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Vice President Machine Learning Jobs in Indiana (NOW HIRING)

Job Summary The VP, Chief Accounting Officer is responsible for the company's accounting function ... Continuous learning mentality Required Education and/or Certifications * B.A./B.S. Recommended ...

Job Summary The VP, Chief Accounting Officer is responsible for the company's accounting function ... Continuous learning mentality Required Education and/or Certifications * B.A./B.S. Recommended ...

Showing results 21-40

Vice President Machine Learning information

See Indiana salary details

$33.8K

$109.2K

$172.7K

How much do vice president machine learning jobs pay per year?

As of Jul 24, 2026, the average yearly pay for vice president machine learning in Indiana is $109,171.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,400.00 and $136,100.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Vice President of Machine Learning, and why are they important?

To thrive as a Vice President of Machine Learning, you need advanced expertise in machine learning, data science, and computer science, typically backed by a master's or PhD and extensive industry experience. Proficiency with platforms like TensorFlow, PyTorch, cloud computing services, and experience managing large-scale AI projects are crucial, along with a track record in leading technical teams. Exceptional leadership, strategic vision, and strong communication skills set outstanding candidates apart by enabling effective cross-functional collaboration and innovation. These skills are vital for driving organizational AI strategy, ensuring technical excellence, and delivering scalable business impact.

What does a Vice President of Machine Learning do?

A Vice President of Machine Learning leads and oversees the strategic direction of machine learning initiatives within an organization. They manage teams of data scientists, engineers, and researchers to develop and deploy AI-driven solutions that support business goals. This role involves collaborating with other executives, setting research agendas, ensuring best practices, and staying updated with the latest advancements in the field. The VP also plays a key role in resource allocation, talent acquisition, and scaling machine learning systems across the company.

What are some common challenges faced by a Vice President of Machine Learning when leading cross-functional teams?

A Vice President of Machine Learning often encounters challenges such as aligning diverse teams on technical priorities, managing expectations across product, engineering, and business units, and ensuring effective communication between stakeholders with varying levels of technical expertise. Balancing the need for innovation with practical business objectives and resource constraints is also a frequent challenge. Cultivating a collaborative culture and fostering ongoing professional development are key to overcoming these hurdles and driving successful outcomes.

What is the difference between Vice President Machine Learning vs Director of Machine Learning?

AspectVice President Machine LearningDirector of Machine Learning
Required CredentialsAdvanced degrees (Master's/PhD), extensive experience in MLSimilar educational background, less senior experience needed
Work EnvironmentStrategic leadership, cross-departmental collaborationProject management, team oversight
Employer & Industry UsageLarge tech firms, enterprises with AI focusTech companies, startups, research labs
Search & Comparison IntentHigh overlap in responsibilities and qualificationsRelated but more operational role

The Vice President Machine Learning typically holds a senior leadership role focused on strategic planning and cross-functional collaboration, while the Director of Machine Learning manages day-to-day projects and teams. Both roles require advanced degrees and experience in machine learning, but the VP is more involved in high-level decision-making and industry strategy.

What are the most commonly searched types of Machine Learning jobs in Indiana? The most popular types of Machine Learning jobs in Indiana are:
What are popular job titles related to Vice President Machine Learning jobs in Indiana? For Vice President Machine Learning jobs in Indiana, the most frequently searched job titles are:
What cities in Indiana are hiring for Vice President Machine Learning jobs? Cities in Indiana with the most Vice President Machine Learning job openings:
Vice President, Software Engineering and Development

Vice President, Software Engineering and Development

ASCENSUS

Indianapolis, IN

Full-time

Medical, Dental, Vision, Retirement

Posted 25 days ago


Ascensus rating

8.2

Company rating: 8.2 out of 10

Based on 39 frontline employees who took The Breakroom Quiz

46th of 150 rated financial services


Job description

Ascensus is the leading independent technology and service platform powering savings plans across America, providing products and expertise that help nearly 16 million people save for a better today and tomorrow.

Section 1: Position Summary

The Vice President of Software Engineering and Development leads high-performing teams across engineering, QA, DevOps, Application Support, and data architecture for the assigned line of business. This role partners closely with product, technology, and business leaders to deliver scalable, secure, and high-quality software solutions. The VP drives operational efficiency through lean practices, automation, and data-driven metrics, and operationalizes AI by establishing governance, delivery patterns, and platform capabilities that move pilots into production with measurable business outcomes. This role influences enterprise technology strategy and fosters alignment across IT to deliver scalable, secure, and innovative solutions. This position reports to the SVP of Software Engineering and Development for the assigned line of business.

Section 2: Job Functions, Essential Duties and Responsibilities

Leadership and Talent Development

  • Attract, develop, and retain diverse, high-performing engineering, QA, DevOps, data, and architecture teams.
  • Set clear goals and succession plans, and foster accountability, learning, and psychological safety.
  • Model leadership behaviors that elevate engagement, inclusion, and performance.
  • Promote a culture of continuous learning and inclusive leadership across all levels.

Operational Excellence and Efficiency

  • Improve time to value using lean value-stream practices and a standard metric set: throughput, cycle time, predictability, change failure rate, lead time for changes, and mean time to restore.
  • Apply SRE practices for reliability, observability, incident response, and post-incident learning.
  • Standardize tools and automation to reduce toil and raise developer productivity, with FinOps to optimize run costs and unit economics.
  • Champion quality assurance practices to ensure software products meet rigorous standards and deliver excellent client experiences.
  • Strengthen onboarding processes for new associates and consultants, ensuring rapid ramp-up through structured training, documentation, and mentorship.
  • Promote continuous learning and upskilling through targeted enablement programs aligned with evolving technology and business needs.

Software Delivery and Agile Execution

  • Ensure consistent agile execution with clear sprint and release commitments across multiple teams.
  • Institutionalize feedback loops and retrospectives that inform capacity planning and roadmaps.
  • Advance engineering excellence including test automation, CI/CD, infrastructure as code, and quality engineering.
  • Promote a metrics-driven culture that uses data to guide roadmap sequencing, capacity planning, and risk management.
  • Lead Developer Experience (DevEx) across the technology organization, championing AIfirst development to significantly improve engineering efficiency, throughput, and developer productivity.what

Innovation and Emerging Technology

  • Translate prioritized business problems into AI roadmaps with defined success metrics and value targets.
  • Govern data privacy, responsible AI, and model risk in partnership with Risk, Security, and Compliance.
  • Scale repeatable AI patterns into production and ensure alignment with business outcomes and compliance standards.
  • Foster a culture of experimentation and innovation across teams.
  • Evaluate and pilot new AI capabilities, tools, frameworks, and platforms that accelerate delivery, improve resilience, or unlock new business value.
  • Monitor emerging technologies and trends to identify opportunities for strategic adoption and competitive advantage.

Platform and Architecture Stewardship

  • Guide development of scalable, maintainable platforms aligned to enterprise architecture.
  • Drive reuse through standards and shared components to reduce complexity and cost.
  • Lead ongoing platform transformation initiatives that support long-term scalability and resilience.

Risk, Security, and Compliance

  • Ensure compliance with SOC-aligned controls and internal governance standards.
  • Embed secure development lifecycle practices with clear vulnerability remediation SLAs.
  • Protect confidential data with proper handling to prevent unauthorized access or disclosure, and produce audit-ready evidence.
  • Collaborate with Security and Risk teams to proactively identify and mitigate threats across the software lifecycle.

Financial and Vendor Management

  • Own budget planning and forecasting, and manage capacity to plan.
  • Manage vendor performance and contracts to outcomes, and make sound build versus buy decisions.
  • Present portfolio health and value delivery to senior stakeholders with clear, actionable insights.
  • Align financial decisions with long-term technology strategy and operational efficiency goals.

Cross-Functional Collaboration and Client Support

  • Align with Product on outcome-based roadmaps and prioritization.
  • Support RFPs and key client engagements as the primary IT contributor .
  • Share best practices and drive alignment across IT.
  • Represent technology leadership in client-facing discussions to reinforce trust and transparency.

General

  • Responsible for protecting, securing, and proper handling of all confidential data held by Ascensus to ensure against unauthorized access, improper transmission, and/or unapproved disclosure of information that could result in harm to Ascensus or our clients.
  • At Ascensus we are guided by our Core Values of People Matter, Quality First and Integrity Always. They inspire us every day to prioritize an environment of respect for those we serve and one another and should be visible in your actions on a day-to-day.

Supervision

  • Hire, coach, and develop associates and leaders with clear growth plans.
  • Provide ongoing feedback and ensure succession coverage for critical roles.
  • Model leadership behaviors that reflect Ascensus Core Values and foster an inclusive, high-performance culture.
  • Ensure team structure supports scalability, agility, and cross-functional collaboration.

Section 3: Experience, Skills, Knowledge Requirements

  • 10+ years in software engineering with 5+ years leading multi-team organizations.
  • Proven success leading distributed teams at scale.
  • Expertise in agile delivery, engineering metrics, and continuous improvement.
  • Hands-on understanding of CI/CD, test automation, infrastructure as code, and quality assurance practices.
  • Experience operationalizing AI and data-driven engineering practices in production.
  • Familiarity with SRE and FinOps practices.
  • Experience leading platform upgrade or platform migration initiatives.
  • Strong communication, stakeholder management, and decision-making skills.
  • Demonstrated ability to influence enterprise-level technology decisions and build consensus across diverse stakeholders.
  • Proficiency in modern frameworks and at least one major language such as C# or Java.

The national average salary range for this role is $230-280k in base pay, exclusive of any bonuses and benefits.This base salary range represents the low and high end of the salary range for this position. Actual salary offered will vary and may be above or below the range based on various factors including but not limited to location, experience, performance, and internal pay alignment. We do not anticipate that candidates hired will begin at the top of the range however, from time to time, it may occur on a case-by-case basis. Other rewards and benefits may include: 401(k) match, Medical, Dental, Vision, Paid-Time-Off, etc. For more information, please visit careers.ascensus.com/#Benefits.

Be aware of employment fraud. All email communications from Ascensus or its hiring managers originate from @ascensus.com or @futureplan.com email addresses. We will never ask you for payment or require you to purchase any equipment. If you are suspicious or unsure about validity of a job posting, we strongly encourage you to apply directly through our website.


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