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About This Role
Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast\-paced environment of a true industry disruptor, you'll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world.
We are seeking a Senior Director of Engineering to lead DigitalOcean's Managed PostgreSQL organization. This leader will own the strategy, architecture, reliability, scalability and long\-term evolution of our PostgreSQL offerings and the next generation capabilities that enable developers to build highly scalable, data\-intensive, and AI\-native applications.
The ideal candidate will merge sophisticated product vision with technical expertise in distributed systems and the rigorous operational standards required for database management. Your mandate is simple: make DigitalOcean Managed PostgreSQL trusted for mission\-critical workloads today, and essential for AI\-native builders tomorrow.
What You'll Do
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### Own Managed PostgreSQL end\-to\-end
Lead the organization responsible for all PostgreSQL editions, overseeing availability, scalability, security, and long\-term architecture.
### Build hyperscaler\-grade reliability and operational excellence
Establish tier\-0 reliability standards. Execute against SLIs/SLOs for failover, backups, and query performance to ensure a high bar for operational excellence.
### Scale Advanced Edition into a production\-grade platform
Evolve the Advanced Edition into a differentiated offering featuring HA architecture, consensus\-based failover, and fast restore capabilities.
### Serve large and demanding database customers
Support large\-scale workloads (up to 64 TB) with storage scaling, read\-only nodes, and query insights to meet demanding customer needs.
### Define the AI\-native PostgreSQL strategy
Integrate PostgreSQL with AI workflows via pgvector, metadata stores, and agent memory, positioning it as the core substrate for AI builders.
### Build and scale a high\-performing engineering organization
Build a high\-performing global organization with strong accountability and technical review mechanisms.
### Partner deeply across Product, GTM, Support, SRE, Infrastructure, and AI/Data teams
Translate customer needs, competitive gaps, and market shifts into a coherent engineering roadmap. Work closely with Product and GTM to define packaging, migration motions, enterprise readiness, roadmap sequencing, launch quality, and customer adoption.
### Raise the technical bar
Be credible in architecture discussions with principal engineers and senior database experts. The role requires enough depth to challenge designs, evaluate tradeoffs, simplify complexity, and prevent the organization from accumulating reliability debt under the cover of roadmap velocity.
What Success Looks Like
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Key outcomes within one year:
- PostgreSQL operates as a tier\-0 service with measurable reliability gains and improved incident response.
- Advanced Edition has a credible path from preview/early adoption to production\-grade readiness for demanding workloads.
- Large customer workloads have clear reference architectures, migration paths, operational playbooks, and escalation mechanisms.
- PostgreSQL has a differentiated AI\-native roadmap, in addition to pgvector.
- Advanced Edition is production\-ready and the technical strategy scales to 64 TB.
- DigitalOcean competes effectively against hyperscalers on price, reliability, and developer experience.
What You'll Bring
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### Required Experience
- 15\+ years of engineering experience, including significant leadership of cloud infrastructure, database, storage, distributed systems, or data platform organizations.
- 6\+ years managing managers and leading multi\-team engineering organizations.
- Proven experience operating customer\-facing, high\-availability production services with strict reliability, durability, and incident\-management expectations.
- Strong distributed systems foundation: replication, consensus, failure detection, failover, consistency, partitioning, control planes, fleet automation, and multi\-tenant operations.
- Experience with database systems, managed database services, storage engines, query performance, backup/restore, high availability, or large\-scale stateful infrastructure.
- Demonstrated ability to define multi\-quarter technical strategy and convert it into shipped customer value.
- Strong customer judgment: ability to understand enterprise\-grade workload requirements and separate real customer needs from noisy feature requests.
- Executive communication skills; able to represent the business and technical strategy to senior leadership, customers, partners, and GTM teams.
### Preferred Experience
- Deep PostgreSQL experience, especially operating PostgreSQL at scale
- Experience with RDS/Aurora, AlloyDB, Cloud SQL, Azure Database for PostgreSQL, Neon, Crunchy, EDB, Yugabyte, Citus, Timescale, Supabase, or similar PostgreSQL ecosystems.
- Experience building or operating HA architectures using proxies, connection poolers, consensus systems, automated primary election, online upgrades, and backup/restore pipelines.
- Familiarity with pgvector, pgvectorscale, hybrid search, RAG architectures, AI application data patterns, and agent memory/state systems.
- Experience scaling databases beyond single\-node limits through read scaling, sharding, distributed SQL, storage/compute separation, or workload\-aware architecture.
- Experience partnering with product and GTM teams to turn platform capabilities into compelling commercial offerings.
- Open\-source credibility or strong familiarity with the PostgreSQL ecosystem.
Key Metrics
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This leader will be accountable for metrics such as:
- Availability, failover success rate, RTO/RPO, backup success rate, restore time, and data durability.
- Fleet health, incident rate, change failure rate, mean time to detect, and mean time to recover.
- Query performance, replication lag, connection stability, noisy\-neighbor isolation, and scaling workflow success.
- Advanced Edition adoption, migration success, revenue growth, attach rate, and retention.
- Large customer onboarding success and reduction in high\-severity escalations.
- Engineering execution velocity, hiring quality, retention, and succession depth.
Why This Role Matters
-------------------------
PostgreSQL is no longer just the default relational database for web applications. For AI\-native companies, it is becoming the operational system of record, the metadata layer, the agent state store, the retrieval companion, and often the first place developers want to add vector and AI\-aware capabilities.
DigitalOcean has a right to win here only if it is honest about the bar. Developers will forgive missing features before they forgive data loss, unreliable failover, poor observability, or painful scaling. This leader's job is to make Managed PostgreSQL boringly reliable at the core and strategically ambitious at the edge.
Compensation Range:
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- $261,600\.00 \- $327,000\.00
- This is a hybrid role
JR: 2026\-7844
*\#LI\-Hybrid*
Why You'll Like Working for DigitalOcean
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- We innovate with purpose. You'll be a part of a cutting\-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
- We prioritize career development. At DO, you'll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high\-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000\+ courses to support their continued growth and development.
- We care about your well\-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
- We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
- DigitalOcean is an equal\-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Application Limit: You may apply to a maximum of 3 positions within any 180\-day period. This policy promotes better role\-candidate matching and encourages thoughtful applications where your qualifications align most strongly.
Salary Context
This $261K-$327K 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 DigitalOcean, 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. This role's midpoint ($294K) sits 35% above the category median. Disclosed range: $261K to $327K.
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.
DigitalOcean AI Hiring
DigitalOcean has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $327K - $327K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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.
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