דרושים » אבטחת מידע וסייבר » Head of LLM Research

You will lead security research. This is not a bug hunting role. Your job is to invent the approaches that become product capabilities: new ways to detect, triage, and remediate vulnerabilities using LLMs and program analysis, proven on real data before a customer ever sees them.

You own the path from idea to shipped capability. You form the hypothesis, build the proof of concept, measure whether it actually works, and partner with engineering until it is in the product. You also own the quality bar: the benchmarks and evaluations that decide whether what we ship is good enough to put in front of customers.

You will set the research agenda, not just execute one. As the team grows, you will shape how research works here.

What You'll Own

The research agenda. Identify where new approaches can meaningfully beat the state of the art in vulnerability detection, triage, and remediation, then decide what we pursue and what we kill. No one hands you a backlog.

Approaches that ship. Build proofs of concept for new detection and remediation techniques, validate them against real world code and data, and carry the winners through to production with engineering. You are accountable for ideas becoming product, not staying research.

The quality bar. Design the datasets, benchmarks, and evaluation pipelines that measure precision, coverage, and false positive rates. Nothing reaches customers past a bar you have not signed off on, and if quality slips, you catch it first.

Vulnerability depth. Deep research into modern attack vectors across cloud (AWS/GCP), containers, microservices, APIs, AI generated code, and LLM applications. Not just how vulnerabilities are found, but how they are born, how they are fixed, and how a whole class gets eliminated.

The data foundation. The internal corpus of vulnerabilities, exploit patterns, and remediation strategies the platform learns from. Its depth and correctness are yours.

Our research voice. What we publish, where we speak, and the credibility the company earns in the security community. Your work should be visible.

As a Senior Product Manager , you will sit at the intersection of data science, engineering, and product, translating complex ML and causal inference capabilities into a product our customers love. You will work hand-in-hand with our data science and the engineering teams to shape how models are designed, evaluated, and delivered into production.

Own a significant part of the product roadmap for our value measurement platform, with a focus on data science driven features (causal inference, incrementality modeling, attribution, and media mix optimization) and customer onboarding.
Partner closely with data scientists from problem framing through experimentation, model validation, and productization, youll be expected to critically read model specs, challenge assumptions, and help prioritize research directions based on customer impact
Define success metrics and evaluation frameworks for ML models alongside the DS team, balancing statistical rigor with business usability
Own the UX of data-heavy features: collaborate with designers and data scientists to turn complex model outputs into intuitive user flows, clear visualizations, and dashboards that customers can actually act on
Design and prioritize dashboards, reports, and in-product analytics, defining what metrics to surface, how to visualize uncertainty and causal results, and how to make insights discoverable without overwhelming the user
Run user research and customer interviews to validate both the analytical value and the usability of features, and onboarding integration; translate findings into concrete design and product improvements
Translate customer problems into well-scoped technical and design requirements; translate model outputs and methodology into clear value propositions for customers
Prepare the implementation of features and support engineering, design, and DS teams through design reviews, QA, and launch, including writing acceptance criteria that account for model behavior, edge cases, data quality, and UX states (empty, loading, partial data, error)
Ensure data integrity through testing, acceptance tests, and ongoing monitoring across our diverse range of data products
Work with clients and internal stakeholders to understand business goals, identify relevant KPIs, and feed those insights back into the product, modeling, and dashboarding roadmap
Oversee end-to-end data flow and engineering processes, collaborating with ML, data, and engineering teams to ensure efficient and accurate operation during critical phases like client onboarding and daily production runs
Strategically plan for missing or imperfect data, designing product behavior and UX for new client onboarding, process failures, client-specific configurations, long-term seasonality, and external signals/features
Drive experimentation: help design A/B tests, back-tests, and validation studies to assess the impact of new models, features, and UX changes