Top Senior Machine Learning Jobs

Lead and grow the Machine Learning Engineering team, set technical direction, define team roadmap, and collaborate across product and engineering teams.

As a Senior Machine Learning Engineer, you will architect and build scalable ML infrastructure, influence technical direction, mentor engineers, and ensure effective production deployment, collaborating closely with AI teams.

The role involves developing and deploying machine learning models, conducting analytical studies, and integrating solutions into software. Responsibilities include mentoring team members and improving decision-making using data insights.

Lead ML data and feature strategy for fraud detection, own end-to-end feature engineering pipelines, close tooling gaps and drive infrastructure roadmap, partner with ML engineers to productionize models, mentor junior staff, collaborate with product and risk teams, and serve as the team's institutional expert on ML evolution for ATO and scam prevention.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

As a Senior AI/ML Scientist, you'll integrate AI/ML into engineering tools, develop predictive models, analyze data, collaborate with teams, and establish best practices in a hybrid role.

Lead applied machine learning research to improve validation of ML components in autonomous vehicles, focusing on simulation-led strategies and automating validation processes.

As a Senior ML Engineer, you'll design and deploy advanced machine learning systems for autonomous vehicles, ensuring real-time performance and robustness in diverse conditions. You'll lead technical projects, mentor engineers, and collaborate across teams to enhance onboard capabilities.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

The Lead Machine Learning Engineer will design, build, and productionize ML applications, oversee model performance, collaborate with teams, and ensure reliable deployment of ML systems at scale.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

The Machine Learning & AI Engineer develops and deploys ML and AI systems for the Energy Industry, focusing on data quality and interpretability. Responsibilities include model design, pipeline development, AI agent systems, and collaborating with cross-functional teams.

The Software Engineer will develop scalable, high-quality AI applications for environmental conservation, working on projects utilizing ML models and cloud technology.

Conduct applied research using signal processing and machine learning to develop predictive ECG and cardiac monitoring algorithms. Collaborate with clinicians and engineers to define requirements, design training/testing/deployment architectures, manage R&D projects, mentor others, and promote development best practices to produce fieldable medical device solutions.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

Design, train, optimize, and deploy efficient on-device ML models (audio and vision) for resource-constrained products; maintain Ambiq ADKs; port and optimize customer models; apply model-efficiency techniques; produce customer-facing demos, docs, and benchmarks.

As a Senior Machine Learning Engineer, you will develop machine learning models, automate data pipelines, and collaborate with teams to meet customer needs.

The Senior Machine Learning Engineer will develop and implement machine learning models, work with stakeholders, and drive AI solutions for complex data engineering challenges while ensuring high-performance operations.

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 actuarial data scientist to oversee technical pricing models, actuarial systems, and a team of actuarial data scientists. Develop strategic roadmap, establish modeling standards and validation, apply ML/AI to pricing, collaborate with legal/compliance, present insights to executives, and manage projects to deliver high-quality actuarial products.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

As a Senior Machine Learning Engineer, you will own ML infrastructure and data systems, scaling them for large volumes of video data and supporting deep learning training and experimentation.