Top Machine Learning Jobs

Lead the architecture and production operation of Grubhub’s ranking, recommendation, retrieval, and query-understanding systems. Drive improvements in long-term diner value through deep learning, embeddings, LLMs, experimentation, and real-time inference. Establish model evaluation, deployment, observability, and reliability standards across teams. Partner with Product, Search, Ads, and Data Platform, mentor engineers, influence technical strategy, and communicate trade-offs to executive stakeholders.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Own the architecture and technical direction of large-scale ranking, recommendation, retrieval, and query-understanding systems. Lead production machine learning across deep learning, embeddings, LLMs, experimentation, model deployment, and observability. Partner across Product, Search, Ads, and Data Platform, mentor engineers, drive architecture reviews, improve operational standards, and translate technical trade-offs for executive stakeholders.

Build and operate end-to-end machine learning platforms for maritime defense manufacturing. Responsibilities include training and deployment, model serving across cloud, edge, and factory environments, AI workflow development with human review, PLM/MES/ERP integration, governance, drift monitoring, and on-call support.

Develop, train, evaluate, debug, and improve machine learning models for real-world applications. Own the ML lifecycle from dataset construction and experimentation through deployment, while applying deep learning, reinforcement learning, and LLM techniques. Collaborate with engineering and product teams, contribute to scalable ML infrastructure and tooling, define evaluation metrics, analyze errors, and translate research concepts into practical solutions.

Lead development of real-time machine learning and computer vision algorithms for ultrasound imaging in Intuitive’s robotic-assisted biopsy system. Responsibilities include detection, segmentation, classification, temporal tracking, artifact mitigation, image fusion, model optimization, and deployment into high-performance C++ medical-device software. The role also supports system integration, testing, formal design documentation, and patent applications while collaborating with clinical and engineering teams under Good Machine Learning Practice principles.

Develop ML-powered motion planning systems for Level 4 autonomous vehicles. Responsibilities include building learned planners and end-to-end models, applying imitation and reinforcement learning, integrating ML with rule-based and optimization-based planning, training and evaluating models using real-world data and simulation, and deploying production systems under safety, latency, and onboard compute constraints. The role also involves scenario mining, evaluation infrastructure, closed-loop simulation, and collaboration across autonomous driving teams.

Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.

Develop AI/ML features using LLMs, generative AI, RAG, conversational agents, and machine learning models. Build production pipelines, integrate model APIs, evaluate quality and performance, and create secure full-stack services. Collaborate with product, architecture, infrastructure, security, and QA teams while documenting solutions and contributing to code reviews.

Develops and advances machine learning and deep learning models for personalized advertising recommendations. Responsibilities include understanding advertising systems and user behavior data, conducting foundational model research, validating approaches with large-scale compute, analyzing metrics, and collaborating with engineering and product teams to productionize research and deliver measurable business results.

Leads and develops a team of autonomy engineers building multi-agent resource allocation, planning, coordination, and edge AI systems for spacecraft. Provides technical oversight on optimization, reinforcement learning, validation, architecture, and constrained real-time deployment. Owns team roadmaps, delivery, cross-functional dependencies, process improvement, and stakeholder communication while managing hiring, performance, career development, and team culture. The role bridges autonomy expertise with spacecraft constraints including compute, latency, power, and communication limitations.

Lead the technical direction for ML-powered experimentation and predictive insights. Build production systems spanning modeling, LLMs, retrieval, simulation, statistical methods, data pipelines, backend services, and product workflows. Develop traceable, calibrated, privacy-aware predictions; establish rigorous evaluation, backtesting, monitoring, and abstention practices; and translate insights into approval-gated experimentation workflows. Partner with engineering, product, data science, and research teams while providing technical leadership for high-quality production ML.

Designs and develops AI/ML observability systems, large-scale data processing infrastructure, storage, data movement, and machine learning platform capabilities. The role supports reliable training, deployment, monitoring, model evaluation, explainability, and Generative AI use cases. Responsibilities include technical leadership, performance optimization, reliability improvements, database and compiler development, enterprise software engineering, Java application development, SQL, testing, debugging, and documentation.

PhD intern will develop uncertainty-aware algorithms for transformer-based multi-task classifiers, implement model and data changes end-to-end, evaluate experiments, and ensure modeling implementations are correct. The role requires strong Python programming, deep learning framework experience, machine learning theory, transformer debugging with TensorBoard, and software development best practices.

Lead design and delivery of end-to-end revenue optimization products using forecasting, ML/reinforcement learning, and optimization. Partner with cross-functional teams to identify opportunities, prototype solutions, implement analytics, and support deployment while developing the analytics team and driving commercial impact.

Designs, develops, tests, and productizes scalable AI/ML algorithms and models. Responsibilities include model architecture, data pipeline integration, metrics analysis, deployment, scheduling, monitoring, retraining, and iterative improvement. The engineer will support mission-critical government systems, apply software engineering and model evaluation practices, communicate with customers, and potentially lead geographically distributed teams. The role requires advanced machine learning expertise, strong analytical and communication skills, U.S. citizenship, and an active or reinstatable TS/SCI with Polygraph clearance.

Own the quality and behavior of AI-generated interactive content. Build evaluation systems using production creator data, develop agentic ML systems that turn ideas into playable games, debug failures across models and pipelines, run rigorous experiments, and help establish the AI team’s technical standards and direction.

Develop, train, evaluate, and deploy machine learning models using Python and PyTorch for defense applications. Build backend software, data pipelines, APIs, and system integrations supporting ML workflows. Prepare datasets, implement published ML methods, work in Linux-based containerized environments, and collaborate across project teams. Document and communicate technical results while contributing to development processes and operationally relevant prototypes.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Leads engineering and machine learning teams building enterprise-scale AI Search, retrieval, ranking, recommendation, personalization, and RAG systems. Responsibilities include setting architecture and strategy, deploying production ML models, improving relevance and performance, driving reliability and observability, partnering with product and engineering stakeholders, and mentoring technical teams delivering AI-powered experiences to millions of users.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Research Engineer responsible for developing, training, evaluating, and deploying healthcare AI models and pipelines for conversational voice agents. The role focuses on instruction following, tool calling, retrieval, memory, speech and audio research, model optimization, benchmarking, failure analysis, and production integration. Candidates should have 5+ years of experience, including AI/ML engineering or research, production LLM deployment, Python proficiency, and a record of converting research prototypes into reliable measurable improvements.

Leads development of an enterprise Agent SDK and agentic AI platform. Designs production-grade agent capabilities, evaluation systems, model-serving workflows, retrieval-augmented generation, and tool-using agents. Writes code, optimizes performance, establishes engineering standards, and partners across product, data, platform, and application teams. Requires extensive experience building large-scale machine learning systems, cloud and Kubernetes workloads, and reliable generative AI solutions.

Lead ML training performance for large-scale VLM, VLA, foundation-model, and reinforcement-learning systems. Design distributed parallelism, mixed-precision strategies, gradient checkpointing, GPU kernels, profiling workflows, and performance benchmarks. Own RL optimization strategy and roadmap, diagnose bottlenecks across compute and networking layers, partner with research and engineering teams on architecture, and mentor engineers.

Develop and optimize large-scale ML training systems for autonomous-driving foundation, VLM, and VLA models. Responsibilities include distributed training, mixed precision, reinforcement learning performance, GPU kernel development, profiling, benchmarking, and debugging across compute, compiler, framework, and networking layers. The role collaborates with researchers, data engineers, and hardware engineers to improve training efficiency, scalability, and cost.