Top Machine Learning Jobs

Design, develop, validate, and deploy scalable AI/ML and simulation/optimization solutions using structured and unstructured data. Lead model development within model risk frameworks, mentor junior scientists, collaborate with engineering and product teams for MLOps/cloud deployments, and communicate actionable insights to business partners while ensuring regulatory and risk compliance.

Design, implement, and deploy end-to-end machine learning and AI solutions. Build data pipelines, train and evaluate models, write production-quality code, and operationalize capabilities in cloud or Linux environments while working with clients and following software engineering best practices.

Designs and develops moderately complex software systems, applies AI/ML methodologies, uses Spring/J2EE frameworks, makes independent technical decisions, manages multiple tasks, and coordinates with internal and external stakeholders while receiving guidance on new assignments.

Develop and productionize C# applications and machine learning solutions on Azure. Work with .NET/Visual Studio, Azure services (IaaS, Storage, SQL, SDKs, Data Lake, Cosmos, Azure ML), implement ML/deep learning models, and collaborate in a global delivery environment.

Develop production C# software and machine learning solutions on Azure. Work with Azure services (VM, Storage, SQL, Data Lake, Cosmos), Azure ML and Cortana Intelligence, perform data mining and deep learning tasks, and collaborate in a global delivery environment.

Architect role to design full-stack, large-scale solutions informed by leading tech companies. Requires deep ML/AI and large-data analysis knowledge, proficiency across languages/frameworks (Go, Scala, Python, R, MATLAB, Node.js, React), NoSQL expertise, and familiarity with mobile testing, deployment pipelines, and SRE practices. US work authorization required.

Join Capsule as an AI Engineer to develop ML models for video editing. Responsibilities include researching ML techniques, collaborating on feature development, and optimizing model performance.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

Lead development and administration of Quality digital tools, build UIs, dashboards, and automated data pipelines. Perform data analysis, predictive/time-series modeling and apply ML/AI to improve warranty, claims validation, and customer experience. Serve as SME for warranty/data platforms, deliver analytics, run PoCs, and partner with stakeholders to reduce warranty cost and lead-times.

Own the end-to-end roadmap for a model-powered product surface, evaluate model performance, build and maintain eval sets, balance accuracy/speed/cost tradeoffs, translate ambiguous customer issues into testable model or UX fixes, and work daily as a peer with CV and ML engineers to ensure model reliability for customers.

Design, build, and deploy novel sensor hardware and data acquisition systems; curate geoscientific datasets; develop statistical, physics-based, and ML models (2D/3D) to predict mineralization; create visualizations and evaluation tools; collaborate with geoscientists, engineers, and vendors; deploy sensors in the field and integrate results to guide exploration programs.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Develop and optimize machine learning and statistical models to predict liquid asset movements. Parse and analyze large datasets, implement academic research for alpha generation, and collaborate with Data, Alpha, and Strategy teams to design market-neutral trading strategies and run simulations to validate hypotheses.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.

10-week Chicago internship applying machine learning and statistical methods to develop trading strategies, design optimization algorithms for portfolio construction, and build quantitative models of market behavior. Interns extend existing research and explore new topics while implementing models and algorithms in Python alongside mathematicians and technologists.

Lead the development of pricing algorithms for vehicle valuation at CarMax, managing a team of data scientists to implement strategies and maintain optimal pricing models.

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

Develop machine learning models and optimize pricing algorithms to enhance Lyft's market dynamics, collaborating with cross-functional teams on real-world business problems.

As an Applied Scientist at Lyft, you will develop ML and optimization models for dynamic pricing, collaborate across teams, and implement algorithms to enhance rider experience.

As a Machine Learning Engineer Intern, you'll modify models for ego-vehicle predictions, optimize loss functions, and evaluate trajectory diversity and accuracy.

Drive end-to-end ML solutions for Uber Freight: identify opportunities, research techniques, prototype, deploy and monitor production models at scale, and integrate Generative AI/agentic systems to optimize logistics and operations.

Lead development of interpretable, production-quality valuation models for residential real estate. Partner with the CTO to design methodologies, quantify uncertainty, evaluate model performance, and translate research into scalable, maintainable ML systems. Mentor hires, set technical standards, and collaborate with engineering to deploy trusted analytics used across the country.

Lead development and deployment of AI and analytic solutions for healthcare using advanced statistical and machine learning methods. Provide enterprise consultative services, design experiments, build predictive/prescriptive models, direct analysis and reporting, and lead technical teams to translate insights into clinical applications.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.