Top Machine Learning Jobs

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

Design and implement knowledge distillation pipelines, optimize training and inference performance, and collaborate with research on production-ready ML models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

Lead the development of machine learning solutions for video personalization, enhancing user experience through data-driven insights and collaboration across teams. Drive technical strategy in building scalable systems and partnerships to innovate customer recommendations.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Develop and execute ML technology stack vision for Ads, improve ML models, and enhance content understanding using LLMs in a collaborative environment.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

Develop and implement state-of-the-art machine learning models and statistical methods using massive structured and alternative datasets to discover predictive signals, design experiments, validate ideas through simulation, and collaborate to move research into production trading strategies.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

The Research Engineer will develop scalable infrastructure for machine learning research, optimize workflows, and enhance model inference for trading systems.

Join Optiver as a Graduate Quantitative Researcher to analyze large-scale market and order-flow data, develop predictive and stochastic models, apply machine learning and deep learning techniques, and translate research into live trading solutions.

Conduct applied quantitative research on market and order-flow data: develop and validate predictive, statistical, and stochastic models using machine learning and deep learning, test hypotheses, and deploy ideas into live trading environments to identify trading opportunities and manage risk.

Lead development of online mapping perception algorithms for Aurora’s autonomous driving system. Research and build deep learning models, develop verification and validation techniques, and collaborate with mapping, simulation, and safety teams. Build infrastructure and tests supporting deployment decisions, then integrate, test, and deploy production-ready solutions into the Aurora Driver codebase.

The Senior Machine Learning Researcher will design experiments, research new algorithms, and collaborate on deploying models for computer vision and robotics applications.

Develop and operate large-scale AI-powered advertising systems using Grok models. Responsibilities span candidate selection, ranking, auctions, campaign optimization, creative generation, and advertiser experiences. The role owns high-throughput products and systems that drive significant revenue while collaborating in a hands-on, high-excellence engineering environment.

Develop and deploy AI/ML solutions for government customers, including data pipelines, model training, MLOps, deep learning, NLP, and computer vision systems. Collaborate with data scientists, software engineers, and government stakeholders; report model performance; write technical papers; mentor teammates; and communicate technical concepts through briefings and presentations.

Design, train, and evaluate advanced machine learning models for autonomous driving, simulation, and ML platforms. Lead small cross-functional engineering teams, establish data and experiment practices for petabyte-scale training, address long-tail model failures, and work with multimodal datasets exceeding 100TB. The role requires commercial robotics experience, mentoring capability, expertise in VLA or generative world models, Python/PyTorch, sensor processing, and potentially edge deployment.

Design and scale semantic data and AI infrastructure, including embedding pipelines, knowledge graphs, ontologies, vector and hybrid retrieval, RAG systems, and LLM-powered applications. Build production-grade APIs and data pipelines, optimize private GPU inference, establish evaluation and reliability frameworks, and ensure explainability, attribution, and safe outputs. Collaborate across product, analytics, data engineering, and ML teams while mentoring engineers and translating business needs into grounded AI systems.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

Conduct research and develop machine learning models for finance, focusing on deep learning and large language models, optimizing them for performance, and collaborating with teams to integrate ML solutions.

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.