Top Machine Learning Jobs

Build machine learning infrastructure spanning large-scale data generation, multi-GPU LLM training, training and inference optimization, and production deployment and monitoring. The role also contributes to AI detection research, publications, and technical innovation. Engineers will develop scalable data pipelines, manage distributed systems, deploy efficient LLM inference services, and collaborate across research and engineering teams in an in-person Brooklyn office.

Build and evaluate machine learning models that generate high-definition maps from raw sensor data. Develop statistical evaluation methods, scalable distributed data-processing pipelines, and backend infrastructure for global mapping. Collaborate with onboard engineering teams to improve mapping formats, manage the full modeling lifecycle, and measure system-wide impact. The role emphasizes autonomous vehicle mapping, large-scale systems, quantitative experimentation, and end-to-end software engineering ownership.

Lead the technical roadmap, architecture, and execution of machine-learning behavior prediction and planning systems for autonomous vehicles. Develop production ML algorithms that generate driving trajectories, guide architecture reviews, collaborate with perception, planning, and research teams, and mentor engineers. The role requires hands-on technical leadership across model development, evaluation, deployment, optimization, and scalable software infrastructure.

Staff-level ML and agent systems engineer responsible for production model lifecycles, multi-step agentic systems, evaluations, retrieval and context engineering, and model cost and latency optimization. The role owns technical direction for Code Understanding products, improves model and agent quality, establishes evaluation and monitoring standards, mentors engineers, collaborates with customers, and delivers reliable AI-powered developer experiences at enterprise scale.

Develop and maintain scalable, secure data pipelines and platforms for public health data ingestion, processing, integration, analysis, surveillance, and reporting. Collaborate with data stewards and stakeholders to apply health informatics standards, governance, quality, and security practices. Support simulation and predictive modeling, scenario analysis, technology selection, and translation of analytical outputs into actionable insights. Mentor junior staff and manage cross-functional projects.

Designs, develops, tests, deploys, and improves machine learning models for business and mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and retraining models, monitoring performance, applying evaluation protocols, and overseeing system integration. The role requires knowledge of model architecture, infrastructure, data engineering, governance, and scalable AI/ML solutions, with potential leadership of distributed teams and customer engagement.

Develop and productionize machine learning and AI solutions for enterprise payroll products. Responsibilities include preparing large-scale HR and payroll data, building and evaluating models, prompts, and frameworks, and applying deep learning, NLP, generative AI, and agent technologies. Collaborate with product managers, software engineers, and data scientists to deliver scalable, reliable, and responsible AI capabilities supporting predictive analytics, recommendations, and payroll automation.

Train and evaluate learned manipulation policies for autonomous excavators using real fleet data and simulated rollouts. Explore diffusion and flow-matching architectures, conditioning, and hyperparameters; build evaluation pipelines for task success, smoothness, safety, and operator-likeness; investigate failure modes and sim-to-real issues; and contribute reproducible code while collaborating with behavior, controls, simulation, and autonomy teams.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Leads enterprise AI and machine learning strategy, architecture, governance, evaluation, and business value realization. Designs and delivers scalable ML solutions, establishes technical standards and reusable capabilities, influences AI/ML direction across multiple product areas, and guides large cloud-based projects from conception through deployment. Collaborates across teams, shapes technology roadmaps, communicates with nontechnical stakeholders, and drives AI-enabled process transformation using modern software engineering, AWS, DevOps, and distributed delivery practices.

Build and improve machine learning systems for ad ranking, relevance, personalization, and optimization. Responsibilities include feature pipelines, model training and evaluation, active learning, LLM-assisted labeling and synthetic data generation, experimentation, and low-latency production inference. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while maintaining reliability, latency, and data quality.

Build and operate scalable infrastructure for Zipline’s machine learning lifecycle, including data ingestion, dataset management, training, evaluation, monitoring, and deployment. Improve reproducibility, observability, orchestration, and performance across autonomy-focused ML workflows. The role requires production software engineering in Python, cloud and on-premise infrastructure experience, Kubernetes, ML frameworks, and strong ownership of secure, mission-critical systems.

Develop and deploy enterprise-scale AI and machine learning solutions for secure government and intelligence missions. Build LLM-powered workflows, agent-based automation, data pipelines, and production ML systems. Integrate AI into operational platforms, conduct model evaluation and testing, optimize workflows, and ensure scalability, reliability, security, and governance. Collaborate with mission users, analysts, software developers, and leadership while providing technical guidance and mentoring.

Develop and deploy AI/ML solutions for classified government and intelligence workflows. Responsibilities include building LLM-powered and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role requires collaboration with mission users, analysts, developers, and leadership while ensuring scalable, reliable, and secure production solutions.

Design, build, optimize, and scale machine learning infrastructure for training, evaluating, deploying, and serving large language models. Develop reusable workflow automation abstractions, distributed training and inference pipelines, model evaluation and monitoring frameworks, and latency optimizations. Collaborate with machine learning, data infrastructure, analytics, engineering, and product teams while establishing best practices for ML and data engineering.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Leads architecture for scalable, reliable agent-based AI products. Partners with product, engineering, data science, clinical, and customer teams to translate needs into architectures, prototypes, technical recommendations, and delivery plans. Conducts technical discovery, engages early-stage customers, evaluates adoption barriers, and guides solutions from proof of concept to production. Requires strong architecture judgment, communication, and experience shipping large-scale AI systems, with healthcare, deep learning, or NLP experience preferred.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Develop high-performance low-level kernels for custom ML accelerators using AI agents and domain-specific languages. Ensure kernel correctness and numerical accuracy, analyze performance bottlenecks, and build automated harnesses to recursively improve kernel generation. The role involves transformer inference optimization, accelerator architecture, numerical format tradeoffs, and programming in Python or C++.

Design and develop a high-performance, multi-tenant ML training platform for autonomous vehicle systems. Responsibilities include building orchestration, control plane, observability, data and model pipelines, performance optimization, profiling tools, and scalable infrastructure. The role provides technical direction, collaborates across ML, infrastructure, autonomy, and safety teams, and promotes engineering excellence and reusable tooling for machine learning engineers.

Design and maintain data pipelines and BI infrastructure, ensure data quality, optimize SQL and transformations, build and validate predictive and ML models, support deployment and BI integration, and collaborate with stakeholders to deliver scalable analytics solutions.

Develop and deploy geospatial AI/ML capabilities for DoD and intelligence customers. Apply machine learning, data analytics, computational modeling, clustering, and statistical methods to structured and unstructured data to generate predictive and prescriptive intelligence insights. Integrate diverse data sources, optimize analytical workflows, support operational missions, lead projects, and translate research and commercial practices into actionable solutions for intelligence analysis and resource allocation.

Conduct machine learning and AI research applied to Pinterest problems. Build and improve large-scale production ML systems spanning algorithms, infrastructure, data engineering, training, inference, and product integration. Develop clean code, validate AI-generated outputs, use AI-assisted engineering tools, investigate bugs, and independently deliver projects while collaborating with engineering and research teams.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.