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Senior AI Researcher_*]:min-w-0 gap-3">
Assail | AI Engineering | Reports to VP of AI Engineering | Remote-friendly, Boston HQ_*]:min-w-0 gap-3">
The Role_*]:min-w-0 gap-3">
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 - a researcher and practitioner with 26 years of offensive security experience, contributions to the OWASP API Security Top 10, and a permanent exhibit at The Mob Museum.
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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.
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The feedback loop is fast and adversarial. Research that proves out goes into production. Research that doesn't gets killed quickly so the next bet can start.
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What You'll Do_*]:min-w-0 gap-3">
- 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
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What We're Looking For_*]:min-w-0 gap-3">
You don't need every item on this list. We care more about depth where you have it than breadth where you don't.
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Core experience that matters most:_*]:min-w-0 gap-3">
- 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
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Helpful but learnable here:_*]:min-w-0 gap-3">
- 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)
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Things we deliberately don't require:_*]:min-w-0 gap-3">
- 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.
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What This Role Will Teach You_*]:min-w-0 gap-3">
- 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
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What We Offer_*]:min-w-0 gap-3">
- 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
