Career
Senior ML Engineer (Cloud)
At Bold, you'll build the AI/ML and agentic capabilities of our behavioral detection pipeline - the part of our product that ingests signals from endpoint agents, uncovers insights, automatically generates incidents, identifies anomalous behavior, and enriches the intelligence we deliver to customers. You'll shape how AI/ML shows up across the pipeline, now and into the future.
Responsibilities
Build and optimize inference systems applying LLMs and ML to insight generation, automatic incidents, anomaly detection, and enhancing classification from endpoint agents.
Design AI agents that reason over data autonomously and return structured, reliable output.
Build datasets and evaluations that measure and improve our pipelines.
Take new AI/ML capabilities from research spike to production feature, choosing the right approach for each problem - from deterministic algorithms to LLMs - and combining them where it makes sense.
Own model execution end to end: data pipelines, context engineering, retrieval, guardrails, telemetry, and failure handling.
Run experiments, benchmark models and context/prompt strategies, and turn results into pipeline improvements.
Qualifications
5+ years of software engineering experience, shipping large-scale production systems.
Experience in Go, Python, Java or similar.
Experience building production ML or LLM systems under scale, cost and latency constraints.
Strong grasp of LLMs in production: context engineering, prompting, structured output, AI agents, retrieval, and managing nondeterminism.
Strong data science fundamentals: building and curating datasets, designing evaluations, and measuring quality rigorously.
Strong algorithmic foundation and the judgment to combine classical algorithms, traditional ML, and LLMs appropriately.
An R&D mindset, strong ownership, and cross-disciplinary collaboration.
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When sensitive data moves, Bold sees it.
When the risk is real, Bold acts.
When intent is unclear, Bold coaches.
All before data leaves the device.
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5+ years of software engineering experience, shipping large-scale production systems.
Experience in Go, Python, Java or similar.
Experience building production ML or LLM systems under scale, cost and latency constraints.
Strong grasp of LLMs in production: context engineering, prompting, structured output, AI agents, retrieval, and managing nondeterminism.
Strong data science fundamentals: building and curating datasets, designing evaluations, and measuring quality rigorously.
Strong algorithmic foundation and the judgment to combine classical algorithms, traditional ML, and LLMs appropriately.
An R&D mindset, strong ownership, and cross-disciplinary collaboration.
