Top Machine Learning Jobs

As a Machine Learning Intern, you'll design, train, and evaluate ML models, work on data preprocessing, develop pipelines, and assist with model deployment while collaborating with experienced engineers.

Designs, trains, deploys, and maintains deep learning models and ML pipelines for electro-optical and infrared image processing. Develops computer vision solutions for space-based defense and intelligence applications, including object detection, segmentation, and tracking. Collaborates with AI and engineering teams, prototypes solutions for operational systems, troubleshoots software, and evaluates emerging ML tools and techniques.

Build and operate production search, ranking, recommendation, and multimodal AI systems for a global jewellery marketplace. Own machine-learning models end to end, including feature development, training, evaluation, deployment, monitoring, and low-latency serving. Develop contextual and conversational search, image-based retrieval, learning-to-rank systems, and embedding pipelines. Define ranking experiments, evaluate LLM behavior, and design graceful recovery from probabilistic failures across multi-tenant storefronts.

Develop and implement frontier-model post-training methods, including reinforcement learning, verifiable rewards, evaluation, and data-centric optimization. Design rigorous experiments across datasets, rewards, environments, and training strategies; investigate model behavior and failure modes; build scalable data-generation and evaluation pipelines; and create benchmarks, rubrics, and scoring systems. Collaborate with researchers, engineers, AI teams, customers, and domain experts while contributing to open-source tools, technical reports, blog posts, and research papers.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Lead a team of 5–6 engineers building Waymo’s marketplace platform and infrastructure. Develop ML infrastructure for training and deploying models that support real-time pricing, rider-vehicle matching, positioning, and supply-demand optimization. Build experimentation, monitoring, analysis, and simulation systems for evaluating deployed models and improving marketplace decisions. The role requires technical leadership, people management, backend programming, and production ML experience.

Own evaluation systems and model-quality improvements for a generative content platform producing AI-generated ecommerce content. Build datasets, rubrics, automated judges, quality gates, and labeled training data from brand feedback. Quantify qualitative improvements, establish approval thresholds, and drive fine-tuning or retrieval experiments into production. The role requires strong software and system design skills, production service ownership, formal machine learning or statistics training, and experience measuring correctness in non-deterministic systems.

Develop and deploy perception, generative AI, and online mapping algorithms for L4 autonomous trucks. Responsibilities include building robust real-time software for safety-critical systems, integrating maps and vehicle sensors, improving mapping performance, leading features from concept through delivery, and coordinating stakeholders. The role requires deep learning model development and deployment, applied research, machine learning algorithm design, Python, and C++, with sensor fusion or online mapping experience preferred.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.

Develop advanced autonomy algorithms for physical AI, including data mining, deep scene understanding, causal modeling of ego-vehicle behavior, and robust identification of complex driving edge cases. Deploy algorithmic solutions for real-world, safety-critical autonomous systems and help establish high technical standards within Uber’s AV Labs team.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Develop autonomy algorithms and foundation models that extract semantic insights from complex driving data. Design scalable machine learning systems, manage sensor dependencies, optimize datasets through data processing and auto-labeling, and deploy models into production. Collaborate with platform, product, and security engineering teams while providing technical leadership. The role requires expertise in Python, Linux, modern ML frameworks, and preferably autonomous driving, safety-critical systems, C++, and high-performance computing.

Develop autonomy algorithms that extract semantic meaning from complex urban driving scenarios and enrich AV data. Design onboard systems, manage sensor dependencies, define evaluation datasets and metrics, and collaborate with platform, product, and security engineering teams to deploy machine learning techniques into production.

Develop autonomy algorithms that add semantic understanding to real-world driving data. Design onboard systems, manage sensor dependencies, define evaluation metrics, and build scalable production solutions for safety-critical autonomous vehicle systems. Collaborate with platform, product, and security engineering teams to deploy onboard systems.

Lead machine learning engineering for fraud prevention and account integrity. Shape technical roadmaps, formulate problems, design and productionize large-scale ML systems, oversee model pipelines and system architecture, conduct A/B testing, and manage rollouts. Mentor and develop MLE talent while applying machine learning, statistics, and optimization techniques to real-world risk and trust challenges.

Build and scale machine learning and backend distributed systems for Uber AI Solutions. Own an AI product vertical end to end, driving technical direction, customer value, revenue, adoption, and market growth. Partner with product and business leaders, guide strategic customer relationships, make decisions in ambiguous environments, and build high-performing teams. The role focuses on production ML, GenAI and LLM systems, evaluation, multimodal data, and other applied AI domains.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Develop novel AI approaches for healthcare using structured and unstructured data, build ML models, conduct experiments, and collaborate with stakeholders.

Leads Lyra Health’s AI/ML engineering roadmap, scales and mentors machine learning teams, and drives production-grade systems for clinical applications. Responsibilities include people leadership, quarterly planning, technical architecture, design reviews, model governance, ML SDLC oversight, and deployment of reliable microservices on Kubernetes. The role partners with product, data science, and clinical stakeholders while ensuring safety, operational excellence, cloud scalability, and compliance for sensitive healthcare data.

Build and operate production small language models for real-time AI governance. Responsibilities include model training, fine-tuning, distillation, high-performance inference, synthetic-data generation, evaluation, telemetry, adversarial testing, failure diagnosis, and closed-loop model improvement. The role also provides technical leadership and partners with product, security, research, and customer-facing teams to deploy reliable, low-latency models at enterprise scale.

Designs and implements production machine-learning workflows for semantic search, classification, entity resolution, retrieval, embeddings, and RAG. Builds reliable AI services, integrates models with data and backend systems, evaluates mission performance, and supports deployment, monitoring, versioning, and MLOps practices. The role also contributes to architecture decisions and may support defense, intelligence, or mission-focused environments.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Designs, trains, evaluates, and deploys AI/ML and deep learning models for mission-critical applications. Analyzes complex datasets, develops predictive algorithms, integrates models into operational systems, and supports testing, validation, deployment, and lifecycle management. Collaborates with engineers, data scientists, and mission stakeholders; documents AI/ML initiatives; researches emerging technologies; and may support LLM, generative AI, NLP, cloud, distributed computing, and MLOps capabilities.

Develop and deploy computer vision modules and machine learning models within AI pipelines. Integrate models with data and software engineering teams, support prototypes, automate data pipelines, build internal tooling, and monitor production performance. The role also involves optimization, infrastructure and security support, staying current with computer vision advancements, and collaborating cross-functionally.