Top Remote Machine Learning Jobs in Peoria, IL

The role involves developing, adapting, and implementing machine learning algorithms for computer vision tasks in power grid analysis. Responsibilities include experimentation, error analysis, and project ownership.

The Sales Executive will develop new AI and ML service opportunities, manage sales pipelines, and engage with technical decision-makers primarily in the US and Canada. Responsibilities include proposal shaping, pipeline management, and client relationship management with a focus on complex technology sales.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Develop and productionize NLP and ML solutions (including LLMs and GenAI) to extract insights from unstructured data. Manage research, data preparation, model training/fine-tuning, evaluation, and cross-functional integration into production systems, focusing on sparse data, model explainability, and performance.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

As a Senior ML Engineer for Payments, you will develop AI solutions for payment experiences, optimize transaction processing, and ensure system reliability while mentoring teammates and collaborating with cross-functional teams.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Build and deploy low-latency, production-grade ML models and end-to-end pipelines for fraud detection. Engineer high-frequency features from massive event data, maintain automated MLOps for continuous training/deployment, optimize runtime scoring across distributed systems, and collaborate with infrastructure, product, and data science teams to translate fraud patterns into scalable algorithmic solutions.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

Lead the development and implementation of machine learning models for energy trading optimization and forecasting, while overseeing a team of data scientists and engineers.

The Principal Machine Learning Engineer will define technical direction, architect scalable systems, lead cross-organizational initiatives, mentor engineers, and communicate effectively at all levels.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Develop and iterate deep learning models for protein-ligand co-folding and structure prediction, benchmark performance, integrate validated models into production drug-discovery pipelines, analyze structural and sequence data, communicate findings, and collaborate with multidisciplinary teams to scale research into products.

Design, build, and operate core AI platform components for training, deploying, and serving ML models at scale. Own inference workflows, GPU workload management, CI/CD for ML, observability, and operational practices. Collaborate across product, infra, security, and data teams and mentor junior engineers.

Lead design, training, evaluation, and productionization of computer vision models for biometric identity verification, face liveness, PAD/anti-spoofing, and deepfake detection. Build data pipelines, run experiments to improve robustness, deploy models on AWS, collaborate cross-functionally, mentor engineers, and research adversarial and biometric ML techniques.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

The role involves improving AI agent quality through designing experiments, maintaining evaluation infrastructure, optimizing LLM inference, and collaborating with product teams. Responsibilities include end-to-end ownership from research to deployment in a dedicated ML Engineer context.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.