Build AI contracting at enterprise scale. Ship it fast.

Ironclad is building AI on one of the most consequential datasets in enterprise software: over 2 billion contracts across thousands of the world's largest organizations. Contracts run every dollar, deal, and business relationship on the planet, and the AI transformation of this space is still largely uncharted. Engineers here are building agentic solutions to problems that haven't been solved yet, in a category that's still being defined. The feedback loop is short, the data is rich, and the people closest to the work are the ones deciding what gets built next. That ability to deliver impact quickly is why the the companies at the forefront of AI choose Ironclad to manage their contracts.

We’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50 and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit www.ironcladapp.com or follow us on LinkedIn.

About the Role

Ironclad's Intelligence Platform team owns Agent Assistant, Conversational Search, and Content Understanding — the systems that help customers and AI agents understand, find, and act on the right contract information. These are the flagship AI capabilities of our product, built and operated by a combined team of ML and ML infrastructure engineers.

We have multiple roles open, and are hiring a range of levels — Staff and Senior Staff. As a Staff or Senior Staff Engineer, Agentic Search, you'll own the architecture that combines LLMs and retrieval systems to answer complex, ambiguous questions about a customer's contracts, and you'll set the technical direction that other engineers across the AI organization build on. You'll partner closely with product, applied science, and engineering leaders to raise the company's search quality bar, and you'll bring the technical depth and eval-driven rigor to turn ambiguous problems into shipped, measurable improvements. Scope and ownership will be calibrated to level.

What You'll Do

• Own agentic search architecture. Design and evolve the systems that combine LLMs and retrieval to produce optimal answers to complex or ambiguous questions.

• Drive eval-driven development. Design and run the benchmarks and experiments that measure search quality, and use that feedback to continuously improve the system.

• Raise the search quality bar. Contribute to and influence the company's overall search quality standard.

• Own content understanding and ingestion. Turn raw documents into processed data that retrieval systems can consume, by building and using NLP/LLM models and pipelines.

• Set technical direction. Define architectural decisions and technical direction that other engineers across the AI organization build on.

Qualifications

• 10+ years building production systems, with a substantial portion in search, information retrieval, content understanding, or recommendation systems at meaningful scale.

• Demonstrated depth in one of: learned/hybrid retrieval (lexical + vector + reranking), query understanding/NLU pipelines, or production LLM agent systems — ideally more than one.

• Experience with search frameworks (Elasticsearch or equivalent — Solr, Vespa, OpenSearch; embedding search) in production, including relevance tuning and reranking.

• Fluency with modern LLM APIs and multi-provider orchestration (Anthropic, OpenAI, Google) — reasoning about token budgets, provider-specific tool-calling semantics, and prompt-caching trade-offs.

• Experience building eval-driven workflows — offline benchmarks, regression detection, structured A/B comparison — as opposed to shipping and hoping.

• Strong autonomy, ownership, and technical leadership across teams, including mentoring senior engineers and driving architectural decisions.

• Comfortable operating in a dynamic, fast-paced, outcome-driven environment.

Great to Have

• Hands-on experience with post-training algorithms and infrastructure, including SFT and RL.

• Experience with content understanding and/or information retrieval in structured-document-heavy domains.

• Prior work on RAG systems involving data sources in different formats (Google Docs, PDFs, DOCX, etc.).

Base Salary Ranges

• Staff: $188,000 - $235,000

• Senior Staff: $220,000 - $270,000

The base salary range represents the minimum and maximum of the salary range for this position based at our San Francisco headquarters. The actual base salary offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected candidate. Our base salary is just one component of Ironclad's competitive total rewards package, which also includes equity awards (a new hire grant, along with opportunities for additional awards throughout your tenure), competitive health and wellness benefits, and a commitment to career growth and development.

US Full-Time Employee Benefits at Ironclad:

• 100% health coverage for employees (medical, dental, and vision), and 75% coverage for dependents with buy-up plan options available

• Market-leading leave policies, including gender-neutral parental leave and compassionate leave

• Family forming support through Maven for you and your partner

• Paid time off - take the time you need, when you need it

• Monthly stipends for wellbeing, hybrid work, and (if applicable) cell phone use

• Mental health support through Modern Health, including therapy, coaching, and digital tools

• Pre-tax commuter benefits (US Employees)

• 401(k) plan with Fidelity with employer match (US Employees)

• Regular team events to connect, recharge, and have fun

• And most importantly: the opportunity to help build the company you want to work at

**UK Employee-specific benefits are included on our UK job postings

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.