Top Machine Learning Jobs

Build and operate production machine learning systems focused on multilingual NLP, including entity linking, classification, event extraction, deduplication, and narrative analysis. Develop GPU-accelerated workflows, fine-tune transformer models, and create LLM features such as RAG pipelines and agents on AWS Bedrock. Establish evaluation frameworks, integrate ML services into Django and event-driven platforms, lead technical architecture discussions, and mentor team members.

Productionize and optimize ML systems for AI reasoning over structured data. Responsibilities include improving training and inference performance, porting workloads from CPU to GPU, building training and deployment infrastructure, developing experimentation and observability tools, and implementing new model architectures and fine-tuning methods using Python and PyTorch. The role requires strong ML research, systems engineering, software architecture, optimization, and experimental design skills.

Lead the architecture and hands-on delivery of production AI systems using agentic frameworks, RAG, LLM orchestration, document intelligence, knowledge graphs, and evaluation pipelines. Manage and mentor 4–8 engineers, direct architecture and delivery, build Python and Java services, establish observability and CI/CD, and integrate AI platforms with enterprise systems. The role owns technical strategy for multi-agent workflows, vector retrieval, prompt engineering, validation, and AI-assisted developer tooling.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Design, train, and deploy ML models and pipelines to optimize Databricks serverless compute. Build end-to-end ML4Sys solutions, scale infrastructure, implement optimization and scheduling algorithms, and collaborate on product roadmap to improve performance and cost efficiency.

Contribute to development, deployment, and monitoring of production ML models; perform data cleaning, analysis, and visualization; collaborate with cross-functional teams to deliver AI-driven business solutions and communicate insights to stakeholders.

Design, train, evaluate, deploy, and own production machine learning models for transaction categorization, risk scoring, and financial data enrichment. Build scalable ML systems and automated data pipelines across large, diverse datasets. Partner with product, data science, and engineering teams, improve model reliability and accuracy, explore emerging AI techniques, and mentor engineers.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Develop, evaluate, and deploy reinforcement learning methods for autonomous vehicle planning. Design real-time model architectures, build offline data-driven ML infrastructure, analyze fleet and field-testing data, optimize and deploy planning models, and collaborate with perception, software, and AI teams to deliver production autonomous driving systems.

Develop, optimize, and deploy computer vision and machine learning algorithms for autonomous robotics systems. Train custom models, including object detection, segmentation, and 3D vision architectures, for embedded platforms. Collaborate across teams to integrate algorithms into products, research emerging computer vision techniques, and maintain documentation and unit tests. The role requires strong PyTorch, C++, Python, multi-view geometry, deep learning, and robotics expertise.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As a Robotics ML Research Engineer, you will develop algorithms for robot manipulation, lead software infrastructure, and innovate in robotics learning research.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

As a Software Engineer intern, you'll work on AI-driven projects spanning data management, onboard systems, ML infrastructure, simulations, and technical infrastructure, collaborating with experts in various fields.

Lead actuarial data scientist to oversee technical pricing models, actuarial systems, and a team of actuarial data scientists. Develop strategic roadmap, establish modeling standards and validation, apply ML/AI to pricing, collaborate with legal/compliance, present insights to executives, and manage projects to deliver high-quality actuarial products.

Conduct ML research for query understanding, ranking, clustering, and AI-powered search. Prototype and productionize algorithms, define data and evaluation requirements, design scalable systems, collaborate with engineers, run experiments, and publish patents/papers to integrate state-of-the-art ML into Yahoo Search products.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

As an AI/ML Engineer, you will develop and optimize machine learning models, collaborate with teams, and enhance mobile app features using AI techniques.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.