Top Machine Learning Jobs

Designs, develops, tests, and optimizes AI and machine learning systems across business problems. Applies machine learning, deep learning, transformer architectures, statistical reasoning, and NLP to build models and pipelines. Uses cloud platforms and distributed computing, collaborates with MLOps engineers on deployment, and follows software engineering practices. Responsibilities span the data science lifecycle, including data preparation, feature extraction, model development, deployment, maintenance, and communication with nontechnical stakeholders.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Lead the technical direction and development of production machine learning and agentic systems for global trade risk, compliance, and enforcement. Responsibilities include designing data-to-decision pipelines, adapting and post-training LLMs, building evaluation and observability systems, improving model performance, mentoring engineers, and partnering with product, customers, and cross-functional teams to ship AI capabilities.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develops research methods for context discovery, retrieval, multimodal representation, reasoning, tool calling, metadata management, and trustworthy AI data pipelines. Builds and evaluates foundation models across language, vision, time series, and scientific applications. The role combines applied research with hands-on engineering, including MLOps, agentic AI, knowledge-infused learning, pipeline optimization, and collaboration with software teams and external partners.

Develop online and offline mapping algorithms for L4 autonomous vehicles. Design, train, optimize, and deploy machine learning and deep learning models using camera and LiDAR data, PyTorch, Python, C++, and TensorRT. Build robust real-time software for safety-critical, resource-constrained systems, lead applied research from concept through productization, and contribute to technical direction and cross-functional delivery.

Develops and deploys algorithms that generate physiologic insights from wearable health sensor data. Responsibilities include coding scalable processing pipelines for streaming and historical data, designing and testing machine learning workflows, conducting statistical analyses, visualizing results, documenting work, and collaborating across engineering teams to improve medical device products.

Develop and maintain scalable infrastructure for deploying AI and machine learning models. Build automation for model evaluation and deployment, optimize inference performance, and implement monitoring and alerting systems. Troubleshoot infrastructure issues, collaborate with ML scientists and cross-functional teams, and contribute to code reviews, design discussions, and architecture planning. Requires a relevant master’s degree, at least one year of related experience, and training or experience with Python, machine learning, generative AI, SQL, system programming, and cloud computing.

Leads software engineering teams developing production-grade AI and machine learning solutions for industrial environments. Responsibilities include identifying AI opportunities, guiding architecture and technology decisions, modernizing industrial products, ensuring scalability, cybersecurity, reliability, and maintainability, and collaborating across engineering, data science, product, commercial, and field teams. The role also mentors engineers, drives technical roadmaps, and oversees cloud-native ML systems, including predictive maintenance, diagnostics, forecasting, LLM assistance, and network optimization.

Deploy Fastino language models into customer production environments by building integrations, data pipelines, evaluation systems, and services. Fine-tune and assess models for accuracy, latency, and cost; troubleshoot authentication, networking, GPU performance, inference, and data quality issues. Own technical delivery from initial discussions through launch, create reusable tools and SDK improvements, and relay customer feedback to research and product teams.

Leads forensic automation and cognitive technology engagements, delivering machine learning, data science, predictive modeling, and statistical solutions for enterprise clients. Designs scalable pipelines and reusable analytics components, integrates cloud and enterprise data, presents findings to executive and legal audiences, manages teams, and provides client and internal training.

Research and develop software prototypes for space-based object visualization and data exploitation. Responsibilities include algorithm development, AI/ML model training and evaluation, workflow and requirements definition, technical documentation, Agile Scrum execution, collaboration, code reviews, and mentoring junior engineers.

Develop and evaluate real-time machine learning algorithms for audio processing applications, including voice pickup and hearing augmentation. Prototype solutions, integrate them into existing platforms, curate internal resources, present findings, and contribute to customer products, patents, and research publications. The role involves collaboration across machine learning, digital signal processing, software engineering, and psychoacoustics.

Leads the design, experimentation, productionization, and optimization of machine learning, deep learning, generative AI, LLM, and agentic systems. Develops Python-based proofs of concept and production solutions, evaluates and tunes models, integrates generative AI into ML platforms, improves system performance, and partners with product and engineering teams. Acts as an ML subject matter expert while mentoring practitioners and influencing product and technical decisions.

Lead the architecture and development of agentic, conversational, and information-retrieval experiences for a consumer voice product. Design and ship ML-based user experiences, build model development and evaluation infrastructure, optimize inference for latency and reliability, and guide a focused multidisciplinary team. The role partners closely with founders and backend, iOS, ML, infrastructure, and design engineers.

Designs and deploys advanced machine learning and deep learning models, leads improvements to AI/ML systems, oversees algorithm development through integration and sustainment, analyzes complex datasets, and communicates technical insights to stakeholders. The role requires extensive experience with algorithms, MLOps pipelines, statistical analysis, experimental design, data storytelling, data governance, and data quality, plus an active Top Secret clearance and eligibility for SCI.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Designs, deploys, validates, and operationalizes AI/ML solutions for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making. Builds models, NLP capabilities, data pipelines, APIs, dashboards, and MLOps workflows across hybrid cloud and on-premises environments. Manages the model lifecycle, including governance, validation, monitoring, remediation, and production support. Partners with engineering, operations, risk, cybersecurity, architecture, data science, and product teams while communicating technical outcomes and risks to technical and executive stakeholders.

Lead the technical strategy and development of machine learning solutions for Uber’s homepage personalization, including sequential recommendation, intent-aware ranking, LLM-based systems, and agentic AI. Partner with product teams to improve engagement and business outcomes, influence technical direction across teams, and mentor machine learning engineers.

Architect and lead large-scale machine learning systems, including LLM infrastructure and production deployments. Define ML architecture, MLOps, governance, observability, security, performance, and cost standards. Partner with stakeholders to translate customer needs into scalable solutions, influence AI strategy, mentor engineers, and guide adoption of modern AI techniques such as RAG, vector search, prompt engineering, and agent workflows.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Summer intern develops real-time machine learning systems and GPU-accelerated digital signal processing algorithms for streaming RF data. Responsibilities include filtering, spectral analysis, waveform generation, data pipeline development, ML model training, outcome analysis, system integration, and deployment using DevOps and MLOps tools. The intern collaborates with engineers on communications, radar, and other defense and intelligence applications.

Designs, develops, and productionizes machine learning, deep learning, generative AI, LLM, simulation, and optimization solutions. Responsibilities include building and scaling model-serving pipelines, improving accuracy and performance, integrating models into production, developing project architectures, defining ETL requirements, and collaborating with data scientists, engineers, analysts, and stakeholders. The role requires office attendance five days per week in Arlington, Virginia.

Build and scale end-to-end machine learning infrastructure for computer vision and multimodal AI systems. Responsibilities include developing training and deployment pipelines, managing GPU cloud infrastructure, optimizing low-latency model serving, and establishing monitoring and evaluation frameworks for reliable production performance. The role partners with research, computer vision, and product teams to operationalize models across warehouse environments.

Supports the design, development, testing, evaluation, and deployment of AI and machine learning solutions for network management and digitization platforms. Responsibilities include data preprocessing, feature engineering, software integration, prototyping, documentation, code reviews, technical presentations, and researching emerging AI/ML technologies.