Senior AI Engineer (US)

Core tasks

• Agent development. Design, implement, and continuously improve the behavior and prompting of Ares' named agents, including orchestration patterns, hand-offs, planning loops, tool use, and shared memory.
• Model training and fine-tuning. Contribute to the model powering Ares across data curation, SFT, preference optimization (DPO/GRPO-style), and evaluation. Own pieces of the training pipeline from dataset construction through eval.
• Javelin loop. Extend the co-evolutionary self-training system that lets Ares learn from its own engagements and improve over time.
• Self-improvement systems (ARES-420 and successors). Build false-positive detection, tiered skill learning (suppression rules, agent directives, code-patch proposals), and the infrastructure that routes proposed changes through human approval and back into the platform.
• Evals. Design rigorous, security-specific evaluations covering OWASP Top 10 coverage, exploit chaining, finding accuracy, and agent reliability. Track performance over every model and agent change.
• Multimodal and platform expansion. Contribute to vision capabilities, mobile (iOS/Android) coverage, and BYOK support shipping in Sidewinder and beyond.
• Production reliability. Own latency, cost, observability, and failure-mode analysis for agents running in customer engagements. Partner with the platform team on Kubernetes-based deployment.
• Customer-facing accuracy. Contribute to the live accuracy gauge and other surfaces where model and agent quality is exposed to customers.

Must-have skills

• 5+ years building production ML/AI systems, with at least 2 years working directly on LLMs or LLM-powered agents.
• Deep Python; strong, production-grade engineering practices (testing, code review, observability).
• Hands-on fine-tuning experience: SFT, preference optimization (DPO, GRPO, RLHF/RLAIF), data curation, and synthetic data generation.
• Strong grasp of transformer architectures and the modern training stack (PyTorch, Hugging Face, DeepSpeed or FSDP, accelerate).
• Experience designing and shipping multi-agent or tool-using LLM systems in production — not just demos.
• Rigorous eval design: building harnesses, tracking experiments, and making model/agent decisions based on data rather than vibes.
• Inference optimization experience: vLLM or TensorRT-LLM, quantization, throughput/latency tradeoffs.
• Comfort with retrieval pipelines, vector stores, and structured memory for agents.
• Kubernetes and containerized deployment fluency.
• Genuine interest in offensive security and the ability to ramp quickly on OWASP Top 10, API security, web app pentesting, and mobile pentesting concepts. Direct offensive security background is a strong plus but not required.

Nice to have

• Offensive security background: OSCP/OSWE/OSWA, CTF, bug bounty, or prior red team work.
• Research publications at NeurIPS, ICML, ICLR, USENIX Security, IEEE S&P, Black Hat, or DEFCON.
• Open source contributions to agent frameworks or LLM tooling.
• Experience with adversarial ML or red-teaming AI systems.
• Familiarity with mobile app reverse engineering or binary analysis.