Top Senior Machine Learning Jobs

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.

Lead design and implementation of complex embedded and application software for signal processing and algorithm development (including machine learning) for EO/IR and electronic warfare systems. Provide technical leadership, mentor teams, produce design/project documentation, evaluate and optimize algorithms (C++), and collaborate with systems, hardware, and test engineers through full lifecycle development and customer technical reviews.

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.

Designs and implements AI-enabled workflows for cyber and intelligence missions, including LLM-based agents, multi-step automation, API and data-store integrations, and production deployments in classified environments. Collaborates with mission users, analysts, developers, and leadership to translate operational workflows into reliable, secure capabilities. Requires Python experience, TS/SCI clearance with polygraph, and relevant AI/ML or software engineering experience.

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.

Develop and optimize deep learning models for computer vision applications, focusing on neural network architecture, training, inference optimization, and production deployment. Build tools and infrastructure for model training, evaluation, and deployment in Linux and cloud environments. Research emerging machine learning techniques, integrate them into products, and collaborate with software engineers on customer-focused projects throughout the product lifecycle.

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.

Design and build Robinhood’s agent platform, including orchestration, tool integrations, context and memory management, trajectory-level evaluations, simulation environments, and action guardrails. Ship production agentic applications, establish safety and evaluation standards, create reusable SDKs and CI gates, and provide technical leadership through architecture reviews, mentorship, and production-readiness decisions.

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.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Leads AI/ML, factory automation, APC, and FDC initiatives in a semiconductor wafer fabrication facility. Develops machine learning models, ETL pipelines, predictive analytics, dashboards, and intelligent manufacturing applications using fab, equipment, metrology, and process data. Supports equipment integration, installations, technology transfers, troubleshooting, and cross-functional optimization of yield, throughput, process stability, and equipment performance. The role is fully onsite and may require cleanroom work.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.

Lead the architecture, development, and deployment of scalable AI/ML and generative AI platforms powering agentic workflows. Own technical strategy, platform evaluations, roadmaps, system reliability, and cross-functional delivery. Mentor engineers and technical leads while establishing enterprise AI best practices. Build multi-component systems involving search, knowledge management, orchestration, databases, streaming, distributed processing, cloud infrastructure, and LLM-based conversational experiences.

Owns end-to-end development of datasets, reinforcement learning environments, and evaluations for coding agents and software engineering models. Leads engineers, researchers, QA professionals, and data creators; establishes quality processes, pipelines, validation, and review workflows. Collaborates directly with frontier AI labs, fine-tunes models, builds benchmarks, develops automation and internal tools, and supports customer-facing enablement. The role combines hands-on technical leadership, engineering management, operational execution, research, and industry thought leadership.

Leads the design, development, integration, verification, and flight-test delivery of machine learning and autonomy capabilities for uncrewed aircraft. Responsibilities include perception, tracking, sensor fusion, planning, mission execution, model deployment, real-time C++ and Python software, simulation, testing, troubleshooting, and cross-functional system integration. Provides technical leadership and mentorship without direct personnel management. Requires on-site aircraft integration and field-test support in Bingen, Washington.

Design and build scalable ML infrastructure and backend systems for high-impact marketplace applications. Lead architecture, solve low-latency and high-correctness problems, develop and adopt engineering frameworks, and create production-ready technical roadmaps. Partner with Product, Science, and Operations while mentoring engineers and improving engineering standards. The role requires experience with ML models, foundational models or LLMs, MLOps, Kubernetes, data pipelines, distributed workflows, and large-scale scheduling.

Develop autonomy algorithms and foundation models for semantic understanding of real-world driving data. Architect scalable machine learning systems, optimize sensor datasets through processing and auto-labeling, and deploy ML techniques into production. The role requires collaboration across platform, product, security, and engineering teams while solving complex autonomous-driving edge cases and maintaining high technical standards.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.

Defines the multi-year technical vision and architecture for foundation models supporting search, recommendations, personalization, and conversational AI. Leads high-impact ML platform initiatives across Mobility and Delivery, drives retrieval, ranking, and LLM system design, and makes build-versus-partner investment decisions. Provides principal-level technical leadership, mentors senior engineers, aligns executives across organizations, and connects model improvements to measurable business outcomes at global scale.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.