Top Machine Learning Engineer Jobs

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.

Build and maintain large-scale machine learning infrastructure, including training-data pipelines, distributed model-training workflows, and orchestrated ML pipelines. Improve dataset validation, monitoring, testing, reproducibility, and system performance while partnering with ML engineers and researchers to translate research ideas into reliable production systems.

Develop and productionize machine learning classifiers and evaluation systems for assessing model behavior. Responsibilities include supervised fine-tuning, distillation, rigorous evaluation, inference optimization, deployment, observability, reliability, and ongoing improvement. The role partners with researchers and software engineers to translate open-ended research needs into measurable ML objectives and scalable production systems, balancing model quality, latency, compute utilization, and cost.

Develop embedded machine learning and robotics solutions, including control, computer vision, sensor fusion, detection, and regression algorithms. Responsibilities include writing production Python and C++ code, implementing ROS-based robotic systems, building ML training and deployment pipelines, creating efficient inter-process communication libraries, and optimizing ML software for embedded and hardware-accelerated devices.

Develop reusable RAG pipelines and MLOps capabilities for domain-specific knowledge repositories. Build document ingestion and processing workflows, integrate embedding models, vector databases, and LLM generation, and evaluate response quality. Automate model training, validation, deployment, monitoring, and data-source configuration. Collaborate with software developers, AI/ML engineers, and systems architects to deliver scalable, secure solutions for customer projects.

Own the roadmap for machine-learning-powered pickup and dropoff features. Train, evaluate, and deploy models for occupancy detection, rider interactions, pullover selection, and lost-and-found. Design onboard/offboard fallback systems, build distributed vehicle-to-cloud services, integrate end-to-end user-facing features, and measure impact through metrics, simulation, offline evaluation, and live experiments.

Develop physics-informed machine learning models for electronic systems. Build automated electrical, thermal, and electromagnetic simulation workflows, generate datasets, create surrogate models, and validate predictions against solvers and experiments. Develop active learning and optimization workflows while owning reproducible pipelines for data generation, training, evaluation, and deployment. Collaborate with hardware and machine learning teams to accelerate electronics development.

Design, train, and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, develop generative augmentation methods, define sensor realism metrics, reduce sim-to-real gaps, and mentor engineers and interns.

Develop and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, apply generative augmentation, define sensor realism metrics, close sim-to-real gaps, and mentor engineers and interns.

Design, develop, and operationalize AI and machine learning solutions for production applications and data workflows. Build data and model pipelines, implement MLOps and LLMOps practices, support generative AI and RAG systems, and contribute to CI/CD, observability, responsible AI, governance, and model lifecycle management. Collaborate with data science, software engineering, and cloud teams in secure federal environments.

Build and deploy NLP and LLM-based machine learning models for an AI-powered risk intelligence platform. Responsibilities include text classification, entity extraction, clustering, topic detection, RAG, agentic workflows, semantic search, model evaluation, production monitoring, retraining, and scalable ML service deployment. Collaborate remotely with data engineers, backend engineers, and product stakeholders on high-volume, multilingual text data.

Leads modeling strategy for Riot’s anti-cheat products, designing adaptive classifiers and anomaly-detection systems that identify bots, cheaters, and behavioral threats. Builds production-ready ML pipelines, prototypes novel approaches, establishes evaluation standards, improves feature and architecture strategies, and translates technical tradeoffs for cross-functional audiences. The role also mentors junior staff, supports recruiting, and bridges machine-learning research with scalable production systems.

Own machine learning projects from scoping through production, developing reliable multi-agent AI systems. Design agent workflows, safety limits, memory and context management, model routing, evaluation pipelines, benchmarks, metrics, error monitoring, prompt structures, search and indexing systems, and document-processing validation layers. Conduct rigorous ML experiments, test systems end to end, write technical specifications, and communicate findings. The role is full-time and on-site five days per week in New York City.

Lead the technical strategy, architecture, development, and productionization of large-scale machine learning and pricing systems for Uber’s real-time marketplace. Develop deep learning, causal, reinforcement learning, optimization, and algorithmic pricing solutions serving billions of trips. Mentor engineers, influence cross-team technical direction, communicate with executives, and deliver high-impact systems for millions of drivers.

Develop and integrate AI/ML capabilities for satellite mission planning, space domain awareness, and resilient operations. Responsibilities include end-to-end machine learning, sensor-data classification and tracking, anomaly detection, scalable HPC pipelines, modeling and simulation integration, algorithm optimization, and technical assessments. The role requires collaboration with satellite designers, operations planners, and subject-matter experts in a fully onsite classified environment.

Designs, develops, and maintains production-ready AI/ML applications for intelligence missions, focusing on LLM integration, agentic workflows, and RAG pipelines. Builds Python services, REST APIs, backend components, database workflows, and enterprise integrations. Optimizes AI systems for performance, scalability, reliability, and usability while evaluating model effectiveness and applying software engineering best practices. Requires collaboration with technical stakeholders to deliver mission-focused capabilities.

Develop and deploy machine learning models for autonomous vehicle behavior planning, including onboard planning, offboard evaluation, simulation, imitation learning, and reinforcement learning. Train large models on massive datasets using distributed computing, advance autonomous driving research, and deploy production-ready systems on public-road vehicles. The role also involves developing foundation models, world models, critic models, and neural simulators while maintaining high standards for safety and performance.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Lead architecture and hands-on development of machine learning and data infrastructure for risk and fraud prevention. Optimize model inference performance, build model deployment and monitoring systems, design scalable Databricks and Spark data pipelines, connect offline and online feature systems, and improve feature services and data integrations. Provide technical direction, mentor engineers, and coordinate delivery across ML, data engineering, cloud infrastructure, security, and product teams.

Leads development of full-stack, cloud-native software using Java, Python, AWS, microservices, APIs, and PostgreSQL. Designs scalable and secure systems, builds LLM and generative AI capabilities, and integrates AI agents into engineering workflows. Improves operational stability through automation and observability while contributing to Agile delivery, system architecture, and technical standards. Collaborates across initiatives and promotes responsible, secure validation of AI-assisted development outputs.

Lead the architecture, strategy, and development of enterprise generative AI initiatives. Define AI/ML standards, guide scalable model deployment, conduct advanced research, and collaborate with executives, product, data science, and engineering teams. Mentor technical leaders, promote MLOps and responsible AI practices, optimize models for cost and performance, and represent the organization through publications and industry engagement.

Develops data visualization, analytics, machine learning, and automation solutions for coherent optical product manufacturing. Mines wafer-to-module datasets to identify yield drivers, failure signatures, anomalies, correlations, and process drift. Builds and evaluates ML algorithms for yield prediction and root-cause analysis, performs statistical analysis, and automates engineering workflows using GenAI. Collaborates with R&D, manufacturing, and AI/ML teams to optimize product performance and communicate findings to technical audiences.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.