Senior AI Researcher

Assail | AI Engineering | Reports to VP of AI Engineering | Remote-friendly, Boston HQ

About Assail

Assail builds autonomous offensive security. Our platform, Ares, finds vulnerabilities in production systems by reasoning about them the way an experienced attacker would — chaining flaws across APIs, web applications, and mobile surfaces to surface the exploits that scanners miss and human testers run out of time to find.

We train our own models. Dagger is our 14B-parameter offensive security model, fine-tuned for vulnerability discovery and exploit reasoning. Javelin is our co-evolutionary self-training architecture, where attacker and defender models train against each other to push capability further than either could reach alone. The research surface is wide open, the domain is consequential, and the work ships into a platform that's actively used against hardened enterprise targets.

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 — 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.

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.

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.

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

You don't need every item on this list. We care more about depth where you have it than breadth where you don't.

Core experience that matters most:

• 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

Senior AI Engineer, Ares Platform

Team: Ares AI Engineering Reports to: Ilir Osmanaj, VP of AI Engineering Location: Boston, MA (hybrid) or remote with overlap to ET working hours

Position summary

The Senior AI Engineer is a core builder on the team responsible for the agents and models that power Ares — Assail's autonomous offensive security platform for APIs, web applications, and mobile applications. This role works directly on Ares' named-agent architecture (Polemos, Hermes, Enyo, Momos, Dolos, Themis, Aletheia, Argus, Kratos), the model powering Ares, and the Javelin co-evolutionary self-training loop. The engineer will ship capabilities that move the platform forward across exploit chaining, multimodal vision, mobile coverage, self-improvement, and customer-facing accuracy.

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.

Company Description

Assail is a venture-backed cybersecurity company building the next generation of offensive AI. Our platform, Ares, is an autonomous AI agent purpose-built for continuous security testing of the modern application stack — web applications, APIs, and mobile apps. Unlike point-in-time assessments or legacy DAST tools, Ares delivers continuous, proof-based exploitation that mirrors real-world attacker behavior. Based in Boston, MA, Assail serves enterprise, federal, and global markets, and came out of stealth at RSA 2026.

Role Description

This is a full-time, on-site role in Boston, MA for a Sales Development Representative (SDR). You will be responsible for identifying and qualifying sales opportunities across enterprise, federal, and commercial segments, engaging prospects through outbound calls and emails, and booking qualified meetings for Account Executives. The role demands a proactive, structured approach to outbound prospecting, disciplined CRM hygiene, and close collaboration with sales and marketing to build and advance pipeline. You'll be selling into security-conscious buyers — red team leads, AppSec engineers, CISOs — so intellectual curiosity about the cybersecurity space is a real advantage.

Qualifications

• Demonstrated experience in inside sales, lead generation, or outbound prospecting

• Strong written and verbal communication skills; ability to convey a compelling value proposition concisely and without fluff

• Comfort engaging technical and executive buyers across security, IT, and procurement

• Proficiency with CRM tools (HubSpot preferred) and sales engagement platforms (Reach.io, ZoomInfo, or equivalents)

• Ability to work independently, hit activity targets, and operate with discipline in a fast-moving startup environment

• Experience in cybersecurity, SaaS, or enterprise software sales is a strong plus

• Bachelor's degree in Business, Marketing, or a related field preferred, but not required for the right candidate

Account Executive, Enterprise

Team: Enterprise Sales Reports to: Chief Revenue Officer Location: Boston, MA (hybrid) or remote with travel

Position summary

The Account Executive owns the full enterprise sales cycle for Ares, Assail's autonomous multi-agent offensive security platform. This is a quota-carrying, closing role focused on landing and expanding Fortune 1000 accounts across regulated industries — financial services, healthcare, automotive, retail, and critical infrastructure. The AE works alongside the CRO, sales engineering, and the founder's office to run disciplined, multi-threaded deal cycles with CISOs, VPs of Application Security, Heads of Product Security, and their teams.

This is a builder's seat. The AE will help shape the enterprise playbook, contribute to pricing and packaging conversations, and set the standard for how Assail sells.

Core tasks

• Own the full sales cycle. Prospect, qualify, run discovery, scope POVs, negotiate, and close new-logo enterprise deals. Carry an annual quota and forecast accurately every week.

• Build and manage pipeline. Generate qualified pipeline through a mix of outbound, account-based plays, ecosystem partners, events, and inbound. Maintain pipeline coverage of at least 3x quota.

• Multi-thread enterprise accounts. Map account org charts, build relationships across security, engineering, procurement, and the C-suite, and run executive-level conversations with CISOs and CIOs.

• Run rigorous discovery. Uncover the customer's application security program, current AppSec/DAST/pentest spend, compliance drivers (PCI, HIPAA, FedRAMP, SOC 2, DORA), and the business outcomes they need.

• Lead POVs and technical evaluations. Partner with sales engineering to scope, run, and close proof-of-value engagements. Translate Ares' findings into business risk and ROI the buyer's executive team will fund.

• Negotiate and close. Lead pricing, redlines, and procurement cycles. Partner with legal and finance to close clean, multi-year enterprise agreements.

• Expand existing accounts. Drive land-and-expand motion across business units, subsidiaries, and adjacent application portfolios within named accounts.

• Operate the CRM with discipline. Keep HubSpot pristine — stage hygiene, next steps, MEDDPICC fields, close dates, and accurate forecasting. The pipeline is the product.

• Feed the company. Surface competitive intelligence, product gaps, pricing signal, and customer language back to marketing, product, and the CRO. AEs at this stage shape the company, not just hit a number.

• Represent Assail externally. Attend industry events, customer dinners, analyst briefings, and partner meetings. Carry the brand the way it deserves to be carried.

Must-have skills

• 7+ years of quota-carrying enterprise software sales experience, with at least 3 years selling cybersecurity to the Fortune 1000.

• Demonstrated track record of consistently hitting or exceeding $1M+ ARR quotas, with deal sizes of $150K–$1M+ ACV.

• Experience selling into CISOs, VPs of Application Security, Heads of Product Security, and DevSecOps leaders.

• Deep fluency in the AppSec landscape: DAST, SAST, IAST, pentesting, bug bounty, ASPM, and where each fits in the buyer's stack.

• Disciplined sales methodology — MEDDPICC, Command of the Message, Force Management, or equivalent. Able to articulate champion-building, economic-buyer access, and decision criteria with rigor.

• Strong technical credibility. Able to run a discovery call with a Head of AppSec without a sales engineer in the room, and to demo Ares competently.

• Experience running POVs and technical evaluations against incumbents and competitive tools.

• Excellent written and verbal communication. Crisp executive presence with CISOs and CIOs.

• HubSpot or Salesforce fluency, with the operational discipline to keep forecasts accurate.

• Comfortable in an early-stage environment — high autonomy, ambiguity, fast iteration, and direct collaboration with the founder and CRO.

Nice to have

• Prior experience selling an emerging-category or AI-native security product where the buyer needs education, not just comparison.

• Existing relationships with CISOs and AppSec leaders in financial services, healthcare, automotive, or critical infrastructure.

• Experience selling into the U.S. federal government, DoD, or intelligence community, including familiarity with FedRAMP, ATO processes, and federal procurement vehicles.

• Background in security engineering, penetration testing, or AppSec prior to moving into sales.

• Experience as an early AE at a Series A–C security startup that scaled.