Interested in this AI/ML Engineer role at Dynatrace?
Apply Now →Skills & Technologies
About This Role
Your role at Dynatrace
--------------------------
The Partner Sales Director – AI Alliances, Infrastructure Partners owns and scales Dynatrace's strategic relationships with the foundational compute layer of the AI\-native ecosystem including NVIDIA, CoreWeave, Lambda Labs, Together.ai, AMD, Intel, Crusoe, and the broader NeoClouds ecosystem. This is a category\-defining, startup role to build the AI native ecosystem for Dynatrace and at the intersection of enterprise observability and AI infrastructure ecosystem.
The Partner Sales Director will build the partnership architecture that positions Dynatrace as the de facto observability platform for GPU\-accelerated AI workloads and AI inference clouds. The successful candidate will own the strategy, onboard partnerships and execute co\-sell motions with enterprise field teams and enable Sell To, With and Through motions, and work with product and field to define the integration and solution roadmap that makes Dynatrace observability native to every GPU and AI inference workload.
This role operates within the AI Native Ecosystem Alliance charter and will collaborate across AI native field sales, Product and Marketing to ensure AI partnerships generate partner\-influenced pipeline and revenue, enterprise customer wins and new logos.
In this role you will:
- Own and develop Dynatrace's strategic alliance with prioritized AI infrastructure partner accounts.
- Build and scale the AI infrastructure partnerships with Sell To, With and Through programs and structure commercial arrangements to embed Dynatrace as the default observability layer in managed GPU platform offerings
- Develop and maintain C\-suite and VP\-level executive relationships, Negotiate and execute formal alliance agreements, MDF co\-marketing budgets, referral frameworks, and joint GTM commitments
- Meet and exceed quarterly co\-sell pipeline, partner\-influenced ACV and manage the co\-sell process with the target partners including deal registrations, joint account maps, and quarterly business reviews (QBRs)
- Work with partner marketing, develop and execute joint GTM campaigns, ecosystem events support and pipeline generation plans.
- Drive joint co\-innovations and solutions roadmap working with product teams
- Build content to deliver sales, partner and channels enablement with clear value proposition and sales plays.
What will help you succeed
------------------------------
Minimum Requirements:
- Bachelor's degree or equivalent professional experience
- 10\+ years of experience in enterprise software sales, technology alliance management, or cloud ecosystem GTM
- 3\+ years in a Director or senior leadership role with direct partner revenue accountability
Preferred Requirements:
- 7\-10 years' experience in software sales, systems integration delivery/business development and alliances management
- A proven track record of developing new partnerships from ground up and crafting clear strategy and execution plans to scale partner ecosystems to deliver multimillion dollar enterprise deals.
- Demonstrated experience building and scaling To, With and Through partner motions: bundled offerings, white\-label programs, or resell structures with technology platform companies
- Strong understanding of GPU AI infrastructure and GPU clouds, and the AI workload economics that drive customer observability needs.
- Strong executive relationships within the NVIDIA partner ecosystem, GPU cloud provider community, or cloud infrastructure organizations
- Experience at or working directly with an AI infrastructure company, GPU cloud provider, or high\-growth enterprise SaaS with a partner\-led go\-to\-market
Why you will love being a Dynatracer
----------------------------------------
- Dynatrace is a leader in unified observability and security.
- We provide a culture of excellence with competitive compensation packages designed to recognize and reward performance.
- Our employees work with the largest cloud providers, including AWS, Microsoft, and Google Cloud, and other leading partners worldwide to create strategic alliances.
- You'll get to work at the forefront of innovation with Dynatrace Intelligence—the industry's first agentic operations system. Bringing together deterministic and agentic AI, it helps teams understand what's happening, why it matters, and what to do next— automatically.
- Over 50% of the Fortune 100 companies are current customers of Dynatrace.
Compensation and Rewards
----------------------------
At Dynatrace, total compensation for this role is made up of a strong base salary, a lucrative variable commission structure, and equity.
- Base Salary Range: $192,000 \- $240,000\. Your exact offer will be based on your unique experience, skill set, education, and geographic location.
- Variable Pay: Additional commission tied directly to your success and performance targets.
Beyond your cash compensation, Dynatrace offers a comprehensive rewards package designed to support your life, well\-being, and long\-term wealth, including:
- Equity \& Financial Future: Eligibility for Restricted Stock Units (RSUs), an Employee Stock Purchase Plan (ESPP), and a company\-matched 401(k) program.
- Time to Recharge: Unlimited personal time off (PTO) and 4 Dynatrace Wellness Days a year.
- Health \& Wellness: Premium medical and dental benefits.
Equal Employment Opportunity
--------------------------------
All your information will be kept confidential according to EEO guidelines.
We offer competitive compensation, company\-sponsored premium benefits, medical, dental, vacation/holidays, company matching 401(k) Plan, etc. Dynatrace is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, sex, color, gender identity, religion, national origin, ancestry, citizenship, physical abilities, age, sexual orientation, creed, disability status, veteran status, pregnancy, genetic status, or any other characteristic protected by law. If your disability makes it difficult for you to use this site, please contact [email protected] . Dynatrace participates in E\-Verify, participant information in English and Spanish. Right to work information in English and Spanish. EEO is the Law . To be considered for this position, please upload your resume/CV.
Note to Recruiters and Agencies : Thank you for your interest in Dynatrace. Please note that we do not accept unsolicited agency resumes —do not forward them via our website or directly to Dynatrace employees. Dynatrace will not pay fees for unsolicited resumes, and any resumes received this way will be considered the property of Dynatrace.
Benefits and work\-life perks
---------------------------------
We offer best\-in\-class core rewards, including paid time off, financial security benefits, retirement savings plans, and health insurance. Beyond that, you’ll get other benefits and work\-life perks designed to make your ride with us even more rewarding.
#### Mental health support
Our Employee Assistance Program, powered by Telus Health, offers support for you and your family members.
#### Wellness Days
Four company\-designated extra paid days off for you to recharge batteries.
#### Flexibility
Our hybrid working model and flexible working hours offer you the flexibility you need.
#### Employee Stock Purchase Plan
Purchase company stock ( NYSE:DT ) at a discounted price and become a shareholder.
#### Learn \& develop
Company\-wide learning perks, designated team's learning days, and more.
#### Volunteering day
A day of paid volunteer time to support a community or cause you care about.
#### Regular team events
We host Global Culture Parties, Family \& Friends at Work Day, Global Breakfasts, Green Weeks, Pride Month, and beyond!
#### International vibe
Most of our offices and teams are proudly multicultural. English is our shared language, but we embrace and learn from each other's cultures.
Rewards vary depending on your employment type. Some benefits and perks also differ by location — explore your city to see what’s available there.
About Dynatrace
-------------------
Dynatrace (NYSE: DT) is the leading AI\-powered observability and security platform. We're advancing observability for today's digital businesses, helping transform modern digital ecosystems' complexity into powerful business assets.
Our AI\-driven insights cut through the noise, allowing customers to focus on what truly matters by automating manual tasks and resolving issues with pinpoint accuracy. Dynatrace offers simplicity, clarity, and reliability at scale to ensure teams can make informed decisions, minimize downtime, and drive their business forward with confidence.
Salary Context
This $192K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Dynatrace, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $192K to $240K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Dynatrace AI Hiring
Dynatrace has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Boston, MA, US, San Jose, CA, US. Compensation range: $150K - $240K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.