Top Machine Learning Jobs in Peoria, IL

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

Remote technical recruiter responsible for sourcing and hiring software engineers and niche technical talent for startups. Tasks include sourcing via LinkedIn and job boards, shortlisting, verifying technical fit, researching companies and market trends, developing and executing recruitment strategies, and communicating with candidates and stakeholders. Immediate joiners preferred.

Apply machine learning to large datasets to build predictive models, perform data mining, and develop algorithms. Analyze results, create visualizations, and provide insights to inform business decisions.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

Lead product initiatives from discovery through launch for healthcare SaaS products using analytics and ML. Own roadmap, prioritize backlog, partner with engineering/design/data, use qualitative and quantitative evidence, and drive adoption and measurable customer impact.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

Own Cash Application and Deduction Resolution Management products end-to-end. Lead discovery, define requirements, prioritize roadmap, partner with engineering/design/sales, ensure financial accuracy, build ERP integrations, drive launches, and maintain product quality and customer-focused outcomes.

Lead implementation of enterprise AI platforms, governance, and high-impact analytics use cases. Drive end-to-end delivery from experimentation to production, mentor teams, embed responsible AI practices, and partner with cross-functional stakeholders to operationalize AI across the business.

The Enterprise Architect leads architectural strategy and integration for technology and processes while aligning IT investments with business goals. Responsibilities include overseeing cloud modernization, governance, and communication with stakeholders.

Manage and grow GPCC portfolio, support strategic priorities, develop KPIs, coordinate cross-functional execution, track performance and insights.

The Director of Product Management will oversee the product lifecycle for AI products, focusing on customer collaboration, rapid prototyping, and product strategy. The role involves leading product teams, driving execution, and shaping the product culture.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

Lead the development of agentic AI systems for real estate, focusing on long-horizon, supervised experiences. Architect collaborative systems and define success metrics while mentoring a small team.

Lead the design and deployment of RL post-training systems, aligning model behavior with user value and business objectives, while mentoring applied scientists and engineers.

As a Principal Machine Learning Engineer, you will develop multimodal AI experiences, design agent interactions, and lead cross-team initiatives while ensuring AI safety and reliability.

As a GTM Engineering Intern, you will automate workflows, design demos, and develop AI-powered tools for market outreach while collaborating with sales and engineering teams.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

The Customer Success Manager drives the adoption of AI solutions in life sciences by managing implementations, developing customer satisfaction strategies, and serving as a trusted advisor.

Lead operationalization of AI strategy by implementing scalable AI platforms, standards, and governance. Identify and prioritize high-impact AI and analytics use cases, drive end-to-end delivery into production, embed AI into business processes, manage AI teams, mentor practitioners, and evaluate emerging AI/GenAI technologies to ensure responsible, secure, and measurable business impact.

Two research fellows will receive a stipend and compute credits to pursue projects in legal AI and automated negotiation. Fellows get expert access, private contract data, and are expected to produce a publishable paper, benchmark, or open-source artifact. Research topics include reward modeling under expert disagreement, negotiation simulation, minimal-diff editing benchmarks, uncertainty/hand-off strategies, or related legal AI problems.

Lead implementation of enterprise AI strategy and platforms, prioritize and deliver high‑impact AI and analytics use cases end-to-end, manage AI teams, embed responsible AI governance, and drive adoption and capability building across business and technology stakeholders.