Top Machine Learning Jobs

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

As a Machine Learning Engineer, you will develop machine learning models, automate data engineering pipelines, and support customer-centric projects while collaborating with data scientists and engineers.

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.

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.

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.

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.

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 Staff Machine Learning Engineer will develop and scale ML systems for content classification, moderation, and decision-making, ensuring quality and compliance across Spotify's platform.

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.

Design, implement, train, evaluate, optimize, and deploy advanced AI/ML solutions including Agentic AI, RAG, and LLMs for constrained/edge environments; translate mission stakeholder requirements into scalable, resource-aware models and recommend performance improvements.

Provide advanced discovery and analytical support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, document methods and collaborate with analysts and engineers to support mission-focused discovery efforts and adopt new analytical technologies.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

Designs, implements, and deploys scalable AI/ML solutions: selects algorithms, engineers features, trains and tunes models, integrates models into production, monitors performance, mentors team members, leads design reviews, and communicates technical strategy to stakeholders.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

The Principal Machine Learning Infrastructure Researcher will design and model performance for ML systems using Lightmatter technology while leading a small team and publishing research findings.

Apply machine learning and AI to structured and unstructured IoT and other datasets: clean and integrate data, develop and deploy user-facing models, create visualizations/dashboards or lightweight web apps, advise clients, lead teammates, and apply inferential statistics and research design. Requires TS/SCI with polygraph.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.