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Teserac is building neuron™, a unified AI-native platform for data center observability, intelligence, and workflow automation. The platform processes real-time telemetry from thousands of sensors, meters, and control systems across heterogeneous environments, enabling infrastructure owners to monitor, analyze, automate, and proactively manage power operations with full situational awareness. An embedded AI teammate serves as an always-on co-pilot for operators, detecting anomalies, correlating events, and surfacing recommendations 24/7.
You will work across the full AI development lifecycle — from data pipelines and model integration to agentic orchestration, evaluation, and production support — collaborating closely with a small, fast-moving engineering team. This is not a research-only role, but research thinking matters: you will be expected to read papers, stay ahead of the field, and bring ideas to the table, then build them into production systems.
Key areas of focus include:
- Multi-agent orchestration and LLM-driven triage workflows
- Time-series modeling for anomaly detection, failure prediction, and health forecasting on multivariate telemetry
- Retrieval-augmented knowledge systems for operations teams
- Data and ML pipelines — ingestion, ETL, and dataset construction
- Fine-tuning and post-training of language models for operational use cases
- AI observability, evaluation frameworks, and production performance benchmarking
Responsibilities:
- Design, develop, and maintain AI-powered applications and automation workflows
- Integrate and optimize LLM APIs for production use cases
- Build and refine retrieval and knowledge-augmentation pipelines
- Develop evaluation frameworks to benchmark AI system performance
- Implement monitoring, tracing, and debugging capabilities for AI systems
- Read and synthesize relevant research; bring ideas forward and debate them with the team
- Contribute to AI architecture decisions and production hardening
- Stay current with the rapidly evolving AI/ML landscape
REQUIREMENTS:
Required:
- Degree in Computer Science, Machine Learning, Mathematics, or a related field — or equivalent demonstrated experience
- Strong proficiency in Python
- Solid software engineering fundamentals: testing, version control, CI/CD
- Experience working with LLM APIs in applied contexts
- Familiarity with agentic system concepts — tool/function-calling, agent frameworks
- Daily use of AI-assisted coding tools (Cursor, Copilot, Claude Code, etc.)
- Ability to read ML research papers and translate ideas into practical experiments
- Strong analytical thinking and clear communication — you can argue a position and update it when wrong
Preferred:
- Professional AI/ML engineering experience (any level)
- Experience building agentic systems using frameworks such as LangGraph or LangChain; MCP a plus
- Time-series modeling — forecasting and anomaly/failure prediction on multivariate data
- Experience fine-tuning or post-training language models
- PyTorch and/or model serving frameworks (e.g., vLLM)
- Experience building data and ML pipelines — ingestion, ETL, dataset construction
- Familiarity with cloud ML platforms, particularly GCP (Vertex AI)
- LLM evaluation and benchmarking: harness design and eval loop development
- Domain experience with data center or industrial telemetry, BMS/OT protocols (Niagara, BACnet/Modbus)
- Background in DevOps, distributed systems, or observability tooling
