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About This Role
At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet.
Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple \- to deliver a sustainable future for the world through science and collaboration. If you’re looking for a challenge and meaningful role, you’re in the right place.
About you and this role
Dow has an exciting and challenging opportunity for a Machine Learning Engineer within our Enterprise Data \& Analytics organization located in Houston, TX; Midland, MI; or Champaign, IL.
As a Machine Learning Engineer in our Data \& Analytics Platforms team specializing in data and analytics solutions, you will work with a cross\-functional team whose objective is to deliver solutions that drive business value for Dow. In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and inference pipelines on the Azure Databricks platform. You will also help drive a culture around setting standards and adopting best practices for machine learning and MLOps across the organization.
Responsibilities
- Designs and implements pipelines and other workflow infrastructure to meet the requirements for new AI/ML solutions that involve online, batch, or real\-time inference
- Deploys and monitors machine learning models in production using Databricks Model Registry, Jobs, and Workspace
- Frequently collaborates with data engineers, DevOps/platform engineers, data scientists, and domain experts as part of a comprehensive MLOps framework to ensure AI/ML solutions are performant, reliable, and maintainable
- Works frequently with application development teams to ensure seamless integrations
- Works proficiently with various ML frameworks, such as scikit\-learn, TensorFlow, PyTorch, Keras, as well as distributed frameworks like Spark MLlib and Ray
- Performs data analysis, feature engineering, model selection, hyperparameter optimization, and model evaluation using Databricks MLFlow, Delta Lake, and SQL Analytics and other tools as part of the end\-to\-end ML lifecycle
- Researches and implements new machine learning techniques and methods using Databricks, staying abreast of the latest trends and technologies
- Documents and communicates machine learning results and insights to stakeholders using Databricks notebooks and dashboards
- Understands IT security policies and implements them as part of new solution designs
- Follows and promotes the best practices and standards for machine learning and MLOps across the organization using Databricks and Azure DevOps
Your Skills
- Solutions Delivery: End\-to\-end ownership of Azure data solutions—translating ambiguous business needs into robust architectural blueprints, then delivering through design, build, test, deployment, and post‑launch monitoring with a focus on reliability, performance, and cost efficiency.
- Cloud Computing: Designing and operating cloud‑native architectures on Azure (e.g., Databricks Lakehouse, ADF/Workflows, Functions, Logic Apps, Azure SQL) with CI/CD and IaC to scale securely and economically for ML, BI, streaming, and web applications.
- Integration Services: Building secure, high‑throughput integrations—REST APIs and event/stream pipelines (e.g., Event Hubs/Kafka)—to connect polyglot data stores (SQL Server, Cosmos DB, Neo4j) and enable real‑time and batch data products.
- Security Awareness: Embedding governance, identity, and compliance into the architecture (OAuth/RBAC, data governance, re‑authorization/ownership verification), enforcing code reviews and automated controls across pipelines and deployments.
- Strategic Planning: Aligning platform roadmaps and technology choices with enterprise strategy, mentoring engineers on best practices, defining standards and KPIs, and prioritizing modernization and cost‑optimization initiatives that maximize business impact.
Qualifications
- A minimum of a Bachelor's degree, or 8 years relevant experience, or relevant military experience at an E6 rank/Petty Officer 2nd Class or higher is required
- A minimum of 3 years of experience developing solutions in machine learning, data science, or related field
- A minimum requirement for this U.S. based position is the ability to work legally in the United States. No visa sponsorship/support is available for this position, including for any type of U.S. permanent residency (green card) process
Preferred qualifications
- A degree in computer science, engineering, mathematics, statistics, data science or related field
- Proficient in Python and one or more machine learning frameworks such as TensorFlow, PyTorch, Scikit\-learn, etc
- Experience in developing and deploying machine learning models and pipelines on the Databricks platform using Databricks MLflow, Delta Lake, SQL Analytics, Model Registry, Jobs, and Workspace
- Strong knowledge of machine learning concepts, techniques, and algorithms
- Ability to perform data analysis, feature engineering, model selection, optimization, and evaluation using Databricks
- Ability to communicate complex machine learning concepts and results to technical and non\-technical audiences using Databricks notebooks and dashboards
- Ability to work independently and collaboratively in a fast\-paced and dynamic environment
- Curiosity and passion for learning new machine learning skills and technologies using Databricks
- Strong knowledge of data modeling, data warehousing, and ETL processes
- Experience designing and deploying into production both traditional and generative AI systems
- Proficiency in SQL and experience with big data technologies such as Apache Spark and Hive
- Experience working within Azure Machine Learning
- Experience containerizing and deploying ML models to Azure Kubernetes Service
- Experience with Azure Data Factory, Azure Data Lake Storage Gen2, and other Azure services
- Multi\-application and cross\-platform design experience
- Understanding of data lakehouse platform design and associated workflows
- Ability to thrive in challenging situations and solve complex problems
- Ability to manage own work effort in multiple projects with little supervision
- Interested in emerging technologies with the ability to quickly learn and exploit cutting edge offerings to achieve business objectives
Additional notes
- This position does not offer relocation assistance
- This position does not have people leadership responsibility. This position is an Independent Contributor; however, you may act as coach and mentor to junior resources
Benefits – What Dow offers you
We invest in you.
Dow invests in total rewards programs to help you manage all aspects of you: your pay, your health, your life, your future, and your career. You bring your background, talent, and perspective to work every day. Dow rewards that commitment by investing in your total wellbeing.
Here are just a few highlights of what you would be offered as a Dow employee:
- Equitable and market\-competitive base pay and bonus opportunity across our global markets, along with locally relevant incentives.
- Benefits and programs to support your physical, mental, financial, and social well\-being, to help you get the care you need...when you need it.
- Competitive retirement program that may include company\-provided benefits, savings opportunities, financial planning, and educational resources to help you achieve your long term financial\-goals.
+ Employee stock purchase programs (availability varies depending on location).
- Student Debt Retirement Savings Match Program (U.S. only).
+ Dow will take the value of monthly student debt payments and apply them as if they are contributions to the Employees’ Savings Plan (401(k)), helping employees reach the Company match.
- Robust medical and life insurance packages that offer a variety of coverage options to meet your individual needs. Travel insurance is also available in certain countries/locations.
- Opportunities to learn and grow through training and mentoring, work experiences, community involvement and team building.
- Workplace culture empowering role\-based flexibility to maximize personal productivity and balance personal needs.
- Competitive yearly vacation allowance.
- Paid time off for new parents (birthing and non\-birthing, including adoptive and foster parents).
- Paid time off to care for family members who are sick or injured.
- Paid time off to support volunteering and Employee Resource Group’s (ERG) participation.
- Wellbeing Portal for all Dow employees, our one\-stop shop to promote wellbeing, empowering employees to take ownership of their entire wellbeing journey.
- On\-site fitness facilities to help stay healthy and active (availability varies depending on location).
- Employee discounts for online shopping, cinema tickets, gym memberships and more.
- Additionally, some of our locations might offer:
+ Transportation allowance (availability varies depending on location)
+ Meal subsidiaries/vouchers (availability varies depending on location)
+ Carbon\-neutral transportation incentives e.g. bike to work (availability varies depending on location)
Join our team, we can make a difference together.
About Dow
Dow (NYSE: DOW) is one of the world’s leading materials science companies, serving customers in high\-growth markets such as packaging, infrastructure, mobility and consumer applications. Our global breadth, asset integration and scale, focused innovation, leading business positions and commitment to sustainability enable us to achieve profitable growth and help deliver a sustainable future. We operate manufacturing sites in 30 countries and employ approximately 36,000 people. Dow delivered sales of approximately $43 billion in 2024\. References to Dow or the Company mean Dow Inc. and its subsidiaries. Learn more about us and our ambition to be the most innovative, customer\-centric, inclusive and sustainable materials science company in the world by visiting www.dow.com.
As part of our dedication to inclusion, Dow is committed to equal opportunities in employment. We encourage every employee to bring their whole self to work each day to not only deliver more value, but also have a more fulfilling career. Further information regarding Dow's equal opportunities is available on www.dow.com.
Dow is an Equal Employment Opportunity employer and is committed to providing opportunities without regard for race, color, religion, sex, including pregnancy, sexual orientation, or gender identity, national origin, age, disability and genetic information, including family medical history. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may call us at 1\-833\-My Dow HR (833\-693\-6947\) and select option 8\.
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 DOW, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
DOW AI Hiring
DOW has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
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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