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:
The
LangSmith Engine
team is building a proactive agent engineer that analyzes production traces, identifies important failures, recommends and writes fixes, and helps prevent those issues from coming back. We’re building agents that can understand complex software systems and continuously improve the quality of other AI agents.
About the Role:
We’re looking for an experienced research engineer to help make the Engine agent more capable and more efficient.
You’ll study real agent failures, build benchmarks that capture what matters, run experiments to improve performance, and turn successful ideas into production. This may include
prompting and agent-harness improvements, model selection, fine-tuning and post-training custom models.
The focus is on measurable improvements to the overall agent.
This role also requires a understanding of
production engineering and system-level tradeoffs
. Engine is a production system, so improving an agent is not just about maximizing benchmark performance—it also means understanding the impact on
cost, latency, reliability, and scalability
. You’ll work in the same team with production engineers to design, test, and ship improvements that work reliably in real-world environments.
Location:
SF and NYC
What You’ll Do:
Build and maintain
benchmarks and evaluations
that measure the quality and efficiency of Engine agents on real-world tasks.
Design and run experiments to improve agent performance across
models, prompting, context, tools, orchestration, and agent strategies
.
Explore and implement
post-training and fine-tuning techniques
when they can meaningfully improve agent capabilities, quality, or cost.
Turn successful experiments into
production improvements
, working closely with engineers and researchers to measure impact and prevent regressions.
Help define the
ML roadmap and technical direction
for improving Engine agents, and mentor other engineers through strong technical leadership.
What You’ll Bring:
4+ years of experience
in ML/AI research, or a closely related field.
Master’s or Ph.D. in a relevant scientific field.
Hands-on experience working with
LLMs and AI agents
, including analyzing model behavior and improving real-world performance
Strong experience designing
benchmarks, evaluations, and experiments
for AI/ML systems; you know how to tell whether a change actually made an agent better.
Strong software engineering skills, with a track record of taking ideas from
research prototype to measurable production impact
.
You have
maximum agency and strong research judgment
: you can identify high-impact problems, work through ambiguity, move quickly, and communicate your findings clearly.
Nice to Have:
Ph.D. in Machine Learning, Computer Science or Physics.
Hands on experience with
LLM-as-a-judge, automated graders, synthetic data generation, or human evaluation
.
Hands on experience with
reinforcement learning, preference optimization, SFT, RLHF/RLAIF, or other post-training techniques
for LLMs.
Experience optimizing LLM agents for
cost, latency, or task efficiency
on productions
Experience with
model serving, inference optimization, distributed systems, or GPU infrastructure
.
Compensation
Salary Range: $240,000-$280,000 USD
Compensation Philosophy:
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. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. 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.