Top Senior Machine Learning Jobs

Lead design and delivery of end-to-end ML solutions for Wealth Management, build and deploy scalable models (recommender systems, GNNs, LLMs), run large experiments, validate and monitor production models, partner with cross-functional teams, and mentor data scientists to drive measurable business outcomes.

Lead the design and delivery of complex ML systems, architect integrations, and set high standards for engineering practices while collaborating with teams to develop robust solutions and mentor other engineers.

The Staff Machine Learning Engineer will design and deliver complex ML systems, lead integration efforts, build scalable solutions, and optimize model deployments, collaborating with various teams and mentoring peers.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying ML/DL models, researching state-of-the-art AI (LLMs, transformers, RL), building scalable training and inference pipelines, leading AI strategy and technical teams, ensuring robust MLOps, optimizing models for production, and collaborating across engineering, data, and product.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Design, develop, and deploy machine learning models and data pipelines for naval applications. Build and optimize distributed, cloud-native analytics and event-streaming systems, work with multiple data formats and GIS tools, containerize and monitor deployments, apply DoD security practices, and collaborate with multidisciplinary teams to deliver mission-ready AI solutions in shipboard and operational Navy environments.

Design, build, and deploy production ML models and scalable pipelines to improve recommendations, ranking, and personalization. Own end-to-end workflows from data exploration and feature engineering to training, evaluation, experimentation (A/B tests), and production deployment while applying responsible AI practices.

Lead and grow a small ML team to turn Evvy's vaginal microbiome dataset into validated, product-ready diagnostic and prognostic models. Drive technical quality, roadmap planning, cross-functional translation, model validation and deployment readiness, hiring, and day-to-day team management.

Lead design and productionization of scalable ML/LLM systems for legal products. Build LLM applications (RAG, agents), hybrid search, model serving, data pipelines, APIs, and cloud-native infrastructure. Optimize for latency, reliability, and cost; establish deployment/observability best practices; partner with data scientists and provide technical leadership and coaching.

Design, develop, deploy, and sustain agentic AI and ML capabilities across the full lifecycle. Build ML models and pipelines, integrate LLMs/agentic workflows, implement CI/CD and containerized deployments, evaluate system safety and performance, and collaborate with engineers and stakeholders to deliver secure, production-ready AI solutions for mission use.

Build and productionize AI systems and infrastructure that power customer-facing features and internal tooling. Partner with product, design, and engineering to identify high-impact ML opportunities, prototype solutions (recommendations, NL interfaces, agents), and manage the full ML lifecycle from data curation to deployment and monitoring.

Lead design, development, and delivery of ML, DL, and Generative AI systems for production. Apply prescriptive analytics, optimization, recommendation, and reinforcement learning. Build and evaluate models, run experiments, develop prototypes, benchmark methods, and collaborate with business partners to translate data into actionable recommendations and influence strategy.

The Senior Machine Learning Engineer will design, implement, and scale advanced foundation models, collaborate with researchers, and build AI systems. Requires hands-on experience with distributed training pipelines and proficiency in AI frameworks.

Prototype, develop, and deliver AI/ML solutions; architect and integrate models into products; perform ETL, data curation, and cloud integration (including AWS); work with graph and relational databases and diverse data formats; monitor and tune model performance; collaborate with stakeholders and document processes.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Lead development of machine learning, computer vision, sensor fusion, and registration algorithms for a robotic lung biopsy platform. Prototype, evaluate, and productionize image-analysis solutions in Python/C++, collaborate cross-functionally, perform failure-mode assessment, and maintain state-of-the-art technical knowledge.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current with ML/AI advances.

Design, implement, and optimize machine learning and deep learning models; preprocess and prepare large datasets; integrate models into production with software engineers; monitor and maintain production model performance; troubleshoot issues; and collaborate with stakeholders while staying current on ML/AI advances.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

The Senior AI/ML Engineer will design, develop, and deploy scalable ML systems focusing on LLM applications, collaborating with various stakeholders to enhance healthcare data insights.

The Senior Machine Learning Engineer will design evaluation frameworks, develop synthetic data pipelines, build evaluation systems, and collaborate with teams to improve model quality in healthcare AI.

Design, develop, and deploy production 3D and BEV perception ML models for autonomous trucks. Own end-to-end model lifecycle from data prep, training, evaluation, and deployment. Improve model robustness, performance, and generalization across sensor modalities. Integrate models with autonomy software, enhance training pipelines and tooling, analyze failures, and mentor junior engineers.

Lead technical development of machine learning algorithms for the onboard Planner, advancing imitation learning, reinforcement learning, and model scaling; integrate foundation models; create metrics, data, and training frameworks; mentor ML developers; and collaborate across perception, simulation, system design, and safety teams to solve autonomous driving challenges in complex urban environments.