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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 want people who are passionate about solving complex cloud infrastructure challenges for our customers.
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We are looking for a Solutions Architect with expertise in cloud infrastructure to support the success and retention of DigitalOcean's high\-value customers. Reporting to the Manager of Solutions Architecture,you will work directly with Customer Success, Sales, and Support functions to retain and grow DigitalOcean's largest and fastest growing customers. You will be the technical subject matter expert on DigitalOcean's portfolio, advising on best practices and guiding customers to the optimal solution to meet their business objectives. You will work closely with other functions within DigitalOcean such as Product, Engineering, and Operations to ensure the needs and insights from the local markets are funneled appropriately back into DigitalOcean's products and services.
This is an amazing growth opportunity for a highly motivated individual to establish and expand our presence within the region. Technical depth, excellent communication skills, and a self\-starter mentality are needed.
What You Will Be Doing:
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As a Senior Solutions Architect II, you will serve as a technical partner to the success and sales teams. You will perform architecture reviews, proof of concepts, demos, uncover customer technical needs, and demonstrate DigitalOcean's value proposition. Working with AEs, GAM and TAM counterparts, you'll help retain large customers and expand their workloads on DigitalOcean.
- Develop deep expertise in the DigitalOcean product portfolio, the evolving cloud landscape, and AI/ML platforms.
- Advocate for customers by providing feedback to product teams, overcoming adoption blockers, and driving new feature development.
- Offer personalized onboarding assistance and guide customers through their cloud journey, resolving technical blockers.
- Design and review architecture tailored to specific business use cases, ensuring optimal solutions for customers.
- Implement and oversee infrastructure management best practices to secure and optimize cloud resources.
- Identify opportunities for cost reduction and performance improvement, helping customers make data\-driven cloud optimization decisions.
- Conduct technical consultation sessions, workshops, and training for customers to empower them in managing cloud infrastructure.
- Partner with Growth Account Managers and Customer Success Managers to create success plans that ensure customer retention, expansion, and satisfaction.
- Provide regular presentations articulating product benefits, functionality, and technical solutions.
- Demonstrate strong expertise in AI/ML frameworks like TensorFlow and PyTorch and usage of platforms like Hugging Face, with experience deploying and fine\-tuning LLMs and GenAI models.
- Optimize GPU workloads using CUDA or TensorRT and build scalable AI applications like chatbots, inference services or recommendation systems using Kubernetes and NFS.
- Design AI solutions tailored to business needs, manage data pipelines, and integrate databases.
- Liaise with Engineering and Support teams to resolve escalations and growth obstacles in a timely fashion.
- Identify and drive process improvement opportunities and promote technical best practices across the organization.
- Contribute to internal and external documentation, such as "The Navigator's Guide to DO."
- Build tools to enhance the experience of Premium Support customers and improve platform usability.
- Demonstrate proficiency in DevOps tools like Docker, Terraform, and CI/CD pipelines.
- Leverage expertise in Kubernetes, Managed DBaaS, and the DigitalOcean GenAI Platform.
- Work both independently and collaboratively with a global team of Solutions Architects, Customer Success Managers, and Account Managers.
What We'll Expect From You:
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- Proven professional experience with cloud infrastructure, AI/ML platforms, or equivalent education.
- Expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch) and familiarity with platforms like Hugging Face.
- Experience deploying and fine\-tuning LLMs (e.g., DeepSeek, Llama, Claude, GPT\-4\) and GenAI models.
- Deep knowledge of Linux, distributed systems, Kubernetes, NFS, Object Storage, and GPU optimization techniques (e.g., CUDA, TensorRT).
- Hands\-on experience leveraging vllm and various quantization methods (e.g., INT4, INT8, and FP8\) for efficient model deployment.
- Programming/development experience, particularly in building AI\-powered applications (e.g., recommendation systems, chatbots).
- Experience working in a post\-sales or technical consultant role with a focus on AI/ML and cloud solutions.
- Knowledge of provisioning, deployment strategies, and tools like Terraform, Kubernetes, and Docker.
- Passionate about customer experience and leveraging AI/ML to solve complex business problems.
- Track record of developing successful technical solutions, including AI/ML use cases, to address customer challenges.
- Ability to balance the demands of multiple stakeholders, define priorities, and set appropriate expectations.
- Strong understanding of AI/ML data pipelines, cloud\-native databases, and integration strategies.
- Passionate about technology, open source projects, and applying cutting\-edge AI/ML innovations.
- Strong communication skills, with the ability to explain technical AI/ML concepts in clear and concise terms.
- Quickly learn DigitalOcean systems and adapt to rapid changes in cloud and AI/ML trends.
- Fluency in English.
- Highly motivated with a self\-starter mentality, ready to tackle AI/ML challenges head\-on.
Extra Credit:
- Cloud certifications are highly desirable (AWS/GCP/Oracle/Azure)
- NVIDIA Certifications around GPU and AI/ML
- Networking: Cisco/Juniper Certifications
- Programming/Scripting: Ruby, Python, Go, Bash
- Source Code: Git
- Automation: Terraform, Ansible, Chef, Puppet, Saltstack
- Virtualization: KVM, Xen
- Databases: MongoDB, MySQL, Redis, PostgreSQL
- Open Source: CoreOS, Docker, Kubernetes (CKA/CKE), Vagrant
- DigitalOcean: API, libraries, services
- Linux: RHCSA/RHCE
Compensation Range:
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- Base: $150,000 \- $182,000 / Target OTE: $200,000 \- 240,000
- This is a remote role
JR: 2026\-7695
*\#LI\-Remote*
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 $150K-$182K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $150K to $182K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
DigitalOcean AI Hiring
DigitalOcean has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Austin, TX, US, Boston, MA, US. Compensation range: $182K - $182K.
Location Context
AI roles in Denver pay a median of $198,000 across 169 tracked positions. That's 8% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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