Top Machine Learning Engineer Jobs

Deploy and optimize computer vision, signal processing, and machine learning algorithms on GPU-enabled embedded systems. Improve real-time performance on constrained low-SWaP platforms, profile CPU/GPU bottlenecks, select algorithms based on hardware constraints, containerize deployments, and produce Linux-based production software. Collaborate with machine learning and software engineers to transition models into operational environments.

Designs, develops, tests, deploys, trains, monitors, and improves machine learning models for business and national security mission requirements. Responsibilities include selecting datasets, statistical analysis, algorithm execution, model evaluation, data pipeline integration, system testing, and scalable solution development. The role requires strong understanding of AI/ML architectures, application development, infrastructure, data engineering, and governance, with potential leadership of distributed teams.

Develop and deploy computer vision and machine learning models for semantic and instance segmentation, object and damage detection across 2D and 3D data. Responsibilities include training loops, dataset curation, labeling guidelines, evaluations, ablation experiments, model export, inference code, testing, profiling, and production support. Collaborate with research, backend, operations, and labeling teams while documenting experiments and improving model performance.

Build and scale Gusto's ML/AI platform: design MLOps solutions, automated pipelines, model development frameworks, deployment, monitoring, CI/CD, and observability; collaborate with ML engineers and apply AI tools and best practices to production ML systems.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Architects, develops, and deploys high-performance machine learning systems across the full lifecycle. Responsibilities include large-scale feature engineering, online and offline pipelines, tree-based and deep learning models, low-latency serving, MLOps automation, model monitoring, and rigorous experimentation. The role requires production-grade software engineering, distributed computing, statistical expertise, and experience shipping ML systems in high-scale environments.

Develop and integrate cloud software, AI/ML capabilities, distributed analytics, and microservices on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, cloud deployment, testing, troubleshooting, RESTful services, Hadoop-based big-data processing, infrastructure as code, CI/CD, and Linux systems work. The role requires active TS/SCI clearance with polygraph and one approved cloud developer certification.

Develop and sustain cloud software for cyber intelligence and AI/ML capabilities. Responsibilities include requirements analysis, Java development, distributed systems, cloud deployment, software integration, testing, troubleshooting, analytics development, and technical documentation. The role uses AWS, Kubernetes, infrastructure-as-code, CI/CD, Hadoop technologies, Linux, microservices, LLMs, and RAG frameworks while supporting large-scale data processing and national security missions.

Develop and sustain cloud software for national security missions, including requirements analysis, integration, testing, validation, and issue resolution. Build AWS-based services and distributed systems using Java, Kubernetes, infrastructure-as-code, CI/CD, Linux, and microservices. Integrate AI/ML capabilities, LLMs, and RAG frameworks while supporting large-scale compute clusters, big-data technologies, RESTful services, and cloud security practices. The role requires active TS/SCI clearance with polygraph and collaboration in a mission-driven cyber intelligence environment.

Design, develop, test, deploy, and maintain scalable AI/ML and full-stack software using Java and Python. Customize LLM interfaces, integrate external requirements tools, troubleshoot defects, conduct code reviews, support planning, design accessible user interfaces, and mentor junior engineers. The role requires collaboration with customers, product managers, developers, subject matter experts, and leadership in a classified Intelligence Community environment.

Lead the end-to-end data platform for large-scale physical-labor video, from on-device capture and reliable uploads over poor networks through object storage, compression, training-shard creation, dataset versioning, and delivery to research customers. Own petabyte-scale media infrastructure, cost and throughput optimization, observability, and ingestion contracts across firmware and international operations. The role requires strong software engineering and production experience with media or sensor data at significant scale.

Own the architecture and hands-on implementation of production MLOps systems, scalable inference services, model deployment pipelines, monitoring, and governance. Integrate machine learning into pharmaceutical manufacturing, process control, automation, and scientific or clinical applications. Collaborate with chemical engineers, computational biologists, and platform teams while establishing enterprise CI/CD and MLOps standards. This is a hands-on senior role requiring hybrid onsite work in Indianapolis.

Build and maintain AWS-based machine learning platform infrastructure supporting model training, evaluation, deployment, annotation, and multi-tenant data management. Develop full-stack tools, optimize deep learning models for training and inference, implement containerized CI/CD workflows, and improve cloud cost efficiency. The role focuses on vision-based machine learning, edge deployment, serverless architecture, and scalable production pipelines while collaborating with machine learning scientists and cloud engineering teams.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.

Lead development of multi-sensor, multi-task machine learning models for autonomous driving perception, including object detection, occupancy prediction, road understanding, and trajectory planning. Architect foundational modeling libraries, sensor fusion components, temporal aggregation systems, and scalable experimentation pipelines. Optimize models for latency, throughput, memory, and onboard hardware constraints while enabling rapid prototyping and analysis of model training and learned representations.

Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations.

Develop and deploy machine learning solutions for construction-site understanding using computer vision, multimodal AI, VLMs, LLMs, generative AI, RAG, and agent architectures. Curate high-quality datasets, optimize models for performance across hardware targets, and build production ML pipelines in the cloud. Own components from design through deployment and monitoring while collaborating in an agile team.

Build and operate machine learning models powering roleplay, scoring, and coaching products. Own the full lifecycle from training and fine-tuning through production deployment and maintenance. Fine-tune open-source models, optimize them for cost and latency, deploy them on-device when needed, and develop evaluation frameworks, benchmarks, and regression suites. Collaborate closely with founders and engineers to shape product direction.

Develop and integrate AI/ML capabilities into operational software, including large language models, retrieval-augmented generation, agentic workflows, prompt engineering, and inference pipelines. Evaluate, test, document, and communicate AI system performance, reliability, design decisions, and technical contributions. Preferred experience includes secure development, DevSecOps, Kubernetes, OpenShift, CI/CD, containerized delivery, and classified or regulated environments.

Design, develop, and deploy enterprise-scale AI systems powering intelligent agents, copilots, and autonomous workflows. Build production-grade LLM, RAG, vector database, and agent orchestration solutions; develop APIs, SDKs, and platform services; and optimize reliability, latency, security, cost, and scalability. Lead initiatives through production, implement testing, CI/CD, monitoring, and governance, troubleshoot distributed systems, collaborate cross-functionally, contribute to architecture and documentation, and mentor or guide team members.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

Build and ship production machine learning systems for content abuse detection, including data pipelines, classifiers, LLM evaluations, inference infrastructure, monitoring, and model evaluation. Optimize precision, recall, latency, cost, and throughput while partnering with engineering and data teams. Develop moderator intelligence features, build training and serving platforms, establish metrics infrastructure, and mentor teammates. The role requires end-to-end ownership in a startup environment, from messy data and feature engineering through deployment and production monitoring.

Own machine learning infrastructure spanning distributed training, reinforcement learning, data pipelines, model serving, deployment, monitoring, and rollout tooling. Partner with researchers to productionize experimental workflows for clinical AI applications. The role also involves GPU scheduling, autoscaling, reproducible environments, online-learning loops, checkpointing, evaluation, and technical leadership or mentoring.

Own and evolve real-time machine learning detection systems for account compromise. Design, train, deploy, and monitor models in production at scale; architect systems for rapid growth; work with customers to understand attacks; and help establish engineering standards on a small, high-autonomy team.