Top Machine Learning Jobs

Lead Aaru’s Simulation Engineering team and own the quality, scalability, reproducibility, observability, and operational health of production AI simulations. Set architecture and productionization standards, build evaluation and regression systems, improve large-scale simulation performance, manage incidents and releases, collaborate across research and engineering, and recruit and develop high-performing engineers. The role remains hands-on, involving technical design, coding, debugging, and difficult tradeoffs among fidelity, calibration, latency, cost, and reliability.

Design and build production LLM infrastructure, including orchestration, prompting, workflow layers, retrieval integrations, MCP tools, evaluation systems, dashboards, and observability. Improve platform reliability through caching, incident response, scalability, and model rollbacks. Establish architecture standards, conduct design reviews, mentor engineers, and communicate AI concepts to nontechnical stakeholders.

Design, build, evaluate, and deploy AI agents and machine learning automation for engineering, product, delivery, and customer-facing workflows. Integrate AI into existing software, collaborate with technical and healthcare stakeholders, optimize systems for accuracy, latency, cost, and safety, and document development processes. The role emphasizes LLMs, agentic frameworks, production ML systems, human-in-the-loop safeguards, and healthcare-aware solutions.

Lead the development, fine-tuning, deployment, and optimization of machine learning solutions involving LLMs, vision models, and NLP for healthcare applications. Build production-grade models and pipelines, support scalable GPU and TPU training, validate performance and compliance, collaborate with healthcare and product teams, and mentor junior engineers. The role also requires documenting technical approaches and applying MLOps and LLMOps practices in regulated environments.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

As a Senior AI/ML Engineer, you will manage the AI/ML lifecycle, develop predictive models, conduct anomaly detection, and integrate AI capabilities into the Confido platform.

Build large-scale user-signal feature pipelines and production machine learning models for advertising targeting, bidding, and ranking. Own the full lifecycle from data preparation and feature engineering through training, deployment, monitoring, online serving, and A/B testing. Develop identity prediction, embeddings, intent, and conversion models; integrate outputs into real-time ads systems; improve signal quality; and provide technical leadership across Ads, Data, and Infrastructure teams.

PhD research interns will develop and evaluate methods for large-scale model training, optimization, and inference. Work includes improving long-context/horizon capabilities, inference-time efficiency, robustness, and translating research into working implementations across research and engineering teams.

Build and train transformer models for plant intelligence, owning data curation, architecture, training strategies, evaluation, and production deployment. Develop robust model lifecycle systems and training and evaluation infrastructure for rapid iteration. Drive machine learning projects end to end, from data collection through production, while working independently on a small team. Apply strong software engineering and scientific skills to maintain practical ML systems.

Develop high-performance, large-scale machine learning services for a programmatic advertising platform. Build and deploy AI models and data-driven algorithms for real-time ad scoring, ranking, and bid optimization. Work with data scientists and cross-functional teams on scalable data analysis, model development, production rollouts, troubleshooting, and continuous optimization using agile methods.

Leads the design, development, deployment, and governance of production machine learning and GenAI systems on Azure Databricks. Establishes MLOps standards, builds MLflow and CI/CD foundations, architects RAG and LLM solutions, monitors model performance and cost, and mentors ML and data engineers. Partners with product, risk, governance, legal, and security teams to ensure models are reliable, explainable, fair, documented, and compliant.

Research and develop machine learning systems, algorithms, prototypes, and data-fusion solutions for complex sponsor problems. Responsibilities include data selection and modeling, statistical analysis, anomaly detection, visualization, software integration, technical reporting, collaboration with sponsors, and supervision of junior staff. The role supports classified defense and intelligence applications and is performed onsite in State College, Pennsylvania, or Reston, Virginia.

Leads the design, development, and scaling of AI-enhanced applications, APIs, microservices, and automation frameworks. Integrates LLMs, generative AI, computer vision, chatbots, agentic workflows, and autonomous systems into production products. Establishes AI-first engineering practices, optimizes model costs and performance, implements observability, and mentors engineers. The role also influences architecture, technical strategy, business trade-offs, and adoption of AI-powered development tools in an agile environment.

Lead PayJoy’s ML platform organization and manage Staff and Senior ML and software engineers. Define the long-term roadmap for ML infrastructure, feature stores, model serving, ETL, internal APIs, deployment, monitoring, and MLOps. Partner with Risk, Fraud, Engineering, Product, and Data Science stakeholders to translate business goals into scalable technical solutions. Mentor engineering leaders, ensure production reliability and compliance, identify operational bottlenecks, and guide adoption of technologies such as LLMs and graph databases.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead development and deployment of AI/ML models for branch network and field workforce optimization using geospatial, graph, reinforcement learning, and behavioral techniques. Manage end-to-end projects, partner with business, governance, UX, and technology, translate model outputs for stakeholders, and ensure regulatory and model governance compliance.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Build and manage a machine learning engineering team focused on making engineering drawings and CAD data usable for automation. Lead hiring, onboarding, mentorship, performance, technical direction, design reviews, and production deployment ownership. Guide annotation tooling, active learning, synthetic data augmentation, model health, and organizational growth while remaining technically hands-on and partnering across engineering, technical leadership, and business teams.

Develop and integrate large-scale data applications using Accumulo, Hadoop, Kafka, Spark, Yarn, Python, and JavaScript. Implement predictive analytics, graph algorithms, and machine learning models; design client/server systems and integration points; improve development workflows and support analytic toolchains for Intelligence Community missions.

Quantitative Researchers will build models and strategies for pricing and trading financial instruments, collaborating closely with teams and using various techniques from statistical methods to deep learning.

The Machine Learning Engineer will lead compliance efforts, review data sets, create databases, perform gap analyses, and evaluate existing AI systems, requiring collaboration with various stakeholders.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.