Top Machine Learning Jobs

Build and operate scalable production data pipelines and agentic workflows for autonomous-driving foundation models. Automate data discovery, selection, curation, and mining; connect LLMs and vision-language models to data tools; monitor workflows and recover from failures. Partner with machine learning engineers and data analysts to translate model limitations into reusable data solutions, define acceptance criteria, and improve training-data coverage through model feedback and human review.

Lead evaluation and automatic optimization for AI models and agents, including benchmarks, LLM judges, simulations, trajectory analysis, regression tests, synthetic data pipelines, and post-training systems. Diagnose failures and convert findings into improvements to prompts, context, tools, rubrics, and agent harnesses. Apply current LLM research to production, collaborate with retrieval and product teams, mentor engineers, and guide Nova’s technical direction.

Design and build production machine learning systems for personalization, recommendations, and campaign optimization. Own the full model lifecycle, including experimentation, feature engineering, distributed training, deployment, latency monitoring, and online testing. Build scalable data pipelines and model-serving architectures with Databricks, Spark, AWS, Ray, and Kubernetes. Partner with platform teams, mentor engineers, and guide technical direction for machine learning capabilities.

Lead development of Iterable Nova’s retrieval and search systems for AI agents. Build hybrid retrieval across structured and unstructured data, semantic query understanding, entity and embedding pipelines, multi-hop search subagents, and evaluation frameworks. Improve relevance and grounding through ranking metrics, LLM judges, human labeling, regression testing, and online experiments. Mentor engineers and guide technical direction while taking systems from research through reliable production deployment.

PhD intern developing controllable generative world models and interactive simulations for autonomous driving. Responsibilities include implementing multimodal conditioning, diffusion and flow-matching steering, representation alignment, realism critics, and closed-loop scenario generation. The role requires writing scalable, tested JAX code and training models on large TPU clusters while collaborating with simulation and foundation-model research teams.

Designs, develops, deploys, and optimizes machine learning models and scalable data pipelines using Python, PySpark, and Azure technologies. The role processes large datasets, implements MLOps practices, develops telemetry-driven insights, and collaborates with engineers, data scientists, product owners, and business stakeholders. Responsibilities include model evaluation, feature engineering, visualization, dashboards, code reviews, technical design, and communicating recommendations to technical and non-technical audiences.

Design and deploy large-scale machine learning systems for offsite advertising, including targeting, ranking, prediction, bidding optimization, retrieval, and campaign performance. Apply deep learning, LLMs, embeddings, and agentic AI while building scalable ML infrastructure and production systems. Define success metrics, conduct experimentation, drive initiatives from concept to production, collaborate across functions, deliver measurable business impact, and mentor engineers.

Leads AI/ML strategy and initiatives, overseeing projects from problem definition and data collection through model development, deployment, monitoring, and improvement. Builds and mentors AI/ML teams, evaluates emerging technologies, collaborates with software engineering and DevOps teams, manages budgets and timelines, communicates findings to stakeholders, and drives innovation through research, patents, and publications. The role requires expertise in advanced AI/ML techniques, generative AI, cloud platforms, big data, and MLOps.

Leads and coaches a global AI/ML engineering team while remaining hands-on in designing, building, and shipping production solutions. Owns data pipelines, models, inference services, agentic and LLM components, APIs, dashboards, and applications. Sets engineering standards for architecture, testing, CI/CD, and production health; partners with MLOps, product, and program teams to deliver reliable AI/ML products. Develops engineers, supports hiring, and drives technical quality across data science, software, and full-stack delivery.

Design and implement machine learning algorithms for recommendation, trust and safety, profile, chat, growth, or revenue applications. Build end-to-end ML workflows and production solutions under senior engineer guidance, collaborate on large-scale product challenges, and present work during Tinder’s hackathon.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing algorithms and data pipelines, operating distributed systems and cloud services, automating testing and deployment, maintaining models, and applying responsible AI, governance, security, and explainability practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Responsibilities include model development, feature selection, tuning, validation, data pipeline construction, cloud deployment, Kubernetes operations, CI/CD, monitoring, responsible AI, and production troubleshooting. The role collaborates with Product, Data Science, and Agile engineering teams to deliver resilient large-scale AI solutions.

Own and evolve Braze’s ML platform for globally scaled training pipelines, model serving, deployment, orchestration, and observability. Lead complex infrastructure initiatives, improve reliability and cost efficiency, manage incident response, and establish production quality standards. Partner across teams, shape technical strategy, mentor senior engineers and data scientists, and connect platform decisions to customer and business outcomes.

Design, build, deploy, and optimize large-scale machine learning models and infrastructure for Uber Maps, including high-throughput, low-latency prediction systems and APIs. Develop distributed ML pipelines, improve routing and ETA applications, explore optimization solutions, and provide technical leadership. Collaborate with product managers, scientists, designers, and engineers to solve complex, real-world mobility problems.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Design, build, optimize, and scale machine learning models and systems for quantitative investment research and production. Collaborate with quantitative researchers and engineers on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries. Improve training speed, scalability, reliability, performance, and cost efficiency across high-performance computing environments while translating research needs into robust technical solutions.

Lead and grow the AI Platform engineering team while remaining hands-on in production machine learning. Own matching, ranking, retrieval, personalization, agentic workflows, trust and safety, evaluation, and model quality, cost, and latency. Hire and coach engineers, set technical direction, translate business goals into roadmaps, and ship systems that improve recruiting outcomes. The role requires production ML or LLM experience, strong evaluation and data skills, technical leadership, and weekly coding involvement.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Lead Adyen’s Personalize Intelligence team developing machine-learning systems for real-time Checkout optimization and AI products. Provide technical leadership on architecture, reliability, and scalable services; coach machine learning scientists and data engineers; define goals and product roadmaps with Product; collaborate across Design, Engineering, Data, and leadership; monitor system performance; foster an inclusive, high-performing culture; and recruit new team members.

Build and deploy product-focused machine learning systems for Uber One, including targeting, personalization, uplift modeling, offer allocation, ranking, and messaging. Own models through experimentation, real-time serving, batch processing, monitoring, and retraining. Partner cross-functionally to translate membership growth challenges into measurable ML solutions and optimize consumer engagement, retention, and incremental business impact.

Lead applied AI and machine learning research for Pinterest’s generative recommendation and reinforcement learning systems. Architect, build, and deploy novel ML solutions at scale, collaborate across product, engineering, and data science, identify research opportunities, shape long-term strategy, mentor others, and translate advanced research involving recommendation systems, LLMs, and VLMs into production impact.

Build and operate scalable machine learning infrastructure, pipelines, recommendation engines, and agentic AI capabilities. Responsibilities include distributed data processing, feature engineering, model training and deployment, low-latency inference, LLM observability, feature-store architecture, and production MLOps. The role partners with engineering, product, SRE, data, and compliance teams to deliver highly available ML systems, mentor engineers, and guide technical and governance decisions.

Build and optimize machine learning models, ranking systems, retrieval systems, and personalized Search experiences for Spotify. Develop production-ready solutions from experimentation through deployment, explore conversational AI and agentic approaches, evaluate models through experimentation and performance metrics, and contribute to scalable machine learning infrastructure. Collaborate with engineering, product, design, and research teams to improve discovery for millions of listeners.

Own the ML platform layer connecting data science and infrastructure. Responsibilities include managing the feature store, building self-service training and deployment platforms, standardizing retraining, scaling batch and online inference, implementing monitoring, and establishing standards for model and feature versioning, lineage, testing, and documentation. The role partners closely with data scientists and backend engineers to improve model delivery speed, reliability, and independence across multiple teams.

Design and integrate production-grade AI/ML models, agents, orchestration workflows, RAG pipelines, vector databases, and MCP tool integrations for USCYBERCOM. Evaluate and optimize models, implement governance guardrails, build automated quality and safety evaluation pipelines, support rapid prototyping, and translate research into secure enterprise capabilities. The role also requires collaboration with data scientists and mission analysts, operational risk management, and research into emerging AI technologies.