Rockstar is recruiting for a founding AI/ML engineer to join a dynamic team building the future of DevOps for the AI age. Our client is a well-funded, fast-growing startup backed by top-tier investors like Y Combinator and 8VC. Their mission is to help organizations gain insights and visibility into their complex systems and applications through a cutting-edge, AI-based observability platform that processes data at a massive scale. About the role They are looking for a talented and experienced AI/ML founding engineer to join the Middleware team. The candidate will play a key role in building AI on their observability platform to detect and fix issues automatically. In this role, the candidate will be able to design, develop, and deploy intelligent solutions that take action where their platform identifies problems. They are looking for someone to help them 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 the 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 the 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: - They are 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. - The candidate will grow fast and work at scale - they collect and process 1 petabyte of data monthly. So the candidate will be working with a large dataset to process and store. - The candidate likes being rewarded directly for high output. - The Founder and CEO built the company before, took it to 200 people, and raised $57m in venture funding. If the candidate is excited about the prospect of building an AI-based cutting-edge observability platform and working with a team of talented and passionate engineers, they are encouraged to apply for this position. Please include your GitHub.

Rockstar is recruiting for a founding AI/ML engineer to join a dynamic team building the future of DevOps for the AI age. Our client is a well-funded, fast-growing startup backed by top-tier investors like Y Combinator and 8VC. Their mission is to help organizations gain insights and visibility into their complex systems and applications through a cutting-edge, AI-based observability platform that processes data at a massive scale. About the role They are looking for a talented and experienced AI/ML founding engineer to join the Middleware team. The candidate will play a key role in building AI on their observability platform to detect and fix issues automatically. In this role, the candidate will be able to design, develop, and deploy intelligent solutions that take action where their platform identifies problems. They are looking for someone to help them 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 the 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 the 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: - They are 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. - The candidate will grow fast and work at scale - they collect and process 1 petabyte of data monthly. So the candidate will be working with a large dataset to process and store. - The candidate likes being rewarded directly for high output. - The Founder and CEO built the company before, took it to 200 people, and raised $57m in venture funding. If the candidate is excited about the prospect of building an AI-based cutting-edge observability platform and working with a team of talented and passionate engineers, they are encouraged to apply for this position. Please include your GitHub.

• Design end-to-end solutions that align with enterprise environments and business needs. • Establish and promote best practices, reusable design patterns, and architectural standards. • Create clear and concise documentation, including backend integration specifications, business logic implementation details, and high-level architecture designs. • Identify the APIs needed (Analysis and onboarding, API request/response structures, and DTO classes). • Also designing high level design for the middleware teams to enable them to produce MW services. • Act as a liaison between business stakeholders and development teams to translate business requirements into technical solutions. • Work closely with engineers to support the implementation of designed solutions. • Engage proactively with external teams to ensure alignment and successful project execution. • Tune voice solutions for optimal performance, leveraging best practices in configuration and AI/ML-powered speech recognition systems.

• Design, build, and deploy scalable machine learning and AI systems in production environments. • Collaborate with Product Managers, Data Scientists and Engineers to lead the implementation of models and integrate them into data pipelines and agentic applications. • Lead model performance monitoring, retraining workflows, and continuous improvement. • Lead the implementation of data preprocessing and feature engineering pipelines for ML use cases. • Lead to experimentation and testing of AI models to improve accuracy and performance. • Develop, maintain, and optimize scalable data pipelines for analytics and machine learning workloads. • Ensure data reliability, quality, and performance across data systems. • Implement and maintain data ingestion pipelines from various internal and external sources. • Build and improve internal tools that support data operations and data quality. • Monitor and troubleshoot data pipelines to ensure consistent and timely delivery. • Contribute to improving platform efficiency and scalability. • Participate in code reviews and follow best practices in data and ML engineering. • Document data pipelines, ML workflows, and system architecture. • Contribute to evolving data and AI engineering best practices. • Stay current with emerging tools, frameworks, and trends in AI, ML, and data engineering.