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About This Role
Company Overview
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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
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As a Lead AI Solutions Delivery Engineer, you will be on the front lines, embedded directly with strategic enterprise customers to solve complex business problems using Docusign’s proprietary Agentic capabilities, third\-party AI integrations, and existing tech stacks. You will bridge the gap between business and technology, defining customer workflows, designing bespoke architectures, writing code, and engineering prompts to deliver tangible business outcomes. You will leverage Docusign’s IAM Platform, Docusign and third party MCP servers, APIs, and third party integrations with leading AI ecosystems (like OpenAI, Microsoft CoPilot, and Claude) to optimize enterprise agreement lifecycles against contract populations exceeding millions of agreements. By capturing insights from these field deployments, you will collaborate cross\-functionally with Docusign’s product and engineering teams to shape our long\-term AI roadmap. This role is a perfect fit if you thrive in client\-facing environments and have a proven track record deploying GenAI and LLMs in production.
This position is an individual contributor role reporting to the Sr. Director, Value Delivery Realization. Responsibility
- Design and lead cross functional customer\-facing engagements to develop and deploy a modern Agentic vision of Agreement Management business processes
- Design, build, and deploy AI enabled workflows and solutions that integrate Docusign AI capabilities including but not limited to, Docusign’s MCP Server and APIs, Docusign Web Forms backed by APIs to third party systems, Custom Extractions, Docusign skills in third party agentic platforms (e.g. Docusign’s integration with Open AI, Claude Cowork, Microsoft CoPilot, Harvey and Legora) and other similar customer Agentic Tech stacks to automate and optimize customer business processes
- Execute deployment 'firsts' to prove out, capture, and productionize new Agentic implementation patterns, repeatable use cases and toolkits
- Run technical demos, trainings, and workshops for technical and non\-technical audiences
- Partner directly with customer stakeholders, Docusign product, engineering, success, services and partner team members and third party system integrators to translate open\-ended operational business requirements into production level AI enabled business solutions
- Memorialize repeatable deployment strategies and contribute insights and learnings back to Docusign product and engineering teams
- Develop robust data pipelines, containerized microservices, distributed system architectures, and system context management repositories to ensure autonomous AI tools operate accurately and reliably
- Maintain strong knowledge of the latest developments in LLM capabilities, implementation patterns, and AI product development stacks
- Synthesize field learnings, establish repeatable configuration scripts and deployment patterns, and develop cross functional data based recommendations to establish and deliver product consulting toolkits to establish enterprise\-deployable implementation guides
- Partner with our Partner Enablement and larger partner organization to equip the partner ecosystem to implement well\-architected solutions by contributing repeatable deployment best practices and blueprints
- Evaluate human and AI\-generated code critically for correctness, quality, security, performance, and compliance within isolated cloud environments
Job Designation
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Remote:
Employee is not required to be in or near an office frequently
and works from a designated remote work location for the majority of the
time.
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
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Basic
- Bachelor's or advanced degree in Computer Science, Mathematics, Computer Engineering, Statistics, Operations Research, etc. or equivalent practical experience
- 12\+ years of experience in software engineering, deployment\-focused engineering, or customer\-facing technical delivery roles, in an established technology company, including AI focused startups or scale\-ups
- 1\.5\+ years of experience implementing LLMs, production\-grade GenAI applications, autonomous agents, or orchestration frameworks for production level software
- Experience working directly with customers during POCs, architecture reviews, and technical evaluations
- Experience with Python, Java, C\+\+, or systems fundamentals
Preferred
- Prior experience as a forward deployed engineer, customer\-facing technical lead, startup CTO, enterprise architect or software engineer with consulting experience
- Experience deploying autonomous agents or AI orchestration frameworks within highly regulated environments such as finance or healthcare
- Deep understanding of data security, compliance frameworks, and isolated enterprise cloud deployments
- Ability to collaborate cross functionally and “love of learning” posture required to support the rapid development in the AI space
- Willingness and ability to travel 25–50% of the time to customer sites as required
- Experience designing agent\-based or LLM\-powered applications beyond simple API calls, including multi\-step workflows, orchestration, and failure handling
- Strong communication and customer engagement skills for conducting customer discovery and conveying and synthesizing technical concepts to or from diverse stakeholders, including non\-technical audiences
- A passion for driving and taking responsibility for customer outcomes, rather than just making customer recommendations
- A bias toward action and a “learning posture” in the ever changing world of AI and LLMs with the ability figure things out on the fly
- Excitement about building and operating AI agents in production, learning with customers, and then sharing those learnings with cross functional teams
Wage Transparency
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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:
Washington $158,300\.00 \- $232,575\.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
Work Authorization Notice:
Please note that we do not provide visa sponsorship or immigration support for this position. Applicants must already be authorized to work in the United States on a full\-time, permanent basis without the need for current or future sponsorship.
Life At Docusign
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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 $158K-$232K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 8% above the category median. Disclosed range: $158K to $232K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
DocuSign AI Hiring
DocuSign has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, Washington, DC, US. Compensation range: $182K - $232K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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