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About This Role
The Director, AI Engineering \& Delivery leads a multidisciplinary team of machine learning engineers, data scientists, software engineers, and MLOps/DevOps specialists to design, build, deploy, and maintain AI/ML systems. This role bridges technical leadership, people management, and execution readiness, ensuring AI products are innovative, reliable, scalable, maintainable and integrated seamlessly int our global AI product portfolio.
In This Role, Your Responsibilities Will Be:* Team leadership \& management
+ Lead, mentor, and grow a team of AI/ML engineers, software developers, and MLOps engineers.
+ Conduct regular performance evaluations, provide coaching, and oversee career development.
+ Foster a culture of fast paced innovation, technical excellence, collaboration and continuous learning
- Technical architecture and design
+ Oversee the design and architecture of AI/ML systems, AI pipelines, ensuring scalability, security, and alignment Enterprise AI and broader software ecosystems.
+ Collaborate with data engineering to ensure the data infrastructure supports AI/ML applications.
+ Partner with AI Product Owners and Solution Managers to translate business requirements into technical roadmaps.
- AI/ML development \& delivery
+ Manage project timelines, scope, risks, and dependencies across AI engineering initiatives.
+ Oversee and ensure the team follows best practices for model development, versioning, testing, validation, and deployment.
+ Define and enforce engineering standards for code quality, documentation, development tools
+ Communicate progress, challenges, and technical decisions to leadership and stakeholders.
- MLOps \& deployment
+ Ensure scalable, secure, and maintainable infrastructure for AI applications.
+ Establish and champion MLOps best practices, including CI/CD pipelines for ML, automated testing, model registry and monitoring, and versioning.
- Governance, ethics \& compliance
+ Ensure AI systems meet security, privacy, and compliance requirements.
Promote responsible AI practices around fairness, transparency, and auditability.
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For This Role, You Will Need:
- Bachelor’s in computer science, Data Science, Engineering, or related field (or equivalent experience)
- 5\+ years of experience in software engineering, AI/ML engineering, or related domains
- 2\+ years leading technical development or engineering teams
- Proficiency in Python and other coding technology
- Experience with cloud platforms (Azure, AWS, GCP) and/or managed AI/ML services
- Hands\-on experience with DevOps/MLOps (GitHub Actions, Azure ML, AWS Sagemaker, Kubernetes, Docker)
Authorized to work in the United States without sponsorship now and in the future
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Preferred Qualifications That Set You Apart:
- Master’s degree in computer science, Data Science, Engineering, or related field
- Deep technical expertise balanced with practical delivery mindset
- Proficiency in Python and frameworks such as TensorFlow, PyTorch, Scikit\-learn
- Strong problem\-solving, communication, and architectural thinking
- Comfort working in ambiguous environments with emerging technologies
Our Culture \& Commitment to You:Our compensation philosophy is simple: we pay a competitive base salary, within the local market in which we operate, and reward performance during our annual merit review process. The salary range for this role is $202,000\-$252,000 annually, commensurate with the skills, talent, capabilities, and experience each candidate brings to a role.
This position will be open for a minimum of 7 days from the day of posting. Applicants are encouraged to apply early to receive efficient consideration. In compliance with the Colorado Job Application Fairness Act, in any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
At Emerson, we prioritize a workplace where every employee is valued, respected, and empowered to grow. We foster an environment that encourages innovation, collaboration, and diverse perspectives because we know that great ideas come from great teams. Our commitment to ongoing career development and growing an inclusive culture ensures you have the support to thrive. Whether through mentorship, training, or leadership opportunities, we invest in your success so you can make a lasting impact. We believe diverse teams, working together are key to driving growth and delivering business results.
We recognize the importance of employee wellbeing and know that to do your best you must have flexible, competitive benefits plans to meet you and your family’s physical, mental, financial, and social needs. We provide, a variety of medical insurance plans, with dental and vision coverage, Employee Assistance Program, profit sharing retirement, tuition reimbursement, employee resource groups, recognition, and much more. Our culture prioritizes work\-life balance and offers flexible time off plans, including paid parental leave (maternal and paternal), vacation and holiday leave.
Work Authorization:
Emerson will only employ those who are legally authorized to work in the United States. This is not a position for which sponsorship will be provided. Individuals with temporary visas such as E, F\-1 (including those with OPT or CPT), H\-1, H\-2, L\-1, B, J or TN, or who need sponsorship for work authorization now or in the future, are not eligible.
\#LI\-PL1
\#LI\-Hybrid
WHY EMERSON
Our Commitment to Our People
At Emerson, we are motivated by a spirit of collaboration that helps our diverse, multicultural teams across the world drive innovation that makes the world healthier, safer, smarter, and more sustainable. And we want you to join us in our bold aspiration.
We have built an engaged community of inquisitive, dedicated people who thrive knowing they are welcomed, trusted, celebrated, and empowered to solve the world’s most complex problems — for our customers, our communities, and the planet. You’ll contribute to this vital work while further developing your skills through our award\-winning employee development programs. We are a proud corporate citizen in every city where we operate and are committed to our people, our communities, and the world at large. We take this responsibility seriously and strive to make a positive impact through every endeavor.
At Emerson, you’ll see firsthand that our people are at the center of everything we do. So, let’s go. Let’s think differently. Learn, collaborate, and grow. Seek opportunity. Push boundaries. Be empowered to make things better. Speed up to break through. Let’s go, together.
Work Authorization
Emerson will only employ those who are legally authorized to work in the United States. This is not a position for which sponsorship will be provided. Individuals with temporary visas such as E, F\-1(including those with OPT or CPT) , H\-1, H\-2, L\-1, B, J or TN, or who need sponsorship for work authorization now or in the future, are not eligible for hire.
Equal Opportunity Employer
Emerson is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, age, marital status, political affiliation, sexual orientation, gender identity, genetic information, disability or protected veteran status. We are committed to providing a workplace free of any discrimination or harassment.
Accessibility Assistance or Accommodation
If you have a disability and are having difficulty accessing or using this website to apply for a position, please contact: [email protected] .
ABOUT EMERSON
Emerson is a global leader in automation technology and software. Through our deep domain expertise and legacy of flawless execution, Emerson helps customers in critical industries like life sciences, energy, power and renewables, chemical and advanced factory automation operate more sustainably while improving productivity, energy security and reliability.
With global operations and a comprehensive portfolio of software and technology, we are helping companies implement digital transformation to measurably improve their operations, conserve valuable resources and enhance their safety.
We offer equitable opportunities, celebrate diversity, and embrace challenges with confidence that, together, we can make an impact across a broad spectrum of countries and industries. Whether you’re an established professional looking for a career change, an undergraduate student exploring possibilities, or a recent graduate with an advanced degree, you’ll find your chance to make a difference with Emerson. Join our team – let’s go!
No calls or agencies please.
Salary Context
This $202K-$252K 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 Emerson, 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. This role's midpoint ($227K) sits 6% above the category median. Disclosed range: $202K to $252K.
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.
Emerson AI Hiring
Emerson has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Shakopee, MN, US. Compensation range: $252K - $252K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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
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