Job Type: Flexible Contract (Remote)
Job Summary: We are hiring PhD-level AI Security Engineers for a contract project that tests whether frontier AI models can handle real security work. You will create hard tasks from your field that a leading AI model cannot solve, then review where and why it fails.

What you will do

• Create command line tasks based on real security problems. Examples:
• Find and patch a vulnerability in a real codebase, such as memory corruption, an injection flaw, or an authentication bypass.
• Analyze a suspicious binary and report what it does, how it persists, and how to detect it.
• Audit a cryptographic implementation and fix misuse of keys, nonces or randomness.
• Investigate logs or a packet capture to reconstruct an intrusion and identify the root cause.
• Harden a misconfigured service, container or CI pipeline.
• Find and fix a weakness in an ML system, such as a poisoned dataset, an unsafe model loading path, or a prompt injection hole in an agent.
• Build each task’s environment, including the code, data and dependencies, and write a reference solution.
• Write automated tests that check whether a solution is correct, with consistent results on every run.
• Run your task against a leading AI model, review its attempt, and adjust the task until the model fails because the problem is truly hard, not because the instructions are unclear.

Who we are looking for

• A PhD in computer science, security, machine learning, engineering or another STEM field. PhD candidates and students are welcome to apply.
• Hands-on experience in security, such as vulnerability research, reverse engineering, applied cryptography, malware analysis, secure systems or AI/ML security. Ten or more years of experience is ideal.
• At least one publication, academic or professional (for example a paper, CVE write-up, conference talk or technical advisory).
• Comfort with Python, the Linux command line, Docker and Git.
• Familiarity with tools such as Ghidra, gdb, Wireshark or similar analysis tooling is a plus.
• Experience with ML frameworks such as PyTorch or TensorFlow is a plus.
• Experience with experimental design, benchmarking or testing AI systems is a plus.
• Peer review experience, or experience building technical tests, CTFs or assessments, is an advantage.
• Able to work independently in a remote setting and explain technical findings clearly.

Why Cobalt AI

• Advance frontier AI where it counts. Apply your expertise to the data frontier labs cannot get any other way, where your reasoning directly shapes how the next generation of models solves technical problems.
• Grow professionally. Expand your influence through evaluation projects, advisory roles and research collaborations, while deepening your understanding of how frontier models are trained and assessed.
• Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
• Set your own schedule. Flexible 10 to 40 hour weeks that fit around your current role and your life.
• Competitive pay. Paid per accepted task, with higher rates for consistently strong work.

Education:

• Doctorate (Required)

Work Location: Remote