• Research audio data quality for machine learning • Investigate how audio quality, signal properties, dataset composition, and localized acoustic issues affect downstream model training, evaluation, and deployment. • Develop new metrics, benchmarks, diagnostics, and evaluation frameworks for measuring audio data quality in ways that are predictive of ML model performance. • Analyze and summarize Protege’s audio catalog and maintain clear, up-to-date quality scorecards and metrics for key speech datasets. • Develop methods to measure true acoustic properties directly from the waveform, including effective bandwidth, spectral energy distribution, high-frequency roll-off, noise, clipping, reverberation, distortion, and codec artifacts. • Build workflows that evaluate diarized or segmented speech regions, surfacing localized degradation that file-level averages may miss. • Design and run targeted evaluations connecting audio quality issues to downstream model behavior, including ASR performance, speaker embedding stability, learned speech representations, and synthesis quality. • Translate research findings into reproducible filtering rules, quality gates, and dataset selection strategies that improve dataset consistency across training runs.

Role Description 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 Qualifications - 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 Requirements - 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) 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 Benefits - 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

• Design and build datasets, tasks, and environments for benchmarking agentic systems and multi-step model behavior. • Translate real-world workflows into structured tasks, interaction traces, trajectories, stateful environments, and verifiable outcomes that can be used to evaluate advanced AI systems. • Develop frameworks that assess diversity, realism, coverage, fidelity, informativeness, and downstream usefulness of datasets for agentic systems. • Build quality scorecards and evaluation methods that make dataset strengths, weaknesses, and failure modes legible across teams. • Evaluate planning, tool use, robustness, recovery from failure, task completion, and generalization behavior in RL-style or agentic environments. • Connect model failures back to concrete dataset, environment, or task-design gaps and recommend improvements grounded in empirical evidence. • Contribute to tools and systems that automate dataset validation, environment generation, rollout analysis, benchmark construction, and evaluation workflows. • Improve internal infrastructure for reproducible experimentation, benchmark management, and evaluation quality. • Collaborate closely with research and engineering teams to identify data bottlenecks, improve evaluation methodology, and shape internal best practices around task-grounded AI training data. • Represent DataLab’s perspective in cross-functional discussions around dataset quality, benchmark design, and frontier agentic-system evaluation.

• Post-training for model development • Develop novel algorithms that improve model reliability, controllability, and alignment • Design and run experiments that shape model behavior • Partner with infrastructure teams to build scalable workflows • Contribute to publications, patents, and external research visibility