Antimetal is the autonomous system for production. Continuously understanding, operating, and improving your environment. Backed by AI Grant.
About the role
Experiment, Evaluate, Iterate, Ship: Run experiments across our research areas, analyze results, validate what works, and take successful approaches to production. Build Evaluation Infrastructure: Partner with platform on live and offline evaluation pipelines, benchmarks, and synthetic data generation. Build the tooling that lets the team measure progress and iterate with confidence.
What they're looking for
- 4+ years of experience in applied ML, research engineering, preferably at a company shipping production AI systems
- Production experience contributing to agentic/LLM systems, including multi-step reasoning, reinforcement learning, fine-tuning, and orchestration
- Proven experience bringing work from prototype to production, using data and experimentation to drive product and architectural decisions
- Strong on ML fundamentals: statistical modeling, probabilistic methods, time-series analysis, evaluation methodology
- Real world expertise in one area of applied ML: search, statistical modeling, NLP, etc
- Experience constructing and running end-to-end evaluation pipelines with real world data
More about this role
We’re looking for a Research Engineer to build the intelligent systems that power Antimetal. You’ll prototype new approaches, run experiments, and own the path from research to production. You’ll work closely with platform and product to shape agent capabilities and contribute to evaluation methodology.
Infrastructure, and its corresponding observability, is a hard domain to model . Telemetry is high-volume, noisy, and ephemeral. Ground truth is approximate. We’re building AI agents that understand this complexity and can reason about what’s happening, why, and how to fix it, including making changes to code and configuration.
Infrastructure Intelligence: Models that help us understand what’s happening in infrastructure and why. Detecting anomalies, forecasting issues, analyzing telemetry across logs, metrics, events, and traces, understanding causality, and connecting runtime behavior back to code. These capabilities form the foundation our agents use to reason about infrastructure.
Autonomous Agents : Long-running, parallel agents that detect, diagnose, and remediate infrastructure issues, including fixing code and configuration. Advancing multi-step reasoning, orchestration,...
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