more backend languages/frameworks, ideally Python and/or Go
- Strong understanding of API design and building reliable data services.
- Experience with high-throughput, mission-critical systems.
Compensation
- 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.
- $170,000- $235,000
- Compensation Philosophy:
Benefits
- Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
- Experience with database systems (Postgres, Redis, Clickhouse), and cloud platforms (AWS, GCP, Azure) would be a bonus.
- 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 LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems.
- From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end.
- If you want to define what great AI observability looks like at production scale, this is where that work gets done.
This listing is sourced directly from LangChain's careers page and normalized into a canonical job model.