Top Machine Learning Jobs

Lead end-to-end development and productionization of high-priority propensity and churn models. Build, train, deploy, monitor models; perform advanced feature engineering and semantic search/RAG workflows; collaborate with marketing pods to translate business goals into microsegmentation and personalization strategies; present model insights and maintain model lifecycle and performance.

Lead development and productionization of interpretable time-series and ML forecasting models for growth, revenue, LTV, and compute. Own end-to-end lifecycle from feature engineering and experimentation to deployment, monitoring, explainability, and self-service forecasting tools. Translate model outputs into strategic business recommendations and collaborate cross-functionally to integrate forecasts into planning and decision-making.

Design and ship production-ready ML systems from research to deployment across NLP, vision, recommendation, and structured data. Scope problems, run rigorous experiments, build training/inference pipelines, optimize for performance and cost, implement safety/fairness evaluations, monitor data quality, and mentor engineers while influencing the AI roadmap.

Lead the AI & Analytics stream to design, build, and deploy predictive and prescriptive ML models, integrate analytics into enterprise platforms (Salesforce, ServiceNow), automate root-cause and log analysis, translate stakeholder needs into production solutions, and manage analytics projects end-to-end while communicating results to technical and executive partners.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

Design, implement, and scale production-ready ML systems (computer vision and LLMs) for blueprint understanding, 3D model generation, document extraction, and materials forecasting. Build ML pipelines, deploy and monitor models, integrate retrieval/RAG and rules-based methods, and collaborate with product, engineering, and domain experts to deliver explainable, business-impacting features.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Design and deploy scalable production ML systems powering Diffbot's Knowledge Graph. Improve model accuracy via research, feature engineering, and hyperparameter tuning; own end-to-end performance for 60+ ML problems; assess new research for production feasibility; collaborate on training data and error analysis with product and customers.

Build and ship applied AI features across web and mobile: integrate agents/LLMs into React/React Native apps and Node.js/Postgres APIs, design real-time matching and vetting systems, partner with design, and own product-engineering work on an early-stage team.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.

The AI Intern supports Hoosier Energy in leveraging artificial intelligence for efficiency by developing AI models, preparing datasets, and ensuring compliance with data governance.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Develop, debug, and maintain AI applications and tools for large-scale data analysis. Collaborate with engineers and leadership to design solutions, document procedures, and support users.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

As an ML Engineer, develop and deploy advanced ML solutions for OCR, document processing, and voice technologies, collaborating with teams to optimize models for performance.

The Senior Machine Learning Engineer will lead applied research projects, design and implement ML models, maintain data pipelines, and mentor junior members for defense-focused missions.

As a Senior ML/AI Engineer, you'll build an AI operational backbone for a new consumer membership app, shipping AI-native features for millions of users.

The Machine Learning Engineer will develop and implement ML algorithms for improving communication system performance, collaborating on various use cases and data analysis.

Develop and translate advanced ML models for heart disease diagnostics, collaborate with cross-functional teams, conduct research, and manage projects.

The Senior Machine Learning Engineer will design, develop, and deploy machine learning solutions for Vitol, working across trading and operations with various stakeholders.

Lead end-to-end ML data programs for autonomous vehicle models: define dataset requirements, develop labeling policies, build evaluation frameworks and metrics, coordinate cross-functional teams, and drive data selection strategies to maximize labeling ROI and model performance.

The Machine Learning Engineer role involves optimizing models for performance, utilizing CUDA, GPU scheduling, and collaborating on ML systems design.