Machine Learning Engineer — Applied AI & Agents
About the role
Machine learning engineer role focused on building learning systems for security agents, including LLM fine-tuning pipelines, RL environments, agent evaluations, production model systems, and the interface between model and harness.
Responsibilities
- LLM fine-tuning pipelines. Curate and version datasets, turn useful trajectories and failure cases into training examples, run post-training experiments, and evaluate generalization on held-out tasks.
- RL environments for security work. Build reproducible tasks with realistic tool interfaces, observations, state transitions, and verifiable outcomes. Design rewards that encourage successful investigations and catch shortcuts, reward hacking, and brittle behavior.
- Agent evaluations. Measure multi-step task completion, tool use, evidence quality, and failure recovery. Use baselines and ablations to distinguish model improvements from changes to the harness or context layer.
- Production model systems. Take models from experiment to serving: versioning, inference performance, rollout, monitoring, and rollback. Own the tradeoffs between quality, latency, and cost.
- The interface between model and harness. Work with the engineering team on context assembly, memory, compaction, and tool design. Decide which failures need better training, better context, or a change to the execution loop.
Qualifications
- You've fine-tuned LLMs before. You’ve worked directly on data preparation, training, and evaluation, and can explain what improved, what didn't, and why.
- You've built RL environments. You have hands-on experience defining tasks, observations, actions, rewards, and episode completion, and debugging the behavior those choices produce.
- You've deployed ML models to production. You've taken responsibility for serving and operating a model used by real users or systems, including monitoring and reliability.
- Strong Python and ML engineering fundamentals, with practical experience in a deep learning framework such as PyTorch. You can move between training code, data pipelines, experiments, and production software.
- Good experimental judgment. You question the metric, inspect the data, investigate regressions, and know when a simpler approach is the right one.
- Experience with tool-using agents, LLM post-training with RL, synthetic data, or security systems is especially relevant.
Skills
Benefits
- healthcare, dental and vision coverage
- an unlimited token budget
- competitive salary and equity
About Cotool
AI Agents for Security Operations Teams
Cotool is an agentic security platform that eliminates manual and repetitive work for security teams. Today’s cybersecurity teams remain bogged down by alert fatigue, context switching, and documentation overhead. Security teams don't need more dashboards with "AI-powered insights" — they need their time back. Cotool empowers teams to build security their way. Our platform provides an AI co-pilot that automatically assembles context across tools, a no-code agent builder that transforms successful investigations into reusable automations, and automated report generation, providing comprehensive documentation in seconds. Unlike solutions that force teams to adapt to vendor workflows, Cotool puts the power of AI directly in practitioners' hands, letting them shape tools around their unique processes. Early users have already cut time spent on investigation and detection engineering by 70%.
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