Why This Job is Featured on The SaaS Jobs
This Founding AI Engineer role sits at a timely intersection in SaaS: applying ML and LLM techniques directly inside an observability platform, where product value is measured in reduced downtime and faster incident resolution. Because the work targets automated remediation across logs, metrics, and traces, it reflects a broader shift in DevOps SaaS toward systems that do more than surface issues and instead take safe, production-grade action.
For a SaaS career, the standout element is end-to-end ownership of the AI lifecycle in a product environment. Building pipelines from data collection through deployment, then integrating models into an existing platform, develops the practical skills that translate across modern SaaS teams: shipping ML features, operating them in production, and tying model behavior to user-facing workflows. Exposure to large-scale telemetry data and reliability constraints also maps well to other infrastructure and platform SaaS domains.
This role is best suited to an engineer who prefers broad scope over narrow specialization and is comfortable making foundational technical decisions early. It will appeal to someone who enjoys cross-functional work with product and platform teams and who wants their AI work judged by operational outcomes rather than offline metrics alone.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
We're looking for a talented and experienced AI/ML founding engineer to join the Middleware team. You'll play a key role in building AI on our observability platform to detect and fix issues automatically. In this role, you’ll be able to design, develop, and deploy intelligent solutions that take action where our platform identifies problems.
We are looking for someone to help us invent the future of DevOps for the AI age.
Key Responsibilities
- Build AI-Powered Remediation Systems: Design and implement machine learning models that can identify, diagnose, and automatically resolve system issues detected by our observability platform.
- Own the AI/ML Pipeline: Take end-to-end ownership of the AI lifecycle — from data collection and preprocessing to model training, evaluation, and deployment.
- Integrate with Observability Stack: Work closely with the core platform team to integrate AI solutions into the existing observability infrastructure (e.g., logs, metrics, traces).
- Experiment and Iterate: Rapidly prototype and experiment with different models and approaches (e.g., anomaly detection, root cause analysis, LLM-based insights) to find what works best.
- Collaborate Cross-Functionally: Partner with product, backend, and DevOps teams to align AI capabilities with user needs and infrastructure realities.
- Set the Technical Direction: As an early technical hire, contribute to foundational architecture decisions and establish best practices for AI/ML within the company.
- Ensure Reliability and Scalability: Build systems that perform reliably at scale and integrate safely into production environments.
- Stay Ahead of the Curve: Keep up with the latest advancements in AI/ML and observability to help shape our product roadmap.
Qualifications
- Engineers with experience building AI products (do side-projects count?)
- Solid software engineering skills: Proficiency in Python and TypeScript.
- Systems knowledge: Understanding of observability tools (e.g., Prometheus, OpenTelemetry).
- Owner mindset: Comfortable working in a fast-paced, ambiguous environment with limited structure and high ownership.
Few More Facts
- We're backed by some of the best investors in the world, like Y Combinator, 8VC, Fin Capital, Tokyo Black (Looker founder fund), Guillermo Rauch (founder of Vercel) and many more.
- You will grow fast and work at scale - we collect and process 1 petabyte of data monthly. So you will be working with a large dataset to process and store.
- You like being rewarded directly for your high output
- Founder and CEO Laduram built the company before, took it to 200 people, and raised $57m in venture funding.
If you are excited about the prospect of building an AI-based cutting-edge observability platform and working with a team of talented and passionate engineers, we encourage you to apply for this position.
Please include your GitHub.
