Top Machine Learning Jobs

Build reliable machine learning and data systems that transform assay videos, scientific data, and experimental outcomes into reproducible models, evaluated predictions, and experiment recommendations. Responsibilities include data and feature pipelines, model training and evaluation infrastructure, batch and online inference systems, monitoring, data quality, versioning, observability, and interfaces across computer vision, molecular modeling, active learning, and laboratory workflows.

As a Machine Learning Engineer, you'll own the design, development, and operation of production-grade ML systems in clinical workflows, ensuring model accuracy, reliability, and integration into patient-facing applications.

Develop and deploy predictive and deep learning models, analyze large datasets to surface insights, collaborate with cross-functional teams, produce visualizations and reports, and deliver model-driven features via web interfaces and APIs.

Lead design and implementation of data pipelines and analytic tools for imagery curation and ML model development. Integrate new sensors and data types, perform quantitative analyses, build visualization and monitoring tools, manage labeling campaigns and ETL processes, and provide recommendations for training/test/validation splits to support operational intelligence missions.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Support a US government customer in Chantilly, VA by applying advanced analytics, statistical modeling, and machine learning to cyber-related data. Collaborate with cyber SMEs to build AI/ML models and analytics, work with cyber datasets (e.g., Shodan, Censys.IO), manage and process large volumes of unstructured text, and operate in Linux and Windows environments. Requires active TS/SCI with poly.

Design and run experiments to improve models' perception and agentic capabilities for using software. Build RL training environments, evaluation frameworks, and validation pipelines. Collaborate with model training, infrastructure, and product teams to deploy research into production and measure model performance on complex computer tasks.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Pre-sales Solutions Architect who defines technical strategy and drives Databricks adoption across large enterprise accounts. Leads technical engagements, builds executive relationships and internal champions, mentors team members, and maps ML/AI use cases to Databricks solutions. Supports proofs-of-concept and guides production implementations.

Lead the architecture, development, modernization, scaling, and reliability of enterprise software and ML platform systems. Use AI coding assistants, drive technical decisions, perform code and design reviews, establish engineering best practices, and mentor engineers. The role requires expertise in distributed systems, cloud infrastructure, production services, and high-quality software delivery, with preferred experience in microservices, containers, CI/CD, public clouds, Salesforce, or ServiceNow.

Train and evaluate machine learning and deep learning models, diagnose training and generalization issues, conduct experiments, and develop reusable research tooling. The intern may work on LLMs, transformers, generative AI, fine-tuning, and evaluation workflows while rapidly prototyping with AI-assisted development tools. The role requires advanced quantitative coursework and graduate study in computer science, AI, machine learning, statistics, applied mathematics, data science, electrical engineering, or a related field.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Architects, builds, and operates enterprise AI platform services across model serving, inference optimization, evaluation, agentic infrastructure, APIs, and air-gapped deployments. Leads model development, fine-tuning, productionization, MLOps, security governance, and responsible AI practices. Establishes rigorous evaluation and release criteria, mentors senior engineers, drives technical roadmaps, and bridges AI research with reliable production engineering across cloud, on-premises, and resource-constrained environments.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Leads the design and development of large-scale search and discovery relevance systems, including machine learning infrastructure, serving, pipelines, and AI products. Drives technical strategy, execution, operational excellence, and business impact while partnering with senior stakeholders. Explores deep learning and large language model innovations, improves search quality, resolves complex technical challenges, and recruits and develops high-performing engineering talent.

Develop and deploy large-scale machine learning systems for advertising and recommendation, including user signals, representation learning, ranking, retrieval, prediction, and optimization. Build scalable models, training infrastructure, evaluation tools, and production systems. Improve data and feature quality, model performance, training and inference efficiency, and system reliability. Collaborate with engineering, data, and product teams to move ML innovations from experimentation into production.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Integrate and optimize LLM and VLA models within Bedrock’s Rust-based onboard infrastructure for autonomous heavy machinery. Responsibilities include profiling inference performance, optimizing models with TensorRT and edge runtimes, streaming camera and LiDAR data, eliminating memory, compute, and IPC bottlenecks, and validating improvements on autonomous equipment. The role requires systems programming, GPU and CUDA knowledge, real-time systems expertise, and experience with PyTorch or JAX.

Train and evaluate world models using real autonomous construction-machine fleet data. Explore model architectures, conduct ablations, build evaluation metrics and harnesses, and collaborate with behavior and controls teams to connect model outputs to planning and control on excavators. Present findings and establish reliable foundations for future development.