Top Machine Learning Jobs

The analyst will leverage multi-INT data for curation and development of labeled data for Machine Learning, focusing on SAR imagery. Responsibilities include recommending curation strategies and addressing data gaps for diverse datasets.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.

Develop applied AI, machine learning, optimization, and statistical models to solve complex business problems for Chase Finance and Business Management. Analyze large datasets, identify actionable insights, contribute throughout the modeling lifecycle, and communicate findings to senior stakeholders. The role also involves serving as a subject matter expert, advising partners, and introducing innovative analytical approaches.

Design, build, and operate production-grade agentic AI systems for ServiceNow. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, RAG and search pipelines, safety guardrails, observability, model integration, evaluation frameworks, and cost and latency optimization. The role also provides technical leadership through architecture decisions, code reviews, and coaching.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Develop and productionize machine learning systems for national security applications. Responsibilities include translating customer needs into technical requirements, architecting scalable systems, building backend services and APIs, designing responsive interfaces, deploying applications across cloud and edge environments, optimizing CI pipelines, and applying software engineering best practices. The role requires model development with Python frameworks, NVIDIA GPU experience, Docker, databases, frontend technologies, testing, observability, and performance optimization.

Develop and productionize machine learning systems for personalization, recommendation, ranking, retrieval, traveler understanding, and sequential modeling. Lead end-to-end scientific work including problem formulation, data exploration, feature engineering, model development, experimentation, evaluation, and operationalization. Collaborate with engineering, product, and business stakeholders on system and API design, mentor other scientists, influence technical direction, and apply advanced deep learning and foundation-model techniques at scale.

Design and deploy scalable machine learning solutions for personalization, recommendation, ranking, retrieval, and traveler understanding. Lead end-to-end scientific work spanning data exploration, feature engineering, model development, experimentation, evaluation, and productionization. Partner with engineering, product, and business teams on system and API integration, improve model reliability and operational quality, influence technical direction, mentor scientists, and apply advanced methods including neural recommenders, foundation models, LLMs, and representation learning.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Develop and optimize search relevance for an enterprise question-answering platform. Build ranking features and frameworks, enable parallel ranking experiments, and create metrics to evaluate ranking performance. Analyze query logs to identify search-quality improvements and apply information retrieval, machine learning, text processing, and natural language understanding techniques. Collaborate with product, UX, customer success, and engineering teams to deliver improved answer quality.

Develop machine learning and generative AI capabilities for conversational enterprise search, including multi-turn question answering, information retrieval, ranking, evaluation, and LLM-enabled features. Build scalable platform frameworks that help product teams prototype and deploy search applications. Establish metrics for search quality, security, privacy, and scalability while collaborating with product, design, infrastructure, data science, and engineering teams. Contribute to a high-performing and inclusive engineering culture.

Develop search relevance algorithms for an enterprise question-answering platform. Create and optimize ranking features, build frameworks for diversified ranking, design tooling for parallel ranking experiments, and develop metrics to evaluate search quality. Analyze query logs to identify improvements and collaborate with product, UX, customer success, and engineering teams to deliver accurate answers across heterogeneous knowledge sources.

Build and operate production small language models for real-time AI governance. Responsibilities include model training, fine-tuning, distillation, high-performance inference, synthetic-data generation, evaluation, telemetry, adversarial testing, failure diagnosis, and closed-loop model improvement. The role also provides technical leadership and partners with product, security, research, and customer-facing teams to deploy reliable, low-latency models at enterprise scale.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Design, build, and deploy core ML systems and components for an AI agent platform, including search ranking, proactive suggestions, orchestration, routing, and planning; integrate cutting-edge LLMs and ensure high-quality user experiences while working autonomously in a fast-paced, collaborative environment.

Perform data analysis and test & evaluation of machine learning algorithms; develop algorithms for physical and autonomous systems; transition algorithms to software and embedded systems; work with stakeholders to present results and solutions; research new ML and data-analysis methods; handle large, multidimensional datasets and data pipelines.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, quality engineers, and product owners to design, implement, test, and deliver scalable software. Apply high-performance algorithms, core AI/ML techniques, object-oriented programming, unit testing, profiling, and code tuning to improve user experiences and workflow efficiency.

Build anomaly detection metrics and monitoring tools to identify fraud; analyze large structured and unstructured datasets to detect suspicious activity and trends; develop ML algorithms and run experiments to optimize detection; visualize and present insights; partner with data architects, engineers, and cross-functional teams to improve fraud mitigation processes.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.