Top Senior Machine Learning Jobs

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Lead end-to-end personalization and recommender system development: define technical roadmap, build and deploy scalable low-latency models, establish experimentation and monitoring, drive cross-team technical decisions, and mentor senior/mid-level scientists to improve recommendation quality and business outcomes.

Lead development of advanced ML models and production-grade systems to optimize pricing, allocation, and auctions in Roku's ad serving platform. Translate business problems into hypotheses, run live experiments, mentor teams, and collaborate cross-functionally to drive marketplace intelligence and demand optimization.

Lead end-to-end forecasting ML systems: scope, feature engineering, model development, backtesting, deployment, monitoring, retraining, and reliability improvements. Build production-safe evaluation infrastructure, AI-assisted development workflows, probabilistic/uncertainty-aware forecasts, and productize versioned forecast outputs. Partner cross-functionally to prioritize roadmap, define business-tied success metrics, and communicate assumptions and risks to stakeholders.

Lead design, development, testing, and deployment of cloud-enabled AI/ML systems (computer vision, sensor fusion, decision support) for maneuver, surveillance, and engagement missions. Evaluate model performance on operational datasets, optimize for robustness and cyber resilience, troubleshoot deployed systems, and produce technical documentation and reports for stakeholders.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

As a Senior Software Engineer at NVIDIA, you will design and implement inference software optimizations for AI applications using TensorRT, collaborating with deep learning experts to influence hardware and software design.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

Develop, analyze, and deploy AI/ML models to support Missile Defense Agency ground test scenario design. Optimize model performance, ensure explainability and reliability, integrate solutions with cross-functional systems, maintain scalable Python code, research emerging AI techniques, and produce technical documentation and briefings for stakeholders.

As an Applied ML Software Engineer, you will design and implement ML models, manage cloud infrastructure, and optimize backend systems for AI-driven content discovery and recommendations.

Lead development and deployment of credit, account-management, and fraud ML models using embeddings, transformers, and classical approaches. Own data pipelines, feature engineering from structured and unstructured sources, production monitoring, and cross-team integration while shaping technical strategy and mentoring Juniors.

Design and implement production-grade multi-agent LLM systems and agent workflows for advisor-facing tools. Build distributed, observable services (model serving, vector DBs, RAG) with memory/state management, validation, and compliance. Partner with product and engineering to optimize for latency, cost, and reliability, and establish evaluation frameworks to ensure grounded, auditable AI-driven insights for portfolio and advisor workflows.

The Staff ML Engineer will lead architecture decisions for ML infrastructure, operate real-time computer vision systems, and mentor engineers, ensuring high-quality ML deployment and operational excellence at SimpliSafe.

As a Senior Machine Learning Engineer, you will build and operate scalable ML systems, oversee projects from inception to deployment, and improve system performance while collaborating with cross-functional teams and mentoring junior engineers.

As a Senior Machine Learning Engineer, you will design and operate scalable ML systems for real-time personalization, collaborating with teams and mentoring engineers.

Design, develop, and enhance AI/ML software solutions, collaborate with teams, and work with large datasets to support mission operations.

As a Lead Machine Learning Engineer, you will operationalize ML models, collaborate with teams, solve complex problems, optimize data pipelines, and implement best practices in ML deployment.

Lead development and refinement of machine learning pricing models (especially reinforcement learning) for Airbnb hosts. Collaborate with data scientists, product, and engineers to design, test, integrate, and monitor models, analyze performance, and prioritize projects to improve host outcomes.

Senior Quant in the AI/ML Validation CoE provides model validation and benchmarking for AI/ML and Generative/Agentic AI across bank functions. Responsibilities include independent testing, R&D of AI methods, developing technical guidance, training curriculum, communicating validation outcomes, and advising on model risk mitigation, implementation, monitoring, and remediation.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Lead product strategy and execution for DriveWell Fusion, focusing on telematics data cleansing, signal processing, and AI/ML model development. Collaborate with engineering, customers, and cross-functional teams to integrate map data, deliver actionable driving insights, and use Databricks/Tableau analytics to inform roadmap and adoption. Serve as primary product contact, prioritize features, and drive customer-facing advisory work to improve safety and engagement.

Design and build large-scale ML models and systems to improve ad targeting, relevance, ranking, and engagement. Implement search, retrieval, and personalized discovery using transformers, embeddings, vector search and LLMs. Build and optimize ML pipelines, define KPIs, collaborate cross-functionally, and mentor engineers to drive production-grade advertising products and platform scalability.

As a Software Development Engineer (AI & ML), you'll architect and implement scalable ML models, optimize data pipelines, and collaborate with teams to deliver financial tech solutions.