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About This Role
Location
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San Francisco; New York City
Address
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San Francisco, California
Employment Type
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Full time
Location Type
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Hybrid
Department
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Engineering, Product \& Design
Compensation
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- Tier 1Base Salary $245K – $295K • Offers Equity
Ironclad is the leading AI contracting platform that transforms agreements into assets. Contracts move faster, insights surface instantly, and agents push work forward, all with you in control. Whether you’re buying or selling, Ironclad unifies the entire process on one intelligent platform, providing leaders with the visibility they need to stay one step ahead. That’s why the world’s most transformative organizations, from Rivian to the World Health Organization and the Associated Press, trust Ironclad to accelerate their business.
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.
*This is a hybrid role based out of our San Francisco office. Office attendance is required at least twice a week on Tuesdays and Thursdays for collaboration and connection. There may be additional in\-office days for team or company events.*
About the team
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Jurist is Ironclad's AI\-native contract review experience, built for the lawyers and business teams who negotiate agreements every day. It brings together first\-pass redlining, reasoning\-backed suggested changes, deviation summaries, obligation detection, and reusable playbook libraries into a single, trusted workspace for contract review and negotiation.
Jurist is one of Ironclad's highest\-priority product surfaces, sitting at the intersection of applied AI, legal workflows, and end\-user trust. The team owns both the user\-facing product and the backend systems that power it, including document understanding, AI orchestration, clause detection, review state management, and enterprise\-scale performance.
We're hiring a Technical Lead Manager to lead the engineering team behind Jurist and define the next generation of AI\-assisted contract review. This is a high\-impact leadership role for someone who wants to build product at the intersection of legal workflows, applied AI, and end\-user experience.
What you'll do
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- Set the technical vision and roadmap for Jurist, spanning the full stack from user\-facing review UI to the backend systems that power accurate, reliable AI outputs.
- Lead architecture and execution for Jurist's core experiences: first\-pass redlining, reasoning\-backed suggestions, deviation summaries, obligation detection, and playbook\-driven review workflows.
- Own the end\-to\-end quality bar for Jurist, including evals, benchmarking, error analysis, regression prevention, and product instrumentation that improves accuracy and user trust over time.
- Drive backend and platform decisions that support document understanding, AI orchestration, review state management, permissions, and enterprise\-scale performance.
- Shape the Jurist UI and interaction model in close partnership with Product and Design, ensuring AI outputs are explainable, actionable, and trustworthy for legal and business users.
- Partner closely with Product, Design, Legal domain experts, Services, and go\-to\-market teams to translate customer pain points into scalable product capabilities.
- Manage and grow a team of strong engineers across frontend and backend, balancing speed, technical rigor, and product judgment.
- Establish strong engineering practices across design reviews, technical planning, experimentation, rollout safety, and operational excellence.
What we're looking for
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### Experience and leadership
- 5\+ years of experience architecting, building, and operating complex software systems in production.
- 2\+ years of technical leadership or engineering management experience, including mentoring and growing high\-performing teams.
- A strong track record of shipping ambiguous, zero\-to\-one or one\-to\-scale products where product and technical direction evolved quickly.
- High agency and strong product instincts; you know when to go deep technically and when to simplify.
### Technical depth
- Experience building and owning user\-facing products end\-to\-end, including both the UI layer and the backend systems that support it.
- Deep backend and distributed systems experience, especially in systems that need to be reliable, observable, secure, and scalable.
- Hands\-on fluency with modern applied AI systems, including LLM orchestration, retrieval patterns, prompt and workflow design, model evaluation, and production quality controls.
- Experience building user\-facing AI products where accuracy, explainability, and trust materially affect adoption.
- Comfort working across application, platform, and infrastructure layers.
- Familiarity with modern web and cloud stacks. Our teams commonly work in TypeScript, React, Node.js, Docker/Kubernetes, and Google Cloud, though direct experience in our exact stack is not required.
### Product mindset
- You care deeply about building tools users love, not just systems that technically work.
- You can translate domain complexity into intuitive product behavior and clear engineering priorities.
- You enjoy close collaboration with product managers, designers, researchers, and customer\-facing teams to iterate quickly on new capabilities.
- You think about the full user journey, from the moment a reviewer opens a contract to the moment it's signed.
The TLM role at Ironclad
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We believe in technical leaders who stay close to the work.
- Roughly 80% building: shaping technical direction, designing systems, reviewing architecture, driving quality, and staying hands\-on where it matters most.
- Roughly 20% leading: coaching engineers, managing performance, hiring thoughtfully, and building a healthy, high\-ownership team culture.
You'll join a company building at the center of AI and enterprise software, with the chance to solve hard product and technical problems that matter to some of the world's most sophisticated legal and business teams.
Base Salary Range: $245,000 \- $295,000
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.
Compensation Range: $245K \- $295K
Salary Context
This $245K-$295K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ironclad, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($270K) sits 23% above the category median. Disclosed range: $245K to $295K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ironclad AI Hiring
Ironclad has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $295K - $295K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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