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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
We're building the foundation that lets every engineering team at Docusign build, ship, and operate AI-native and agentic systems safely and at scale. This isn't a traditional infrastructure or DevOps role: it's an architecture and orchestration role for someone who has already designed and run production LLM and agentic systems at scale. As the AI Platform Lead, you own the architecture of our AI platform — the infrastructure, model gateway and routing layer, the agent orchestration frameworks, and the inference and GPU serving infrastructure that other teams build on top of. You set the technical direction, write the reference implementations and standards, and partner directly with product and platform engineering teams to get them adopted. You are deeply hands-on: you prototype, you write production code, and you use AI coding assistants and agentic tooling as a core part of how you work — but your primary output is the architecture and platform other engineers build against, not a single service. The successful candidate has real, hands-on experience building or operating LLM-powered and agentic services in production — not just experimenting with them. You're self-directed, comfortable owning ambiguous technical direction, and able to explain complex AI-infrastructure tradeoffs to both engineers and non-technical stakeholders.
This position is an individual contributor role reporting to the Sr. Director of Cloud Services.
Responsibility
- Own the end-to-end design, build, and integration of the AI/agentic platform that compliments human-operated execution with systems that route, remediate, and provision themselves — model gateway, agent orchestration, inference serving — and the standards other teams build against. You write the code; you don't hand it off
- Architect, build, and operate a multi-provider LLM gateway and routing layer (e.g., LiteLLM, Portkey, Bedrock, or equivalent) so requests reach the right model automatically — secure, cost-aware, and reliable — without a person routing them by hand
- Design and build the orchestration layer and frameworks (e.g., LangGraph, CrewAI, or a custom runtime) that let agents carry out work autonomously, and define the rules — capability schemas, guardrails, policy enforcement — those agents operate under
- Architect and operate scalable model-serving and GPU infrastructure (e.g., vLLM, KServe/Triton, Kubernetes-based inference serving, autoscaling, capacity planning) that balances latency, throughput, and cost
- Replace manual provisioning and ticket-driven execution with automated, self-service systems — teams get what they need and common failures resolve themselves, without a person in the loop
- Build golden paths for fine-tuning, model/agent registries, evaluation, and safe deployment — including CI/CD for models, prompts, and agents with canary, A/B, and shadow rollouts
- Define and evangelize architecture patterns, security guardrails, and reusable accelerators/reference architectures that reduce time-to-value for teams building on the platform
- Partner closely with product and platform engineering teams to drive adoption of the AI platform, unblock their AI/agentic use cases, and translate platform capabilities into their roadmaps
- Implement security controls and Zero Trust practices — including SBOMs, image signing, and policy-as-code — partnering with InfoSec to maintain a strong security posture across model access, data handling, and agent permissions
- Establish observability for AI infrastructure — token spend, model latency, GPU utilization, agent behavior — and continuously verify that what you've automated performs as intended
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
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience
- 12+ years of experience in Cloud, Platform, or Infrastructure Engineering with a Bachelor's degree or 8+ years of experience with a Master's degree
- Experience architecting and operating LLM-powered or agentic systems in production (not just prototypes or POCs)
- Experience replacing manual, ticket-driven operations with automated or self-service systems — building the systems that act on what they observe, not just dashboards and monitors
- Experience designing and operating an LLM gateway / model routing layer across multiple providers (e.g., OpenAI, Anthropic, Bedrock, Azure OpenAI)
- Experience building and operating agent orchestration frameworks and agentic workflows (e.g., LangGraph, CrewAI, AutoGen, or a custom orchestration runtime) in production
- Experience with GPU/inference-serving infrastructure and Kubernetes at scale (e.g., vLLM, KServe, Triton, model autoscaling, capacity planning)
- Experience writing, reviewing, and testing production-quality code (Python and/or Go) — this is a build role, not a slide-deck role
- Experience with cloud platforms (AWS, Azure, or GCP) and Infrastructure-as-Code (Terraform or Ansible)
- Experience setting technical direction across teams and driving adoption of a shared platform, with strong written and verbal communication
Preferred
- Experience with RAG and vector database infrastructure (embedding pipelines, retrieval services) in production
- Experience running LLMOps practices — model evaluation, prompt/version management, guardrails, and agent monitoring
- Experience with containerization and microservices architecture (Docker, Kubernetes) beyond AI workloads
- Understanding of network architecture and security best practices (VPNs, firewalls, load balancing) in cloud environments
- Experience presenting architecture and roadmap to senior technical leadership and driving org-wide adoption
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: $164,700.00 - $266,000.00 annual 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
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 $164K-$266K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $164K to $266K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
DocuSign AI Hiring
DocuSign has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $266K - $342K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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