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About This Role
Company Overview
Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).
What you'll do
As the AI Transformation Director, you will launch and scale AI agents and lead pilots. This is a senior activation role for someone who possesses deep strategic knowledge in GTM operations alongside expert-level fluency in modern LLMs, agentic orchestration, multi-agent governance, and enterprise systems integration. You will partner directly with C-suite and VP leaders across revenue and technology to drive operational velocity, superior SaaS unit economics, and sustainable competitive advantage.
This position is an individual contributor role reporting to the Vice President, Enterprise Transformation & Program Office.
Responsibility
- Apply deep expertise in SaaS sales models, compensation design, quota setting, and territory optimization to integrate these domain-specific business rules into the logic of autonomous AI agents to ensure alignment with financial and operational objectives
- Focus on launching pilots, and globally scaling up pilots, drive rep enablement, track friction, and manage culture shift
- Own the end-to-end lifecycle of complex AI agent program rollout-from conceptualization and development to pilot design, rollout governance, rep enablement, friction tracking, and global scaling, and establish rigorous readiness gates and iteration frameworks
- Define target populations, pilot/control cohorts, sequencing, and rollout approach
- Track adoption by cohort and identify where performance is off track, provide RCA and action plan
- Mobilize FLMs, Enablement, Product, or Ops when adoption needs correction
- Determine when to iterate, scale, pause, or transition into run-the-business ownership
- Connect AI transformation directly to enterprise financial levers, maximizing LTV, shortening CAC payback periods, accelerating pipeline velocity, and lowering cost-to-serve
- Integrate SaaS sales models, compensation design, quota setting, and territory optimization rules into the logic of autonomous AI agents to ensure financial and operational alignment
- Serve as a trusted advisor to the CRO, CMO, CTO, and VP of Transformation, aligning AI capabilities with core growth metrics and driving change management across global revenue teams, and create the GVP/senior-leader cadence needed to drive accountability and adoption
- Establish enterprise-grade standards for Responsible AI, model drift monitoring, hallucination prevention, data security, and auditability across all automated revenue motions
- Connect AI transformation directly to enterprise financial levers, designing frameworks that maximize LTV, shorten CAC payback periods, accelerate funnel velocity, and lower cost-to-serve
- Define global patterns, reusable agent architectures, Agentic UI (AGUI) standards, and governance protocols to ensure rapid, secure scaling across all global GTM regions
- Direct cross-functional pods (Engineering, Applied AI, RevOps, Product, Data Platform) in deploying production-grade agentic platforms, custom RAG integrations, and autonomous action engines
- Define enterprise-grade standards for Responsible AI, model drift monitoring, hallucination prevention, data security, and auditability across all automated revenue motions
- Evaluate continuously emerging market trends in LLM orchestration, multi-agent frameworks (LangGraph, AutoGen, CrewAI), and sales intelligence to keep the GTM tech stack at the market forefront
- Lead change management visions across global revenue teams, driving organizational readiness, culture shifts, and sustained adoption of autonomous AI systems
- Lead the end-to-end lifecycle of complex AI agent programs, from conceptualization and development to deployment and performance monitoring, and establish rigorous program management frameworks to oversee agent interdependencies, lifecycle governance, and integration, ensuring that autonomous systems consistently deliver reliable, high-value outcomes aligned with enterprise business goals
- Focus on designing and launching pilots, and globally scaling up pilots that pass the gate, drive rep enablement, track friction, and manage culture shift
Job Designation
Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation)
Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law.
What you bring
Basic
- Experience in SaaS Sales operations, including designing sales compensation structures, managing quota allocation, and optimizing territory models in large-scale enterprise environments
- 15+ years of leadership and program delivery experience within Go-To-Market operations, Revenue Operations, and GTM strategy in enterprise SaaS environments
- 6+ years of specialized leadership in applied AI, machine learning automation, and operationalizing LLM agent applications
- Experience designing and scaling multi-workstream AI transformation roadmaps across complex, global GTM motions (Sales, CS, Marketing, Digital, CPQ, Billing)
- Experience with AI system architecture: multi-agent frameworks (LangGraph, AutoGen, CrewAI), LLM orchestration, RAG systems, vector DBs, and modern GTM data platforms (Snowflake, Reverse ETL, CDPs)
- Experience with core SaaS financial metrics (ARR, NRR, LTV, CAC payback, pipeline velocity)
- Experience presenting to, influencing, and aligning C-suite leadership on disruptive strategy, build vs. buy decisions, and technology investments
- Bachelor's Degree in Computer Science, Data Science, Engineering, or related technical/quantitative field
Preferred
- Master's Degree / MBA in Data Science, Computer Science, or Management Information Systems
- Recognized industry thought leadership in AI for Revenue Operations, AI GTM Systems, or Agentic AI Workflows
- Expertise in leading build vs. buy evaluations for enterprise AI tools and negotiating key technology partnerships
- Track record of successfully executing multi-million dollar AI/digital transformation portfolios that deliver measurable financial ROI
Wage Transparency
Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience.
Based on applicable legislation, the below details pay ranges in the following locations:
California: $212,100.00 - $342,650.00 base salary
This role is also eligible for the following:
- Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals. Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance.
- Stock: This role is eligible to receive Restricted Stock Units (RSUs).
Global benefits provide options for the following:
- Paid Time Off: earned time off, as well as paid company holidays based on region
- Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement
- Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment
- Retirement Plans: select retirement and pension programs with potential for employer contributions
- Learning and Development: options for coaching, online courses and education reimbursements
- Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events
Life at Docusign
Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal.
We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live.
Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at [email protected].
If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at [email protected] for assistance.
Salary Context
This $212K-$342K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At DocuSign, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($277K) sits 31% above the category median. Disclosed range: $212K to $342K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
DocuSign AI Hiring
DocuSign has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $235K - $342K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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