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DigitalOcean

Runs cloud infrastructure for apps and AI

Senior Product Manager 2 - Managed Databases, MySQL

Full-Time
No salary listed
Senior
Bachelor's, Master's
Bengaluru, Karnataka, India
HybridHybrid role based in Bengaluru; some on-site days required.
ProductTechnical Product Management

About the job

Requirements
  • 5+ years of product management experience specializing in cloud database products, with direct experience owning an open-source database offering (MySQL strongly preferred)
  • Deep technical knowledge of relational databases and their applications in traditional transactional workloads and modern AI/ML applications
  • Proven track record of successfully taking database products and features from concept through large-scale deployment and ongoing optimization
  • A strong sense of ownership and a drive to identify and resolve whatever is preventing you and your team from delivering value to customers
  • Exceptional analytical and problem-solving skills, with a demonstrated ability to make and communicate strategic, data-driven decisions, especially related to capacity, scaling, and pricing
  • Strong leadership and cross-functional communication skills, capable of driving consensus and inspiring collaboration across technical and business teams
  • Customer-focused mindset, with direct experience gathering customer insights and translating them into meaningful product improvements
  • A Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field — or equivalent practical experience
  • This job is located in Bengaluru, India
Responsibilities
  • Own and drive the product roadmap for DigitalOcean's Managed Databases (MySQL), including provisioning workflows, deployment experience, reliability, performance, and pricing — in close alignment with broader DigitalOcean objectives
  • Conduct comprehensive market analysis on database trends, competitive offerings, pricing, and emerging database technologies to inform product direction and positioning
  • Collaborate directly with Engineering and Infrastructure teams to conceptualize, develop, and deliver managed database solutions required for traditional OLTP and modern AI applications
  • Serve as the advocate for customer needs, actively collecting and prioritizing feedback to continually improve the provisioning, scaling, and day-two operations experience
  • Partner with Capacity Planning, Solutions Architecture, Go-to-Market, and Customer Success teams to ensure successful, scalable managed database product launches and full lifecycle management
  • Use data-driven approaches to evaluate product success, track key metrics around utilization, reliability, and deployment velocity, and guide strategic product decisions

About the company

DigitalOcean provides cloud infrastructure for developers and businesses to build, host and scale applications and AI services. Teams rent virtual machines called Droplets, store data and run managed databases or Kubernetes clusters through a web console and APIs. Its AI services supply GPU computing, model inference and managed environments where software agents use tools and retain information between sessions. Customers pay for the resources they use, with subscriptions and managed services helping teams run applications without maintaining all the underlying infrastructure themselves. DigitalOcean was founded in 2012 and is headquartered in Broomfield, Colorado.

Company Size

1,001-5,000

Company Stage

IPO

Headquarters

Broomfield, Colorado

Founded

2012

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Simplify Jobs

Simplify's Take

What believers are saying

  • Revenue is accelerating: DigitalOcean's August 2026 report showed Q2 revenue of $281 million, up 29% yearly.
  • AI demand is scaling: DigitalOcean's August 2026 report put AI Customer ARR at $234 million, up 212% yearly.
  • Learning receives funding: DigitalOcean's US rewards page offers education reimbursement and one professional development book per month.

What critics are saying

  • Expansion uses borrowing: DigitalOcean secured a $725 million equipment financing facility in September 2026, maturing in 2030.
  • Cloud competition is broad: DigitalOcean's annual filing names AWS, Azure and Google Cloud alongside smaller providers targeting similar customers.
  • Infrastructure work extends after hours: DigitalOcean's live Wenatchee data center manager role requires onsite work and on-call escalations.

What makes DigitalOcean unique

  • Entry costs are bundled: DigitalOcean's $4 monthly Basic Droplet includes 500 GiB of outbound transfer.
  • Agent spending gets discounts: DigitalOcean's October 2026 preview subscriptions apply 15% or 20% discounts until the monthly balance runs out.
  • Agent sessions are isolated: DigitalOcean's September 2026 Managed Agents preview gives each session its own runtime and governed tools.

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Benefits

Remote-first

Full health coverage

Wellness coverage

Flexible vacation time

Team-building & social events

401(k) plans

ESPP

Education support

Partner support

Employee giving

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

1%

2 year growth

0%
Business Wire
Oct 13th, 2026
Newsroom | Business Wire

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Business Wire
Oct 13th, 2026
Newsroom | Business Wire

The global leader in press release distribution and regulatory disclosure. Public relations and investor relations professionals rely on Business Wire for broad-based and targeted market reach.

Associated Press
Oct 12th, 2026
DigitalOcean launches learning and data services for self-improving AI agents

DigitalOcean (NYSE: DOCN) has launched Agent Learning Services and Agent Data Services, which let companies build AI agents that improve themselves. The cloud provider announced the products ahead of its Open Intelligence Summit in San Francisco. Learning Services record what an agent does, flag where it fails, and test fixes before customers see them. Data Services give agents one managed place to read context and write back what they learn. The enhanced Inference Router already applies this loop to models, picking the best-scoring one for each task. DigitalOcean plans to extend it to agent workflows. Learning Services are in public preview. Data Services and the Inference Router optimizer are in private preview.

Trade Show News
Oct 12th, 2026
DigitalOcean launches Agent Learning and Data Services for recursively self-improving AI agents.

DigitalOcean launches Agent Learning and Data Services for recursively self-improving AI agents. DigitalOcean Agent Learning Services turn agent interactions into better business outcomes, powered by DigitalOcean Agent Data Services, a fully managed data layer built for agents SAN FRANCISCO-(BUSINESS WIRE)-Ahead of its Open Intelligence Summit in San Francisco, DigitalOcean (NYSE: DOCN), the AI-Native Cloud, today announced Agent Learning and Data Services as another key element of its modern agentic stack, so teams can launch self-improving agents. Together with Inference Engine and Managed Agents, DigitalOcean is one of the few clouds where agents can run, learn, and improve on one platform. * DigitalOcean Agent Learning Services: three products that work as one loop. Insights records what an agent does, Signals flags where it falls short, Evaluations & Simulations tests a change before customers see it. The evidence they produce is what teams use to optimize an agent. * DigitalOcean Agent Data Services: feeds the loop, giving agents one managed place to read the context they need and write back what they learn. "The question has changed from 'can we launch an agent' to 'does our agent get better with every run," said Vinay Kumar, Chief Product and Technology Officer, DigitalOcean. "We built Inference Engine so agents could think, Managed Agents so they could act, and now Agent Learning Services and Agent Data Services so they can improve, on one open cloud and on the customer's own data. That is what owning your intelligence means, and it is why DigitalOcean is the place to launch and scale self-improving agents." Once an agent is deployed, it plateaus. Making it better is hard because every piece of the job lives somewhere else. The record of what the agent did sits in a monitoring tool that shows whether systems are up, not why the agent called the wrong tool or lost a user. A fix is usually a prompt tweak with no way to test it, so the first to notice a regression is a customer. Improving the model means exporting production data to a training vendor and wiring together four or five tools, so it happens once a quarter or not at all. DigitalOcean Agent Learning Services: Improve agents with evidence Agent Learning Services give every team that loop in one place. Insights records every step an agent takes, and Signals flags the sessions that matter: where the agent stalled, looped, lost the user, or failed. That evidence becomes a proposed change. Evaluations and Simulations test changes ahead of real users with realistic conversations built from flagged sessions. Each change feeds the next stage of the loop. Today, this recursive self-improvement is available for models: the newly enhanced Inference Router picks the best-scoring model for each task, optimized for cost, quality, or the team's own evaluations, and re-runs those evaluations as new models ship. Next, DigitalOcean plans to bring the same automated loop to agent workflows, augmenting the current human approvals. DigitalOcean Agent Data Services: The data foundation for agents Agent Data Services is the data layer the loop runs on, and the one agents work from. Every piece of a company's data becomes agent-ready the moment it arrives. Video is transcribed and described, documents are read and indexed, logs and agent sessions are assembled with configurable redaction controls for sensitive details, and all of it is searchable by meaning in a single call. It lands in one catalog, designed to be logically isolated to that customer, with governance designed so that only authorized people and agents can reach each piece of data. Agent Learning Services are built on it: Signals writes sessions into it, and Optimizer draws its training and test data from it only where the customer expressly opts in. The same platform serves far more than the learning loop. Teams can use it for search and analytics across video, audio, documents, and structured data, and for grounding any agent in the company's own records. "Amplitude helps organizations deliver more impact when building with AI," said Spenser Skates, Co-founder and CEO of Amplitude. "Amplitude Wave is an agentic product team for building self-improving products. It analyzes product data, customer feedback, and code to recommend changes, ship experiments, and measure the real-world results. With DigitalOcean, the models and the agent infrastructure are on one platform. We choose the model for each task based on the results it delivers, not just its cost. DigitalOcean Managed Agents powers Wave and handles the agent infrastructure as we scale, so our engineers can focus on helping customers build better products and get more value out of their AI spend." Availability About DigitalOcean DigitalOcean (NYSE: DOCN) is the AI-Native Cloud, purpose-built for the inference and agentic era. One integrated platform spans GPU and CPU infrastructure, core cloud, inference, data, learning, and managed agents. It is open throughout so builders can start fast, scale production AI workloads, and improve unit economics. More than 680,000 customers and millions of developers globally trust DigitalOcean to build, ship, and scale their applications. Learn more at digitalocean.com. Forward-looking statements. This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 regarding product capabilities, pricing, and availability, including plans to extend the learning loop to agent workflows, identified by words such as "expects," "plans," and "will". Insights, Signals, and Evaluations & Simulations are offered as public previews, and Inference Router optimizer and Agent Data Services are offered as private previews; preview services are provided "as is", are not supported for production workloads, and may change, never become generally available, or be discontinued. Actual results may differ materially due to risks and uncertainties. Additional information on risks is included in DigitalOcean's filings with the SEC, including its most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q.

MarketScreener
Oct 1st, 2026
Digitalocean introduces Agent Droplets: everything an ai agent needs.

Digitalocean introduces Agent Droplets: everything an ai agent needs. Published on 10/01/2026 at 02:21 pm EDT S&P Capital IQ DigitalOcean Holdings, Inc. announced Agent Droplets giving companies a simple way to start their agentic journey on DigitalOcean and scale with predictable spending controls. DigitalOcean Managed Agents, launched in public preview on September 22, gave builders a managed runtime, governed access to 16,000+ tools, Serverless Inference, and persistent agent memory and storage on one cloud. Agent Droplets bundle those capabilities into a single monthly subscription simplifying adoption of emerging agent technologies without the guesswork of assembling a stack. Agent Droplets come in two sizes, Pro at $50 a month and Team at $200 a month, with discounts of 15% and 20%, respectively, across all included resources. AI coding subscriptions often cap usage and charge per seat. DIY agent stacks require multiple vendors, multiple invoices, dozens of pricing units, and a pile of glue code to hold them together. Both approaches are complex, and their costs are hard to predict. Agent Droplets bring essential services together in one plan, simplifying adoption and making spend easier to predict. An AI builder on Pro or a startup team on Team pays one monthly subscription and runs as many agents as the work needs, with resource usage covered by the plan?s allowance. Customers choose whether spending stops at the plan price or continues at list prices once the allowance is used. This gives teams a defined starting budget and an explicit choice about additional spend as workloads grow. As companies move from a first agent to production workloads, DigitalOcean provides elastic scale across the agentic primitives their applications depend on: isolated execution, Serverless Inference, persistent memory, session storage, and governed tool access. Agent Droplets simplify how teams adopt and pay for these services, while the underlying platform lets resource consumption grow with workload demand, within their chosen spending controls. Companies can start small and scale on the same cloud. Developers choose an Agent Droplet size to access a bundle of essential agent services, reducing the work of selecting, connecting, and managing each technology separately. The relevant discount applies automatically to the included Managed Agents resources their agents use: Harness Runtime: dedicated microVM sandboxes that start in about a second, resume from a pause in about 300 milliseconds (in DigitalOcean testing), and pause automatically when idle, with memory and session storage included in the discount. Use them to scale agent execution as workloads grow or run arbitrary code generated by an agent. Inference Engine: DigitalOcean-hosted open models including Kimi K3 and GLM 5.3. Frontier models such as Claude and GPT remain available on a pay-as-you-go basis at list price. Action Gateway: governed access to more than 16,000 tools, including code interpreter, browser automation, web search, and web fetch, with credentials brokered outside the agent. Also included: unlimited agents, no seat charges, observability, and enterprise security (single sign-on, multi-factor authentication, role-based access, audit logs, cloud firewalls, and DDoS protection) on every size. New customers can start with a free trial: $5 in credit, no card required, enough to take an idea from zero to running this afternoon. To scale, Agent Droplet Pro and Team apply a bigger discount to every hour, token, and gigabyte included in the plan, 15% off on Pro and 20% off on Team. DigitalOcean also announced Inference and Agents Balance to make it easier for customers to prepay for their agents. Inference and Agents Balance is a prepaid balance, funded in amounts from $5 to $500, that only Serverless Inference, Action Gateway, and Managed Agents can use, while the rest of the cloud keeps billing as usual. Fund your Inference and Agents Balance directly, and your agents draw on it automatically once your Agent Droplet allowance is used up. Agent Droplets and Inference and Agents Balance are available in all regions where DigitalOcean Managed Agents is offered. Developers can create an Agent Droplet or add to their Inference and Agents Balance in the DigitalOcean Cloud Console, and agents already running on Managed Agents move onto an Agent Droplet without changes. (C) S&P Capital IQ - 2026