Senior AI Researcher

The Role

We're hiring our first dedicated AI Researcher to advance the core models powering Ares. You'll work alongside our VP of AI Engineering and a small AI engineering team, with direct collaboration with our CEO.

This is a research role, not an applied ML role. You'll own original research on offensive security agents — how they reason, plan, use tools, and operate autonomously over long horizons. You'll design experiments end-to-end, build the evaluation infrastructure the field doesn't yet have, and translate research wins into capability that ships.

What You'll Do

• Drive original research on offensive security agents — reasoning, planning, tool use, and autonomous long-horizon operation
• Advance Dagger's post-training pipeline: supervised fine-tuning, RL from verifier signals, LoRA adaptation, and evaluation against adversarial benchmarks
• Extend Javelin's co-evolutionary self-training architecture: curriculum design, self-play dynamics, and reward modeling for security-specific outcomes
• Design and execute experiments end-to-end, from hypothesis through writeup
• Build internal evaluation harnesses that measure capability rigorously, where no public benchmark exists
• Translate research into production handoffs to AI Engineering — model cards, deployment notes, and known failure modes
• Contribute to Assail's external research voice through papers, talks, responsible disclosures, and technical writing
• Collaborate with engineering teammates on research methodology and experimental design

What We're Looking For

• Original ML research output — published papers, widely cited preprints, significant open-source releases, or shipped research that materially advanced a production system
• Hands-on post-training experience with language models at the 7B+ parameter scale, end-to-end ownership of a pipeline including data, training, and evaluation
• Direct work with at least one of: RL from verifier or reward signals, preference optimization (DPO/IPO/KTO), or supervised fine-tuning with synthetic data pipelines
• Experience with agentic LLM systems — tool use, multi-step reasoning, planning, or long-horizon execution
• Ability to design evaluation that measures real capability and avoids contamination or specification gaming
• Strong Python and PyTorch, with experience in distributed training at multi-GPU scale
• Clear technical writing — research memos, experiment writeups, papers, or equivalent

Helpful but Learnable Here

• Working knowledge of offensive security fundamentals (we'll teach you the rest if you bring strong ML depth)
• Prior work on code-generating or code-reasoning models
• Experience with sparse, delayed, or expensive reward signals in RL
• Research on robustness, adversarial ML, or red-teaming of language models
• Familiarity with long-horizon agent benchmarks (SWE-bench, Cybench, WebArena, or similar)

Things We Deliberately Don't Require

• A PhD. Track record matters more than the credential. If your work demonstrates the capability, the degree is secondary.
• A security background. Strong ML researchers can develop security depth here, and we'll support you in doing it.
• A specific number of years. Senior is a function of judgment and output, not a count.

What This Role Will Teach You

• How to train and post-train capable models in a narrow, high-stakes domain
• How to design evaluation that holds up to scrutiny when no benchmark exists yet
• How agentic systems behave under adversarial conditions — including failure modes that don't appear in benign settings
• The full offensive security stack — API, web, and mobile — at a depth most ML researchers never reach
• How to make publication and disclosure decisions for dual-use research
• How research moves from hypothesis to production in a small team where the handoff is measured in days

What We Offer

• Competitive base salary and meaningful early-stage equity
• Comprehensive health and dental coverage
• Unlimited paid time off, including parental leave
• Conference, publication, and continued learning budget — we want you engaged with the research community
• The chance to work on a problem that matters, with people who care about doing it well