Top Machine Learning Jobs

Design and deploy ETL pipelines and machine learning models on platforms like Databricks, Snowflake, or Palantir Foundry. Work with clients—including military—to translate operational problems into AI-driven solutions, deliver modular applications, and support mission-critical decision-making. Support real-time analytics and collaborate on deployment and workflow design.

Work with enterprise customers to identify high-value tabular ML use cases, design production-grade architectures, prototype solutions, lead deployments, coordinate cross-functional teams, and codify reusable deployment patterns to scale adoption of the NEXUS tabular foundation model.

Design and implement backend and systems-level software for defense and intelligence, integrating signal-processing and machine-learning algorithms into real-time, field-ready systems. Collaborate in small teams to prototype, architect, and deploy extensible solutions that integrate with existing infrastructure while translating customer requirements into development priorities.

The role involves building predictive models for blockchain performance and user behavior, implementing solutions, and owning production prediction pipelines.

Develop and productionize machine learning models to predict sports outcomes and optimize pricing. Handle end-to-end data collection, cleaning, feature selection, model building, and visualization. Collaborate with product, trading, and stakeholders to deploy models, create dashboards, and improve betting product offerings.

Remote three-month internship building and deploying AI and data solutions for engineering problems. Develop architectures, implement proofs-of-concept, write code, demo applications, and ship production solutions using cloud, databases, ML/LLMs, and data engineering.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Lead analytics for Discovery Mode's ML squad: design experiments, build evaluation frameworks, analyze model performance, produce dashboards, and translate insights into product and model improvements.

Lead design, development, and deployment of production-grade, large-scale ML systems. Influence ML strategy, integrate models with platform and data infrastructure, mentor engineers, communicate results to stakeholders, and mature ML infrastructure and abstractions across teams.

Lead design, development, and deployment of large-scale production ML systems and platform strategy. Research and prototype ML and big-data technologies, integrate ML services across the org, mentor engineers and data scientists, and communicate complex ML solutions to technical and non-technical stakeholders.

Lead improvements to behavior and trajectory prediction for autonomous vehicles. Own prediction work within Planning, collaborate with ML, Perception, Simulation, and Systems teams, refine prediction-planning interfaces, define metrics and evaluation frameworks, analyze failures via logs/simulation, and lead technical projects while mentoring junior engineers.

The role involves designing and implementing AI and machine learning solutions, transforming data into insights, and supporting client engagements. Responsibilities include model development, data analysis, and enhancing model performance using established technologies.

Develop novel AI approaches for healthcare using structured and unstructured data, build ML models, conduct experiments, and collaborate with stakeholders.

Develop AI approaches using structured and unstructured data, build ML models, perform statistical analysis, and collaborate with stakeholders and experts.

The Staff Machine Learning Engineer will design and build scalable ML systems for health insights, collaborating with cross-functional teams to ensure compliance and quality in production-ready services.

Manage a team of Machine Learning Engineers, architecting systems for content integrity, and ensuring effective model training and deployment.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

The role requires developing machine learning models for cyber threat detection, optimizing response times, and utilizing innovative tools for cybersecurity applications.

Lead a team of machine learning engineers and data scientists to develop algorithms for personalized shopping, enhancing client engagement and driving growth through AI and data science.

As a Forward Deployed Machine Learning Engineer, you'll build and deploy machine learning models, enhance system performance, and collaborate with Data Science and Engineering teams for effective solutions.

The Director of Prediction and ML Planning leads machine learning teams to advance AI-driven behavior in autonomous vehicles, ensuring technical breakthroughs and organizational growth.

Lead a team in advancing large-scale machine learning through research in efficiency and optimization, particularly for LLMs, while collaborating with engineering and product teams to implement innovations into production.

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Build and deploy AI/ML models for various asset categories, design workflows, and quickly prototype solutions using customer data.