Senior AI and Machine Learning Scientist
2 weeks ago
Every minute of downtime means lost revenue, lost trust, and engineers under fire.
AI is 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.
Think AIOps meets generative AI , but designed from day one for security, scale, and production readiness .
We are seeking a Senior AI Engineer or Research Scientist w ith practical experience in developing and deploying AI/ML systems at scale. AI's agentic intelligence, allowing our platform to conduct root cause analysis (RCA)and automated remediation in demanding enterprise settings.
This position bridges AI research and production engineering . You will experiment with LLMs, reinforcement learning, and synthetic data, and also implement these concepts into enterprise-grade, scalable, and dependable systems .
Enterprise-Ready AI Development: Design, implement, and deploy AI agents that operate reliably under enterprise constraints, such as multi-cloud environments, zero data egress, and compliance requirements.
LLM & Agent Research: Develop and test reasoning workflows, prompting strategies, and tool-use policies specifically tailored for large-scale observability data.
Training & Alignment: 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.
Synthetic Data Generation: Create scalable pipelines for producing synthetic incidents—logs, metrics, traces—used for training and benchmarking in scenarios with limited data.
Cross-Team Collaboration: Collaborate closely with infrastructure engineers, SREs, and product leaders to incorporate AI research into the BugRaid.AI platform.
Frontier Tracking: 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).
~ Experience designing secure AI systems (data privacy, zero-data egress, compliance frameworks like HIPAA/FedRAMP).
Experience with real-time ML serving (low-latency inference, batch + streaming pipelines).
Prior experience in enterprise SaaS or reliability-focused products.
AI
Work on high-impact AI challenges that sit at the intersection of AI, DevOps, and enterprise reliability .
Shape the core intelligence layer of a product designed to be enterprise-ready from day one .
Collaborate with a world-class founding team experienced in AI, DevOps, and large-scale systems.
Competitive compensation, equity (ESOPs) , and a chance to join at an early stage with an outsized impact.
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