Momentic AI Logo

Momentic AI

AI Engineer

Posted 9 Days Ago
In-Office
San Francisco, CA, USA
175K-250K Annually
Mid level
In-Office
San Francisco, CA, USA
175K-250K Annually
Mid level
Develop and improve AI models and agents powering an AI-native software testing platform. Responsibilities include supervised fine-tuning, creating training and evaluation datasets, building LLM evaluations and observability, tuning prompts and context, running experiments, and improving agent reliability, latency, token usage, and cost. The role also owns technical projects from definition through delivery and collaborates closely with engineering leadership.
The summary above was generated by AI

Momentic is building the definitive verification layer for software: an AI-native testing platform that helps engineering teams catch bugs before they ship. As AI accelerates software development, teams need a reliable way to verify that their products work. Engineering teams at Notion, Webflow, Xero, and Whatnot use Momentic to ship with confidence.

You’ll join our engineering team and work closely with our CTO to improve the models and agents behind Momentic. We’re looking for a hands-on AI engineer who can fine-tune models, build evaluations, and turn production data into more reliable, efficient agents. Momentic executes more than 1 million tests daily and processes more than 1 billion tokens per hour at peak, giving your improvements a direct impact on the product.

 
What you’ll do
  • Fine-tune models for long-running agents, including agents that investigate whether test failures come from infrastructure, recent code changes, or test authorship.

  • Build and curate training and evaluation datasets from production workflows, using failure patterns to identify opportunities for improvement.

  • Develop LLM evaluations and observability that connect offline results with production behavior and help the team verify improvements.

  • Tune prompts, context, and model behavior to improve task success, reliability, latency, token usage, and cost.

  • Design repeatable experiments and help publish benchmarks that demonstrate our core agents’ performance on testing and browser workflows.

  • Own technical projects from problem definition through delivery, working with the engineering team to make practical tradeoffs and ship improvements.

 
You’re perfect for this role if…
  • You have relevant software engineering experience, typically 3+ years, including hands-on contributions to AI-powered capabilities used in production.

  • You have hands-on experience with supervised fine-tuning, including preparing training data and evaluating model behavior.

  • You’ve worked with prompt and context tuning, evaluation datasets, and LLM observability to diagnose failures and measure improvements.

  • You have strong engineering fundamentals, code efficiently, and know what you’re great at and where you have room to grow.

  • You use AI agents to build software, breaking down work, tracking progress, checking results, and redirecting agents when they get stuck.

  • You’re comfortable with autonomy and ambiguity. You can turn an unclear problem into a plan, manage your own work, and follow through to delivery.

 

We’re interested in the depth of your contributions and what you’ve shipped. Your experience may come from owning an AI capability within a larger product.

 
Nice to haves
  • Experience with reinforcement learning fine-tuning, or LLM-as-judge evaluators.

  • Experience with long-running agents, recovery from failed steps, or coordinating multiple agents.

  • Experience publishing benchmarks or running rigorous experiments on AI systems.

 
About us
  • Raised a $15M Series A led by Standard Capital, with backing from Y Combinator, FCVC, Transpose Platform, and Dropbox Ventures.

  • Our customer base has grown 4× year to date.

  • A team of 17 and growing.

  • Based in person in San Francisco at 650 5th Street.

 
Benefits
  • Medical, dental, and vision insurance.

  • 401(k).

  • Unlimited PTO and sick days, plus company-observed US holidays.

  • Daily lunch and a fully stocked kitchen.

  • Commuter benefits.

  • Team offsites and events.

Similar Jobs

Yesterday
Hybrid
181K-317K Annually
Senior level
181K-317K Annually
Senior level
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Lead the DevOps practice for ServiceNow Otto, owning CI/CD, release automation, Kubernetes infrastructure, observability, reliability, and operational tooling across SaaS environments. Build infrastructure automation, GitOps workflows, and scalable cloud platforms while supporting AI and agentic services in production. Partner with scientists and engineers to improve deployment velocity, security, resilience, troubleshooting, and operational efficiency.
Top Skills: Agentic AiAnsibleArgocdBashCi/CdCursorDockerGenerative AiGithub ActionsGithub CopilotGitopsGoHelmInfrastructure As CodeJenkinsKubernetesKubernetes OperatorsLlmsModel InferencePythonRagTektonTerraformTool Calling
Yesterday
Hybrid
159K-277K Annually
Senior level
159K-277K Annually
Senior level
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Own the build, deployment, and reliability of ServiceNow Otto’s conversational AI systems across SaaS environments. Design Kubernetes infrastructure, IaC, CI/CD and GitOps workflows, observability, release automation, and incident response practices. Partner with scientists and engineers to operate scalable AI services securely and efficiently. Apply expertise in LLM architectures, agentic workflows, AI-assisted development, model integrations, and production troubleshooting while leading cross-team infrastructure initiatives and improving deployment velocity and reliability.
Top Skills: A2AAnsibleArgocdBashCi/CdCursorGenerative AiGithub ActionsGithub CopilotGitopsGoHelmJenkinsKubernetesKubernetes OperatorsLlmsMcpPythonRetrieval-Augmented Generation (Rag)TektonTerraform
Yesterday
In-Office
2 Locations
153K-204K Annually
Mid level
153K-204K Annually
Mid level
Cloud • Information Technology • Machine Learning
Build AI agents, copilots, and first-party applications that automate internal workflows across Finance, Supply Chain, HR, and business operations. Responsibilities include developing full-stack web and mobile applications with React, TypeScript, Node.js, and Go; integrating enterprise systems; building APIs, microservices, and event-driven architectures; deploying cloud applications; and applying generative AI frameworks and tools. The role partners with technical and business stakeholders to create secure, maintainable, user-centered productivity solutions.
Top Skills: AWSAzureBashBoomiC#C++Chatgpt CodexCi/CdClaudeCohereContainer OrchestrationCursorData WarehousesDcimDevOpsErpEvent-Driven ArchitectureFastapiGCPGeminiGithub CopilotGleanGoGrpcHrisJavaKafkaKestrelLangchainLanggraphMicroservicesMrpNext.JsNode.jsOpenaiPdmPythonRayReactRest ApisSparkTypescriptWorkato

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account