Top Machine Learning Engineer Jobs

Develop and deploy production-grade machine learning systems that generate insights from structured and unstructured financial data. Responsibilities include NLP, summarization, semantic search, classification, prediction, GenAI, and LLM development; model training, monitoring, optimization, and operationalization; scalable data pipeline integration; architectural contributions; and collaboration with engineering, product, and data science teams.

Develop and deploy proprietary machine learning models for a software product, managing data preparation, experimentation, production deployment, monitoring, and refinement. Build neural networks and learned representations, improve experiment tooling, collaborate with product teams, and write reliable Python. The role requires production machine learning experience, advanced modeling skills, and familiarity with transformers, large language models, and graph-based architectures. Bonus experience includes world models, robotics, simulation, cloud ML services, backend development, SQL, CAD, and geometry computing.

Own the ML platform powering production training pipelines, model serving, deployment, orchestration, observability, and multi-region scaling. Drive major infrastructure initiatives, improve reliability and cost efficiency, lead incident response, collaborate across teams, and set the technical vision for ML operations. The role is hands-on, requiring production distributed-systems expertise, Kubernetes and cloud infrastructure knowledge, technical leadership, mentoring, and strong communication.

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.

Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Own and evolve Braze’s ML platform for training, deploying, serving, and monitoring models at global scale. Lead complex infrastructure initiatives, multi-region model serving, CI/CD, orchestration, reliability, cost optimization, and incident response. Set technical direction, improve production quality, collaborate across teams, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Develop and deploy signal-processing algorithms and machine-learning models that convert wearable sensor data into real-time physiological insights. Responsibilities include data analysis, model training, Python prototyping, optimized C/C++ implementation, embedded firmware integration, and production validation. The role requires collaboration with firmware, hardware, data science, and domain teams to optimize accuracy, power, memory, compute, and latency while conducting rigorous offline, laboratory, real-world, and on-device testing.

Develop, train, deploy, and maintain deep learning models and machine learning pipelines for electro-optical and infrared image processing. Build computer vision solutions involving object detection, image segmentation, and tracking; support rapid prototyping and operational integration. Collaborate with AI and engineering teams, troubleshoot systems, evaluate emerging ML techniques, and maintain production software. The role requires advanced machine learning expertise, strong mathematics, Python proficiency, deep learning library experience, and eligibility for a U.S. Government security clearance.

Builds and operates production AI, machine learning, generative AI, and agentic systems. Responsibilities include designing ML architectures, developing models, implementing MLOps and LLMOps pipelines, monitoring production performance, establishing AI governance, and partnering with data engineering teams. The role sets technical standards, mentors engineers, drives scalable Databricks and Azure solutions, and influences architecture across multiple project and product teams.

Develop and deploy machine learning models and data pipelines for automated decision-making. Responsibilities include collecting and transforming data, exploratory analysis, predictive modeling, production deployment, model serving infrastructure, monitoring drift and performance, and communicating insights to stakeholders. The role requires strong Python and SQL skills, experience with cloud platforms and MLOps tooling, and collaboration across global teams.

Lead the architecture, design, and delivery of complex, scalable software systems for Expedia Group’s flight search platform. Guide engineering teams, develop APIs and data models, integrate responsible AI/ML solutions, improve operational excellence, and collaborate across functions. The role requires expertise in distributed systems, secure software architecture, modernization, and technical leadership, with preferred experience in neural search, vector databases, deep learning, ranking, personalization, and real-time model serving.

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.

Develops multimodal deep learning and reasoning models for Edge AI, including time-series, audio, video, and sensor data. Responsibilities span data collection, annotation, preprocessing, model training, evaluation, supervised fine-tuning, reinforcement learning, knowledge distillation, transformer modification, and multi-agent orchestration. The role also prototypes emerging AI methods, documents research, and publishes results. Requires extensive AI/ML product experience, a related PhD, and a strong publication record.

Develop and evaluate multimodal deep learning and reasoning models for Edge AI, covering data collection, annotation, preprocessing, training, post-training, reinforcement learning, knowledge distillation, and rigorous evaluation. Build agentic and multi-agent reasoning systems, modify transformer architectures, and prototype in Python using PyTorch or TensorFlow. Document findings through reports, publications, and conference submissions while exploring emerging approaches such as world models, JEPA, diffusion, and deep research automation.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Lead the architecture, deployment, and scaling of agentic AI infrastructure for embedded memory design, characterization, DFT, silicon bring-up, and yield optimization. Develop multi-agent LLM workflows, integrate AI systems with EDA tools and enterprise data, automate scan-failure analysis, build predictive yield models, and establish evaluation, testing, and safety guardrails. Collaborate with memory designers, DFT experts, EDA specialists, and infrastructure engineers to deliver production-grade AI engineering solutions.

Develop and productionize time-series machine learning models for energy market forecasting and battery optimization. Responsibilities include designing evaluation frameworks, improving model performance, analyzing market and experiment data, documenting findings, collaborating with software engineers on APIs and deployment, and supporting energy market strategy. This is a full-time, in-office role in Brooklyn, New York.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Design, productionize, and operate machine learning models and rule-based decision systems for credit underwriting. Build scalable pipelines for feature engineering, training, validation, deployment, monitoring, and continuous improvement. Optimize model performance, collaborate with software, credit, product, risk, and data teams, and promote strong engineering and MLOps practices across production ML systems.

Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.

Design, develop, and productionize machine learning models and pricing algorithms for Uber’s large-scale, real-time marketplace systems. The role focuses on deep learning, causal modeling, reinforcement learning, optimization, and online serving at high volume. Responsibilities include collaborating with engineers, product managers, and scientists, shaping technical direction, solving complex marketplace problems, mentoring junior engineers, and monitoring ML solutions across offline and online environments.

Design, productionize, and operate LLM-powered agents on NEO across CIB and Payments. Build production-grade Graph RAG, agent memory, entitlement-aware context, multi-agent workflows, tool integrations, evaluations, guardrails, and observability. Deploy solutions on AWS or Azure while meeting security, resiliency, compliance, and SDLC standards. Partner with technical, product, and business stakeholders to deliver supported AI agent products.

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

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Leads the design and deployment of production-grade AI and machine learning systems for enterprise workflows and analytics. Integrates LLMs and generative AI, builds end-to-end ML pipelines, establishes AI governance and responsible AI practices, and partners with data engineering, governance, and business stakeholders. Serves as a senior technical authority, makes architectural decisions, sets development standards, mentors peers, and translates complex AI concepts for executive and nontechnical audiences.