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Ai Alignment Jobs in Michigan (NOW HIRING)

This leader works across business units, technical teams, and functional stakeholders to align priorities, remove barriers, and ensure consistent, scalable execution of AI initiatives. This role is ...

... aligned with existing technology standards. Designs, develops, and deploys AI solutions that enhance business processes, improve decision-making, and drive innovation. Collaborates with cross ...

Data Pipeline Alignment: * Ensure AI solutions effectively leverage enterprise data pipelines (e.g., Databricks). * Feature & Data Strategy: * Guide design of features and data structures required ...

Establish AI operating models, governance frameworks, success metrics, and best practices aligned with organizational technology standards. * Collaborate with business leaders, technical teams, and ...

AI/ML and Data Engineer

Southfield, MI · On-site +1

$104K - $125K/yr

The position also contributes to organizational capability-building by coaching stakeholders on appropriate AI use, shaping standards-aligned governance practices, and representing SME's point of ...

AI/ML and Data Engineer

Southfield, MI · On-site

$104K - $125K/yr

The position also contributes to organizational capability-building by coaching stakeholders on appropriate AI use, shaping standards-aligned governance practices, and representing SME's point of ...

Ensure all solutions align with IT governance, security, and compliance standards. * Facilitate discovery sessions with department stakeholders to build and prioritize a backlog of automation and AI ...

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Ai Alignment information

What is AI alignment?

AI alignment refers to the process of ensuring that artificial intelligence systems act in ways that are aligned with human values, intentions, and ethical standards. This field focuses on designing AI models that not only achieve their objectives but also do so safely and beneficially for humanity. As AI systems become more advanced, alignment becomes increasingly important to prevent unintended consequences or harmful behaviors. Researchers in AI alignment work on technical solutions, such as value learning and interpretability, as well as broader ethical and policy considerations.

What are some common challenges faced by professionals working in AI alignment roles?

Professionals in AI alignment roles often encounter the challenge of translating complex ethical principles and human values into machine-understandable objectives. Balancing technical constraints with theoretical considerations requires close collaboration with cross-functional teams, including ethicists, engineers, and product managers. Additionally, the rapidly evolving landscape of artificial intelligence demands continuous learning to stay current with new alignment techniques and research findings. Navigating these challenges can be intellectually stimulating and offers significant opportunities for interdisciplinary growth.

What are the key skills and qualifications needed to thrive as an AI alignment specialist, and why are they important?

To thrive as an AI Alignment Specialist, you need a strong background in computer science, mathematics, and machine learning, often evidenced by an advanced degree in a related field. Familiarity with technical tools such as Python, TensorFlow, PyTorch, and formal verification systems is typically required, along with understanding of AI safety principles. Analytical thinking, ethical reasoning, and effective communication are crucial soft skills for success in this role. These skills ensure that AI systems are developed safely, ethically, and in alignment with human values, which is essential for mitigating risks associated with advanced AI.

What is the difference between Ai Alignment vs Data Scientist?

AspectAi AlignmentData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or related fieldsDegree in Data Science, Statistics, Computer Science, or related fields
Work EnvironmentResearch labs, AI development companies, tech firmsTech companies, finance, healthcare, consulting firms
Industry UsageFocuses on ensuring AI systems behave as intendedAnalyzes data to extract insights and build predictive models

While both roles involve advanced technical skills, Ai Alignment specialists focus on aligning AI systems with human values and safety, whereas Data Scientists analyze data to inform business decisions. The roles often overlap in AI research environments but serve different primary objectives.

What are popular job titles related to Ai Alignment jobs in Michigan?

For Ai Alignment jobs in Michigan, the most frequently searched job titles are:

What cities in Michigan are hiring for Ai Alignment jobs?

Cities in Michigan with the most Ai Alignment job openings:

Infographic showing various Ai Alignment job openings in Michigan as of August 2026, with employment types broken down into 73% Full Time, 21% Part Time, 2% Temporary, 3% Contract, and 1% Nights. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Head of AI Acceleration

Novi, MI

DexKo Global
Manufacturing • 5 - 10K employees

Full-time

Re-posted 25 days ago


Key responsibilities

  • Drive adoption and business impact of AI solutions by ensuring they are embedded into work processes and monitored for performance

  • Shape and execute the AI roadmap by translating enterprise strategy into prioritized initiatives and refining them based on results

  • Collaborate across teams to ensure end-to-end execution of AI initiatives, removing barriers and ensuring progress at pace


Job description

Overview:

The Head of AI Acceleration is responsible for driving AI strategy execution and adoption across DexKo. This role drives the enterprise AI direction and ensures initiatives move from idea to production at speed, delivering measurable business impact.

This leader works across business units, technical teams, and functional stakeholders to align priorities, remove barriers, and ensure consistent, scalable execution of AI initiatives.

This role is accountable for orchestrating efforts across the organization to accelerate progress and outcomes. As AI resources are distributed across the company, this role will need to coordinate and influence resources in other business functions and segments.

Responsibilities:

Drive Adoption and Business Impact:

  • Ensure AI solutions are not just built, but adopted and embedded into how work gets done

  • Partner with business leaders to define changes in roles, processes, and decisions as a result of AI initiatives

  • Establish adoption, usage, and impact metrics across all initiatives

  • Monitor performance and drive continuous improvement post-deployment

Shape and Execute AI Roadmap:

  • Translate enterprise strategy into a prioritized, actionable AI roadmap

  • Balance quick wins with longer-term, high-impact initiatives

  • Continuously refine priorities based on business needs and results

Build Organizational Capability:

  • Support the development of AI capability across the business through AI Business Partners, Training and enablement programs, and communication.

  • Foster a culture of experimentation, learning, and responsible AI use

Drive Enterprise AI Execution:

  • Collaborate with business, CoE, and external partners on the end-to-end execution of AI initiatives through the pipeline (idea ? use case ? business case ? POC ? implementation ? production)

  • Ensure initiatives progress at pace, with clear accountability and measurable outcomes

  • Identify and remove barriers to execution across teams and functions

  • Apply company-wide value stream lens to portfolio of projects to ensure holistic approach and project connectivity across business processes, functions, segments and geographies

Orchestrate a Federated AI Model:

  • Align and coordinate across: Central AI / technical resources and CoE, Business Unit AI Business Partners, Business analysts and AI stewards embedded in functions

  • Establish clear ways of working across this network to ensure consistency and efficiency

  • Drive prioritization across competing initiatives and resources

Standards, Governance, and Ways of Working

  • Collaborate with and provide feedback to CoE and AI Governance Council regarding standards for: Use case development, Business case rigor, Model deployment and lifecycle management, change management, ROI and benefit tracking

  • Partner with governance bodies (e.g., AIGC) to ensure appropriate controls without slowing progress

  • Ensure a repeatable, scalable approach to AI delivery

Provide Visibility to Leadership:

  • Deliver clear, concise reporting to ELT and stakeholders on:

  • Pipeline status

  • Initiative progress

  • Business impact

  • Risks and dependencies

  • Create transparency and accountability across the AI portfolio

Key Interfaces:

  • Chief Business Strategy & Transformation Officer

  • Business Unit Leaders

  • AI Business Partners (dotted-line alignment)

  • Central AI / Data / Technology teams

  • Functional leaders (Operations, Finance, Purchasing, etc.)

Success in this role will result in the following:

  • AI initiatives consistently move from idea to production without stalling

  • Clear visibility into pipeline, priorities, and impact across the enterprise

  • Strong alignment between business units and technical teams

  • Measurable business value delivered at increasing scale

  • AI becomes embedded in how work is done—not isolated pilots

Qualifications

Ideal Attributes

  • Execution-focused: drives outcomes, not just strategy

  • Influential: able to lead without direct authority

  • Pragmatic: balances speed with structure

  • Credible: trusted by both business and technical stakeholders

  • Resilient: able to push through resistance and ambiguity

Experience & Knowledge

Years of experience: 7-15 years

  • Proven experience leading large-scale, cross-functional initiatives in a complex organization

  • Strong track record of driving execution and delivering measurable business outcomes

  • Experience working across business and technology teams

  • Familiarity with AI, data, and advanced analytics (deep technical expertise not required, but strong fluency expected)

  • Experience operating in a matrixed model

Working Conditions

  • Travel frequency: Up to 25%