Interested in this AI/ML Engineer role at Datadog?
Apply Now →About This Role
Datadog is expanding the Technical Solutions (TS) organization by seeking a customer\-focused, deeply technical Distinguished Architect to join our Product Solutions Architecture (PSA) team. In this role, you will act as a technical multiplier for the world's leading AI labs and AI\-native companies. You will bridge the gap between their bleeding\-edge infrastructure aspirations and Datadog’s technology roadmap, ensuring our platform natively solves the unique observability challenges of training and deploying foundational models at scale.*At Datadog, we place value in our office culture \- the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work\-life harmony that best fits them.*
What You’ll Do:
- Leadership: Demonstrate thought leadership in the AI/LLM space. Influence key decision makers and stakeholders by connecting technical capabilities to organizational and business impact.
- Advisory: Strategically partner with highly technical Founders, Heads of Infrastructure, and Research Lead peers. Guide them on best practices and emerging industry trends in the AI/LLM space. Lead high\-level technical and architectural conversations around AI adoption.
- Presentations: Lead deep\-dive architecture reviews and design engagements with customer teams and their leaders to share industry trends, best practices, and demonstrate how Datadog can support high\-throughput hyper scale AI workloads.
- GTM: Identify emerging AI\-native technology shifts and feed them directly back to Datadog Product Management. Co\-create custom observability integrations and solutions alongside Product SAs to keep Datadog at the absolute forefront of the AI stack.
- Collaboration: Collaborate with Product Solutions Architecture (PSA), Sales, Sales Engineering and Marketing in providing high\-quality technical resources to a broad audience of practitioners and economic buyers.
- Hiring: Assist leadership in recruiting and hiring of top talent for the Product Solutions Architecture and Field CTO teams.
Who You Are:
- Industry Experience: 10\+ years of experience with at\-scale distributed systems architecture, high\-performance computing, or large\-scale infrastructure. Deep familiarity with the AI/LLM ecosystem, accelerator hardware (GPUs/TPUs), and modern orchestration frameworks.
- Strategic Thinker: Able to think long term and creatively about a wide variety of technical and business challenges
- Stakeholder Management: Proven experience interacting with and influencing elite individual contributors, research scientists, and technical founders in flat, rapid\-growth environments.
- Technologist: A true close\-to\-the\-metal technologist who maintains deep hands\-on credibility and can white\-board architectural solutions seamlessly with senior engineers. Strong understanding of best practices and real world challenges AI/LLM Ops and LLM Observability (LLMO).
- Presenter: Excellent customer\-facing presentation skills for large audiences, comfortable discussing complex technical details as well as with briefing executives or non\-technical personas.
- Communicator: Excellent verbal and written communication skills, ability to link product functionality to business objectives, value realization and ROI.
- Travel: Able to travel via auto, train or air up to 50% of the time
- Education: Bachelor’s degree in engineering or related field, Master’s degree preferred
*Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.*
Benefits and Growth:
- Best\-in\-breed onboarding
- Generous global benefits
- Intra\-departmental mentor and buddy program for in\-house networking
- New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
- Continuous professional development, product training, and career pathing
- An inclusive company culture, able to join our Community Guilds and Inclusion Talks
*Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.*
Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.
The reasonably estimated yearly salary for this role at Datadog is:
$300,000—$484,000 USDAbout Datadog:
Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high\-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about \#DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
Equal Opportunity at Datadog:
Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.
Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.
Privacy and AI Guidelines:
Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.
Requisition ID: R19299
Salary Context
This $300K-$484K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1956 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 3,739 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Datadog, 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 in Demand for This Role
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 $179,000 based on 11,901 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($392K) sits 119% above the category median. Disclosed range: $300K to $484K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Datadog AI Hiring
Datadog has 6 open AI roles right now. They're hiring across Research Scientist, AI Product Manager, AI/ML Engineer. Based in New York, NY, US. Compensation range: $195K - $484K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,739 open positions tracked in our dataset. By seniority: 115 entry-level, 1,764 mid-level, 1,444 senior, and 416 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (597 positions). The remaining 3,119 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,739 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,650), Data Scientist (271), AI Software Engineer (252). 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 (115) are outnumbered by mid-level (1,764) and senior (1,444) 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 416 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (597 positions), with 3,119 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,929 postings), Aws (1,185 postings), Azure (869 postings), Rag (866 postings), Gcp (726 postings), Prompt Engineering (578 postings), Pytorch (575 postings), Claude (547 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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