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Senior Machine Learning Engineer Jobs in Ames, IA

Machine Learning Tutor

Ames, IA · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Leverage machine learning, agentic AI, large language models, or intelligent agents to improve engineering workflows * Analyze testing data and system behavior to identify trends, inefficiencies, and ...

Data Engineer

Urbandale, IA · On-site

$80K - $120K/yr

We are seeking passionate, talented engineers to work on exciting projects using the latest tools and technologies including robotics, computer-vision, machine learning, IoT, cloud computing, and ...

Data Engineer

Ankeny, IA · On-site

$108K - $130K/yr

PURPOSE The Data Engineer is responsible for designing, building, and maintaining the data ... Prepares and structures data to support AI and machine learning use cases, including feature-ready ...

Embedded Software Engineer

Urbandale, IA · On-site

$70K - $120K/yr

We are seeking passionate, talented engineers to work on exciting projects using the latest tools and technologies including robotics, computer-vision, machine learning, IoT, cloud computing, and ...

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Senior Machine Learning Engineer information

See Ames, IA salary details

$58.2K

$123.8K

$179.5K

How much do senior machine learning engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for senior machine learning engineer in Ames, IA is $123,797.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,200.00 and $140,400.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What are the key skills and qualifications needed to thrive as a senior machine learning engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Ames, IA?

The most popular types of Machine Learning Engineer jobs in Ames, IA are:

What are popular job titles related to Senior Machine Learning Engineer jobs in Ames, IA?

For Senior Machine Learning Engineer jobs in Ames, IA, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Engineer jobs in Ames, IA look for?

The top searched job categories for Senior Machine Learning Engineer jobs in Ames, IA are:

What cities near Ames, IA are hiring for Senior Machine Learning Engineer jobs?

Cities near Ames, IA with the most Senior Machine Learning Engineer job openings:

Infographic showing various Senior Machine Learning Engineer job openings in Ames, IA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $123,797 per year, or $59.5 per hour.

Sr Machine Learning Engineering Manager - AI Quality and Governance

Workiva, Inc.

Ames, IA • On-site

Other

Retirement

Re-posted 4 days ago


Workiva rating

9.9

Company rating: 9.9 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

1st of 247 rated software companies


Job description

Join Workiva as a Sr Machine Learning Engineering Manager - AI Quality and Governance and help establish how we build, evaluate, release, and operate trustworthy AI products at scale. You will lead a multidisciplinary team of software, machine learning, and quality engineers responsible for two connected missions: advancing end-to-end quality across Workiva's AI platform and products, and building shared evaluation and governance capabilities that make our AI systems measurable, observable, reliable, and ready for enterprise use.

Your team's scope spans generative AI and agentic products, including AI platform services, agent frameworks and runtimes, conversational experiences, and RAG/knowledge systems. You will partner across Product, Engineering, Data Science, Security, Risk, and Legal to establish practical quality standards and embed evaluation and governance throughout the AI development lifecycle.

What You'll DoLeadership & Team Development
  • Lead, mentor, and develop a multidisciplinary team of software, ML, and quality engineers

  • Build a culture of technical excellence, quality ownership, experimentation, and continuous improvement

  • Establish clear team priorities while balancing platform investments, product needs, and enterprise risk

  • Recruit engineers with complementary expertise across software quality, ML evaluation, platform engineering, and governance automation

AI Product Quality
  • Define and drive a comprehensive quality strategy for Workiva's AI platform and products, spanning unit, integration, end-to-end, performance, resilience, security, and production testing

  • Establish measurable quality bars, release-readiness criteria, and automated quality gates for AI and agentic capabilities

  • Advance testing approaches for nondeterministic systems, including RAG pipelines, agents, prompts, models, tools, and multi-step workflows

  • Detect regressions, model or data drift, unsafe behavior, and degraded customer experiences before and after release

AI Evaluation Platform
  • Lead architecture and delivery of a scalable, self-service evaluation platform for generative AI, RAG, and agentic systems

  • Enable teams to create, manage, version, and reuse evaluation datasets, golden test sets, task-specific metrics, graders, and benchmarks

  • Support deterministic checks, statistical metrics, model-based graders, human evaluation, adversarial testing, and domain-expert review

  • Build capabilities for offline evaluation, pre-release regression testing, online experimentation, production sampling, and continuous evaluation

  • Ensure evaluation results are reproducible, explainable, actionable, and integrated into developer workflows, CI/CD pipelines, and operational dashboards

AI Governance & Assurance
  • Translate Workiva's Responsible AI principles into practical engineering controls and platform capabilities

  • Build governance into the AI lifecycle through traceability, lineage, versioning, documentation, risk classification, approval workflows, and auditable evidence

  • Partner with Security, Legal, Privacy, Compliance, and Risk teams to define controls that support enterprise and regulated use cases

  • Enable inventories and traceability across models, prompts, datasets, evaluations, tools, knowledge sources, and deployed AI features

Cross-Functional Leadership
  • Collaborate with Product, Program Management, UX, UXR, Data Science, Security, Legal, Risk, and engineering leaders to define quality expectations and roadmaps

  • Influence engineering teams across Workiva to adopt shared evaluation standards, testing practices, observability, and release controls

  • Communicate complex technical tradeoffs, quality signals, and risk findings clearly to technical and non-technical audiences

Operational Excellence
  • Ensure the evaluation and governance platform is secure, scalable, reliable, observable, and cost-effective
  • Define service-level objectives and meaningful operational and quality metrics

  • Champion production readiness, incident response, root-cause analysis, and continuous operational improvement

What You'll NeedMinimum Qualifications
  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)

  • 10+ years in software engineering, ML engineering, quality engineering, or related roles, including 4+ years leading an engineering team

  • Strong software engineering and systems-design fundamentals, with experience delivering and operating production SaaS or platform capabilities

  • Demonstrated experience establishing automated quality practices for distributed, cloud-based products

  • Practical understanding of the generative AI development lifecycle and challenges of evaluating nondeterministic systems

  • Experience with generative AI concepts: LLMs, RAG, embeddings, vector/hybrid search, agents, tool use, and prompt orchestration

  • Experience defining measurable quality criteria using data, experimentation, telemetry, and production signals

  • Experience with cloud-native architectures on AWS, Azure, or GCP.

  • Proven ability to lead senior individual contributors, navigate tehhnical disagreements, and build high-performance cultures

  • Strong communication and cross-functional leadership skills

Preferred Qualifications
  • Master's degree in Computer Science, Engineering, ML, Data Science, or related field.

  • Experience building or operating AI/ML evaluation, experimentation, observability, model-governance, or ML platform capabilities

  • Experience evaluating RAG and agentic systems, including retrieval quality, groundedness, task completion, tool use, and safety

  • Familiarity with evaluation techniques: golden datasets, statistical metrics, model-based graders, human evaluation, red teaming, A/B testing, and drift/regression detection

  • Working knowledge of ML/AI lifecycle practices: dataset management, model/prompt versioning, experiment tracking, deployment, monitoring, and feedback loops

  • Experience translating Responsible AI, model-risk, privacy, security, or regulatory requirements into scalable engineering controls

  • Familiarity with AI risk/governance frameworks (NIST AI RMF, ISO/IEC 42001, or comparable)

  • Experience with Kubernetes, microservices, CI/CD, infrastructure as code, and modern DevOps/MLOps practices

  • Experience supporting enterprise software in regulated or high-assurance environments

Working Conditions
  • Willingness to travel up to 15% for team and corporate meetings

  • Reliable internet access for remote work

How You'll Be Rewarded

Salary range in the US: $193,000.00 - $308,000.00

A discretionary bonus typically paid annually

Restricted Stock Units granted at time of hire

401(k) match and comprehensive employee benefits package

The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.

Why Join Workiva

Workiva is the platform designed to bring confidence, control, and a competitive edge to the world's most complex organizations. Our AI-powered platform unifies finance, risk, and sustainability on a single, secure foundation-ensuring data is trusted, traceable, and ready to act on. With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world.

At Workiva, you'll bring technology to market that executives, boards, and regulators depend on. The work you do here helps organizations navigate uncertainty, maintain trust, and make decisions that stand up to scrutiny. If you're energized by meaningful challenges, inspired by collaborative teams, and motivated to help organizations turn uncertainty into advantage, we'd love to meet you.

Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic.

Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email talentacquisition@workiva.com.

Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards.

Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment.

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