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About This Role
Join us as we scale our business by building on our tremendous success around the world. The massive database market is going to double over the next few years and TiDB is a global player positioned as a major disruptor with TiDB Database and Database as a Service offering. TiDB is the proven enterprise database foundation for the AI era. We help companies solve today's database problems. Cost, scale, downtime, and complexity. With a distributed SQL architecture that is already battle\-tested at enterprise scale. And now we help those same companies future\-proof for innovating with AI, by giving them one unified foundation for transactional state, vector context, real\-time analytics, and safe agent operations. In a world where AI agents need to work on fresh data, act safely, and scale economically, TiDB is not another database add\-on. It is the highway system for agentic workloads. Large and high\-growth organizations in markets as varied as financial services, logistics, gaming, e\-commerce and software as a service have successfully deployed and expanded their TiDB footprint on mission\-critical applications. Our strong open\-source community roots (40,000\+ stars on GitHub), innovative products and inclusive culture draw passionate and dedicated people to our company. Learn more about TiDB careers and join our team to be at the forefront of innovation and growth.
### Role Overview:
We are seeking a Director of AI Ecosystem Partnerships to own and lead TiDB's partner ecosystem strategy in the AI era. This is a senior, high\-impact role focused on product and solution collaboration, not traditional co\-selling, reseller, or channel management. You will build and deepen integrations with leading AI platforms, infrastructure providers, and technology companies across North America, working closely with Product, Engineering, Marketing, and Sales to position TiDB as the data infrastructure backbone for agentic AI and GenAI applications.
### Responsibilities:
AI Ecosystem \& Partnership Strategy
- Own TiDB's AI ecosystem partnership strategy, including identification, prioritization, and development of partnerships that accelerate product innovation, customer adoption, and strategic market expansion
- Identify and cultivate strategic relationships with AI platform companies, LLM providers, cloud hyperscalers, and ISVs building in the agentic AI and GenAI space
- Drive product and solution\-level integrations that embed TiDB into partner platforms and AI stacks
- Build and manage a portfolio of technology partnerships aligned to TiDB's position as the database for agentic workloads that include supporting vectors, transactions, analytics, and elastic scale in a single engine
- Develop joint value propositions and solution blueprints with partners that address real enterprise AI infrastructure challenges
Product \& Solution Collaboration
- Work cross\-functionally with TiDB's Product and Engineering teams to scope and execute technical integrations with partner platforms
- Identify opportunities to embed TiDB natively into AI frameworks, agent orchestration layers, RAG pipelines, and cloud\-native application platforms
- Leverage TiDB's capabilities, including vector search, Graph RAG, knowledge graphs, and unified data management to design compelling joint solutions with partners
- Collaborate with partners to develop reference architectures, joint solution guides, and go\-to\-market assets
Go\-to\-Market \& Ecosystem Activation
- Develop and execute co\-marketing and co\-innovation initiatives to drive awareness of joint TiDB partner solutions in the AI/ML and enterprise data communities
- Enable partners with technical resources, training, and tools to effectively build on and promote TiDB
- Represent TiDB at partner events, AI industry conferences, and developer communities across North America and EMEA
- Engage developer ecosystems, open\-source communities, and platform adoption programs to accelerate ecosystem growth through grassroots technical adoption
Pipeline \& Performance
- Track and manage partnership pipeline, integration milestones, and business impact metrics, including ecosystem\-influenced pipeline, joint customer opportunities, product adoption, and strategic market positioning
- Provide regular updates to leadership on partnership progress, ecosystem health, and strategic opportunities
- Continuously assess the competitive landscape and identify new whitespace opportunities for TiDB in the AI ecosystem
### Qualifications:
- Bachelor's degree in Business, Computer Science, or a related field; technical background a strong plus
- 8\+ years in technology partnerships, business development, or solutions alliances, with a track record of building ecosystem functions in AI, cloud, or data infrastructure
- Deep understanding of the AI/ML ecosystem, including LLM platforms, agentic AI frameworks, vector databases, RAG architectures, and cloud\-native infrastructure
- Proven experience driving product\-level or solution\-level integrations, with ownership of ecosystem strategy that delivers measurable business outcomes
- Ability to engage both technically and commercially with engineers, product managers, and C\-suite stakeholders
- Strong cross\-functional collaboration skills bridging BD with Product, Engineering, and Marketing
- Excellent communication and storytelling skills; able to articulate complex technical value propositions to diverse audiences
- Experience with distributed databases, cloud platforms (AWS, GCP, Azure), AI infrastructure, developer ecosystems, or open\-source communities is a strong plus
*We encourage people from underrepresented groups to apply. Come advance with us! In keeping with our values, no employee or applicant will face discrimination/harassment based on: race, color, ancestry, national origin, religion, age, gender, marital domestic partner status, sexual orientation, gender identity, disability status, or veteran status. TiDB also strives to prevent other, subtler forms of inappropriate behavior (e.g., stereotyping) from ever gaining a foothold in our organization. Whether blatant or hidden, barriers to success have no place at TiDB.*
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 TiDB, 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. Director-level AI roles across all categories have a median of $272,150.
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.
TiDB AI Hiring
TiDB has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 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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