Top Machine Learning Jobs

Lead development and production deployment of end-to-end driving, VLA, and world-model systems for L4 autonomous vehicles. Define scalable autonomy architectures, adapt foundation models, improve training and evaluation efficiency, and drive technical initiatives from concept to deployment. The role also includes mentoring engineers, shaping technical direction, and contributing to the founding autonomy team.

Leads end-to-end machine learning model development for banking and digital lending use cases, including credit risk, collections, fraud detection, segmentation, and personalized financial services. Responsibilities include feature engineering, validation, deployment, monitoring, documentation, model review, stakeholder collaboration, production implementation, innovation, and mentoring junior data scientists and engineers.

Lead the architecture, deployment, and optimization of scalable machine learning serving systems for real-time and batch workloads. Build CI/CD pipelines, monitoring, model versioning, and reliable MLOps infrastructure. Manage AWS cloud infrastructure, collaborate with data scientists and engineering teams, and support production systems through on-call rotation. The role also involves improving observability, reproducibility, security, and deployment reliability, with opportunities to deploy LLMs and distributed ML systems.

Design, develop, evaluate, and refine machine learning models using Python. Manage training data with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and deliver data-driven insights. Collaborate remotely with cross-functional contributors and support deployment or operationalization of models in cloud or enterprise environments.

Build and deploy advanced machine learning and agentic AI systems that improve Airbnb’s customer service experience. Responsibilities include developing AI assistants, integrating ML products, optimizing model performance, applying LLM techniques such as fine-tuning and RAG, building multi-agent architectures, and shipping scalable production ML systems. The role collaborates closely with Engineering, Product, and Design teams and contributes to emerging AI initiatives from concept through production.

Own performance optimization for foundation-model training across thousands of accelerators. Diagnose and resolve bottlenecks across data paths, kernels, parallelism, communication, observability, and long-running stability. Partner with modeling, infrastructure, simulation, data-mining, and robotics teams; establish reusable performance improvements and technical direction. Build profiling infrastructure and agentic automation to reproduce jobs, verify numerical parity, detect regressions, and automate optimization workflows.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

Build, deploy, and improve production machine learning systems, including models, data pipelines, AI services, and monitoring workflows. Responsibilities include fine-tuning and evaluating models, designing scalable training and inference infrastructure, optimizing performance and cost, integrating APIs and internal tools, and partnering with product, research, and design teams to deliver reliable features. The role requires hands-on software delivery experience, cloud knowledge, and practical application of machine learning or analytics-heavy systems.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Build, train, deploy, and optimize production-scale machine learning models and neural networks. Design scalable data pipelines, distributed training systems, backend improvements, and high-throughput inference solutions. Conduct metric-driven research, improve model accuracy and performance, maintain secure and high-quality code, mentor junior engineers, and collaborate cross-functionally. The role focuses on deep learning, computer vision or NLP, and taking ML products from concept through production.

Develop and deploy production-scale machine learning models, from data gathering and neural network design through training, optimization, scalable deployment, and continuous performance improvement. Build highly available distributed systems and backend APIs, improve engineering standards and tooling, conduct metric-driven research, mentor engineers, collaborate cross-functionally, and help define product strategy and roadmaps.

Develop and productionize deep learning models across the full machine learning lifecycle, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines for large datasets, collaborate with Backend, DevOps, and data-labeling teams, and apply security and information-protection practices. The role requires strong Python and machine learning framework expertise, production deployment experience, and familiarity with distributed data technologies.

Develop and deploy deep learning models into production at scale. Responsibilities include designing neural networks, collecting and refining data, training and tuning models, building scalable data pipelines, deploying high-throughput systems, analyzing results, improving model performance, and collaborating with Backend, DevOps, and data-labeling teams. The role also includes applying secure coding practices and protecting information assets.

Leads end-to-end machine learning development, including neural network design, data collection, model training, tuning, deployment, monitoring, and optimization at production scale. Builds scalable code and data pipelines, develops novel ML algorithms, improves backend systems and engineering processes, conducts metric-driven research, mentors engineers, and contributes to product strategy and roadmap planning.

Develop and productionize machine learning models at scale, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines and secure backend systems, conduct metric-driven research, improve engineering practices, mentor junior engineers, and collaborate cross-functionally. The role requires expertise in modern ML frameworks, distributed systems, Python or shell scripting, and a machine learning specialization such as computer vision or NLP.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable production solutions. Requires strong expertise in machine learning algorithms, software architecture, data engineering, model validation, and system integration. The role may lead distributed teams and requires an active TS/SCI clearance with polygraph.

Design, deploy, and integrate machine learning models into digital-twin and multiphysics simulation environments for laser-driven fusion systems. Build physics-informed models, surrogate and reduced-order models, ML pipelines, anomaly detection, uncertainty quantification, Bayesian optimization, and reinforcement-learning workflows. Collaborate with physicists and engineers to accelerate simulations, optimize system performance, support predictive diagnostics, and establish reproducible engineering practices.

Build and deploy machine learning systems for ransomware detection, suspicious sessions, user behavior analytics, and anomaly detection. Own high-volume ML pipelines using Spark, GCP Dataflow, and Vertex AI; develop feature stores; operate production models; and collaborate with platform, engineering, and product teams. The role includes on-call support and requires strong Python, cloud ML, MLOps, model evaluation, and production deployment experience.

Develop and deploy machine-learned interatomic potentials for PFAS substitution. Responsibilities include generating and curating first-principles training data, building reproducible cloud and HPC workflows, training and validating ML force fields, running molecular dynamics and quantum chemistry campaigns, predicting physical properties, extending models to condensed-phase and process chemistries, and communicating model uncertainty and domain limitations.

Develop and deploy production AI/ML and generative AI solutions for FDA modernization initiatives. Build RAG pipelines, LLM applications, machine learning models, APIs, data pipelines, and MLOps/LLMOps capabilities. Evaluate model quality, implement responsible AI safeguards, monitor production systems, and integrate AI services into enterprise applications. Collaborate with data, software, cloud, and product teams in an Agile environment while documenting solutions and mentoring less-experienced engineers.