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AI Innovation Lead
2 weeks ago
We're building the world's first enterprise-ready incident copilot — intelligent, agentic systems that can detect, diagnose, and resolve complex production incidents across massive streams of logs, metrics, traces, and alerts.
As a Senior AI Engineer or Research Scientist, you will drive our agentic intelligence, allowing our platform to conduct root cause analysis (RCA) and automated remediation in demanding enterprise settings.
Key Responsibilities- Design, implement, and deploy AI agents that operate reliably under enterprise constraints, such as multi-cloud environments, zero data egress, and compliance requirements.
- Develop and test reasoning workflows, prompting strategies, and tool-use policies specifically tailored for large-scale observability data.
- Utilize techniques such as fine-tuning, RLHF, and reward modeling to ensure AI behavior aligns with real SRE workflows, thereby avoiding reliance on toy datasets.
- Create scalable pipelines for producing synthetic incidents—logs, metrics, traces—used for training and benchmarking in scenarios with limited data.
- Collaborate closely with infrastructure engineers, SREs, and product leaders to incorporate AI research into our platform.
- Design systems with built-in observability, monitoring, and CI/CD processes, ensuring failover, rollback, and resilience capabilities.
- Keep up with the latest research in LLMs, agentic AI, and reliability, and incorporate these innovations into enterprise production environments.
- 5+ years of production experience building and deploying ML/AI systems at scale.
- PhD or Master's in Computer Science, Machine Learning, or related fields (or equivalent experience).
- Strong foundation in deep learning, reinforcement learning, or robotics.
- Hands-on experience with LLM fine-tuning, RLHF, or synthetic data generation.
- Proven track record of shipping AI systems into production (not just research prototypes).
- Experience with cloud-native architectures (AWS/Azure/GCP), Kubernetes, and MLOps tooling (SageMaker, MLflow, Ray, Kubeflow).
- Comfort working with large-scale data streams (Kinesis, Kafka, Flink) and vector databases (OpenSearch, Pinecone, Weaviate).
- Strong collaboration skills and ability to communicate across research, engineering, and product teams.
- Background in observability (logs, metrics, traces) or large-scale production debugging.
- Experience designing secure AI systems (data privacy, zero-data egress, compliance frameworks like HIPAA/FedRAMP).
- Contributions to open-source agent frameworks (LangChain, LlamaIndex, Haystack, Ray Serve).
- Experience with real-time ML serving (low-latency inference, batch + streaming pipelines).
- Prior experience in enterprise SaaS or reliability-focused products.
