Top Senior Machine Learning Jobs

Develop, deploy, and integrate machine learning and deep learning models for scientific and industrial problems (materials, chemistry, process optimization). Build data pipelines, feature engineering, model evaluation, and scalable production integrations. Collaborate with scientists, engineers, product, and customer teams to translate domain problems into AI solutions, support deployments, and communicate results. Conduct strategic research and stay current with AI/ML methods; periodic customer travel.

Design, develop, and deploy production-grade AI/ML models and scalable architectures. Lead model training, validation, deployment, monitoring, and cross-functional integration. Conduct research, document methods, and share knowledge to drive business impact and operational reliability.

Lead inference acceleration and GPU-parallelism optimizations (TP, SP, PP); implement high-performance CUDA/NCCL kernels; deploy videogen and LLM models to production; collaborate with researchers, conduct code reviews, mentor engineers, and optionally improve training efficiency and resource utilization.

Own end-to-end clinical prediction models: design, train, evaluate, and ship models using physiological time series and EHR data. Define labels with clinical teams, build evaluation/backtesting and regression infrastructure, assess robustness and subgroup performance, support production deployment and monitoring, and contribute to validation and regulatory evidence.

Design, develop, and maintain AI/ML software solutions; collaborate with developers and leadership; build tools for analyzing large-scale data; debug, profile, and optimize code; create documentation and user support materials; leverage modern LLM tools to accelerate development.

Design, develop, and maintain AI-driven software solutions; collaborate with teams to analyze large-scale data, debug and optimize code, build user tools, and produce technical documentation and support guides.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.

Senior ML Engineer joins a product team to design, build, deploy, and monitor production-grade AI/ML solutions. Responsibilities include algorithm and software design, pairing with teammates, creating scalable data pipelines and infrastructure, implementing model serving and monitoring, performing performance tuning and testing, and supporting product lifecycle and stakeholder collaboration.

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.

Design, build, and deploy large-scale ML systems for ad ranking, bid optimization, and recommendations. Build and operate production ML pipelines (feature engineering, distributed training, deployment, retraining, monitoring) that process billions of events and serve low-latency predictions. Optimize training and inference for compute and cost efficiency. Partner with Data Science, Data Engineering, and product teams to ship models, run online experiments, and measure CTR/CVR/ROI impact. Incorporate applied ML research and techniques into advertising and audience modeling.

Responsible for designing and optimizing VLSI circuits, translating requirements into data science problems, and building machine learning solutions. Collaborate on hardware design and integrate models into existing tools.

The job involves developing machine learning algorithms, designing data pipelines, utilizing cloud infrastructure for ML solutions, and ensuring model governance practices.

Design and run experiments measuring SL5 controls on real ML workflows; forecast 2028 frontier workloads and infrastructure needs; run and interpret scalable experiments to inform a purpose-built, high-assurance datacenter standard for frontier AI.

Lead end-to-end development and productionization of high-priority propensity and churn models. Build, train, deploy, monitor models; perform advanced feature engineering and semantic search/RAG workflows; collaborate with marketing pods to translate business goals into microsegmentation and personalization strategies; present model insights and maintain model lifecycle and performance.

Lead development and productionization of interpretable time-series and ML forecasting models for growth, revenue, LTV, and compute. Own end-to-end lifecycle from feature engineering and experimentation to deployment, monitoring, explainability, and self-service forecasting tools. Translate model outputs into strategic business recommendations and collaborate cross-functionally to integrate forecasts into planning and decision-making.

Design, implement, and scale production-ready ML systems (computer vision and LLMs) for blueprint understanding, 3D model generation, document extraction, and materials forecasting. Build ML pipelines, deploy and monitor models, integrate retrieval/RAG and rules-based methods, and collaborate with product, engineering, and domain experts to deliver explainable, business-impacting features.

As a Senior ML/AI Engineer, you'll build an AI operational backbone for a new consumer membership app, shipping AI-native features for millions of users.

Develop and translate advanced ML models for heart disease diagnostics, collaborate with cross-functional teams, conduct research, and manage projects.

The Senior Machine Learning Engineer will design, develop, and deploy machine learning solutions for Vitol, working across trading and operations with various stakeholders.

Lead end-to-end ML data programs for autonomous vehicle models: define dataset requirements, develop labeling policies, build evaluation frameworks and metrics, coordinate cross-functional teams, and drive data selection strategies to maximize labeling ROI and model performance.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

The ML Engineer will build and deploy machine learning models to optimize operations in manufacturing, including demand intelligence and production monitoring.

The role involves designing, developing, and deploying machine learning models for healthcare solutions, optimizing data processing pipelines, and collaborating with cross-functional teams to enhance patient care.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

The Senior Machine Learning Engineer will design, train, and evaluate ML models to solve complex problems, mentor others, and optimize production systems.