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About This Role
remote type
Hybrid
locations
Detroit, MI US
time type
Full time
posted on
Posted Today
time left to apply
End Date: August 19, 2026 (13 days left to apply)
job requisition id
DT\-19096Inside the Role
The Senior Data and AI Specialist serves as a technical leader responsible for advancing Detroit's data, analytics, artificial intelligence, and automation capabilities. This role partners with enterprise AI, data engineering, infrastructure, cybersecurity, and business teams to design, implement, and govern AI\-enabled solutions that drive operational efficiency, data\-driven decision making, and business value.
The position combines expertise in data engineering, business intelligence, AI/ML architecture, analytics delivery, and technical project leadership. The Senior Data and AI Specialist acts as a trusted advisor, helping the organization identify, evaluate, and deploy AI technologies while ensuring alignment with enterprise standards, security requirements, governance policies, and long\-term business objectives.Posting Information
We provide a scheduled posting end date to assist our candidates with their application planning. While this date reflects our latest plans, it is subject to change, and postings may be extended or removed earlier than expected.
We Take Care of Our Team
Position offers a starting salary range of $117,000 – $150,000 USD
Pay offered dependent on knowledge, skills, and experience.
Benefits include annual bonus program; 401k company contribution with company match up to 6% as well as non\-elective company contribution of 3 \- 7% depending on age; starting at 4 weeks paid vacation; 13\+ calendar holidays; 8 weeks paid parental leave; employee assistance program; comprehensive healthcare plans and wellness programs; onsite fitness (at some locations); tuition assistance and volunteer paid time off; short\-term and long\-term disability plans.
What You Will Drive
- Data Science Engineering \& Analytics Delivery
+ Build, support, and guide solutions across data engineering, automation, advanced analytics, reporting, and visualization.
+ Apply data engineering concepts, data modeling, and database knowledge to help solve complex business challenges.
- AI Platform Enablement \& Configuration
+ Develop a deep understanding of enterprise AI platforms available across the global enterprise, such as Microsoft Copilot, Copilot Studio, LibreChat, Snowflake Cortex, and other approved internal AI tools.
+ Partner with technical teams to define, document, and implement standardized AI configurations for Detroit in alignment with enterprise architecture and governance.
- Infrastructure, Security \& Ownership Clarity
+ Define and document roles, responsibilities, and ownership across AI platforms, security controls, and operational support.
+ Coordinate with infrastructure, enterprise architecture, cyber security, and governance teams on items such as endpoints, firewall requirements, IAM roles, and access patterns.
- AI Architecture \& Pipeline Integration
+ Collaborate with data engineers, platform owners, application teams, and business partners to design and implement AI/ML pipelines and workflows.
+ Support end\-to\-end AI architecture documentation, including data ingestion, processing, orchestration, model execution, integration points, and operational dependencies.
- AI Strategy, Advisory \& Use Case Enablement
+ Partner with global technical teams, enterprise AI teams, and business stakeholders to identify where AI/ML, automation, analytics, or business intelligence can improve business performance, operational efficiency, and decision\-making.
- Performance, Cost \& Optimization
+ Evaluate performance, scalability, token usage, compute consumption, and cost implications of AI platform configurations and architectural decisions. Recommend approaches that balance speed, value, governance, cost efficiency, and long\-term maintainability.
- Cross\-Functional Collaboration \& AI Adoption
+ Partner with teams driving end\-user AI adoption and enablement to ensure alignment between technical capabilities and business adoption strategies.
+ Act as the technical counterpart to AI enablement initiatives.
+ Act as a key point of contact for AI related data and technical topics.
- Project \& Initiative Leadership
+ Lead and coordinate DDC AI and data technology initiatives across cross\-functional teams, managing scope, dependencies, risks, stakeholders, communication, and delivery milestones for medium to large complex projects.
- Continuous Improvement \& Emerging Technology Awareness
+ Stay current on AI/ML, GenAI, agentic approaches, automation, data engineering, analytics, cloud, and governance trends.
+ Recommend opportunities to improve standards, tooling, workflows, and solution delivery practices.
Knowledge You Should Bring
- Bachelor’s Degree in Computer Science, Data Science, Information Systems or 5\-8 years of relevant experience in data analytics, AI/ML, automation, business intelligence, or data engineering related roles.
- Working understanding of AI/ML concepts, LLMs, prompt\-based systems, tokens, GenAI, automation, BI, and agentic approaches.
- Hands\-on familiarity with enterprise AI and productivity platforms such as Microsoft Copilot, Copilot Studio, Snowflake Cortex, OpenAI\-based tools, or similar approved enterprise AI platforms.
- Demonstrated proficiency or strong working knowledge in database concepts, data modeling, data extraction, and transformation.
- Experience with business intelligence and analytics tools such as Power BI, or comparable enterprise analytics platforms.
- Familiarity with cloud and hybrid environments, access provisioning, network dependencies, IAM roles, and security considerations for enterprise data and AI solutions.
- Knowledge of data governance, information security, enterprise standards, policies, and measures to protect sensitive data.
- Ability to evaluate technical trade\-offs across architecture, user experience, speed to value, risk, cost, scalability, maintainability, and governance.
- Strong project leadership skills with the ability to lead department\-level and cross\-functional initiatives of notable complexity, risk, dependencies, and resource requirements.
- Excellent communication, facilitation, presentation, and interpersonal skills, including the ability to translate complex AI, data, and technical concepts into clear business language for stakeholders at multiple levels.
- Ability to navigate complex organizational structures and align Detroit, DTNA, and Global stakeholders around standards, ownership, and implementation decisions.
- Ability to learn new technologies quickly, adapt to changing technology landscapes, and operate effectively in ambiguous situations.
Exceptional Candidates May Have
- Experience implementing, configuring, or operationalizing enterprise AI platforms, copilots, agents, or LLM\-enabled solutions.
- Experience moving AI/ML, automation, analytics, or data engineering solutions beyond prototype into governed, production\-ready solutions.
- Experience with AI/ML pipelines, data engineering patterns, model integration, workflow orchestration, or enterprise architecture standards.
- Experience with cost governance for cloud, AI token, processing and compute on various platform workloads.
- Experience in manufacturing or automotive environments.
\#LI\-DC1
\#LI\-HYBRID
Where We Work
This position is open to applicants who can work in (or relocate to) the following location(s)\-
Detroit, MI US. Relocation assistance is not available for this position.Schedule Type:
Hybrid (4 days per week in\-office / 1 day remote). This schedule builds our \#OneTeamBestTeam culture, provides an unparalleled customer experience, and creates innovative solutions through in\-person collaboration.
At Daimler Truck North America, we recognize our world is changing faster than ever before. By listening to the needs of today, we’re building to solve with cutting\-edge solutions in sustainability and future driving technology across electric, hydrogen and autonomous. These solutions, backed by years of innovative success and achievement, continue DTNA’s legacy as the undisputed industry leader. Our evolving brand portfolio is second to none, including Freightliner Trucks, Western Star, Demand Detroit, Thomas Built Buses, Freightliner Custom Chassis, and Financial Services. Together, we work as one team towards our envisioned future – building a cleaner, safer and more efficient tomorrow for all.
That is what we are working toward \- for all who keep the world moving.
Additional Information
- This position is not open for Visa sponsorship or to existing Visa holders
- Applicants must be legally authorized to work permanently in the country the position is located in at the time of application
- Final candidate must successfully complete a criminal background check
- Final candidate may be required to successfully complete a pre\-employment drug screen
- Contractors, professional services, or other contingent workers should confirm with their local agency if they are eligible to apply for FTE positions
- EEO \- Disabled/Veterans
Daimler Truck North America is committed to workforce inclusion and providing an environment where equal employment opportunities are available to all applicants and employees without regard to race, color, sex (including pregnancy), religion, national origin, age, marital status, family relationship, disability, sexual orientation, gender identity and expression (including transgender and transitioning status), genetic information, or veteran status.
For an accommodation or special assistance with applying for a posted position, please contact our Human Resources department at 503\-745\-8982 or toll free 800\-206\-3369\. For TTY/TDD enabled call 503\-745\-2137 or toll free 866\-355\-6935\.
Salary Context
This $117K-$150K range is in the lower quartile 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 Daimler Truck North America, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($133K) sits 38% below the category median. Disclosed range: $117K to $150K.
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.
Daimler Truck North America AI Hiring
Daimler Truck North America has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Detroit, MI, US, Portland, OR, US. Compensation range: $150K - $150K.
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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