Top Senior Machine Learning Jobs

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Develops advanced machine learning, deep learning, signal processing, optimization, and numerical modeling algorithms for biomedical signals and next-generation medical devices. Responsibilities include algorithm design, implementation, testing, documentation, data analysis, disease detection and prediction modeling, patentable innovation, and collaboration with hardware and software engineering teams.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Leads and grows engineering teams building scalable machine learning, backend, and data platforms for advertising measurement. Sets technical strategy, productionizes causal inference and experimentation solutions, and develops products such as incrementality, lift testing, attribution, anomaly detection, and automated insights. Partners across Product, Data Science, Ads Engineering, Infrastructure, and Sales while hiring and mentoring technical leaders. Champions AI-assisted development with strong standards for validation, explainability, privacy, governance, and accountability.

Build, deploy, scale, and operate production machine learning models and platforms. Responsibilities include developing ML components, distributed data pipelines, cloud and Kubernetes infrastructure, model monitoring and retraining, CI/CD automation, responsible AI governance, and collaboration with product, data science, and engineering teams.

Build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML applications, distributed data pipelines, cloud and Kubernetes infrastructure, automated testing and deployment, model monitoring, retraining, governance, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while solving complex business problems using Python, Scala, Java, and large-scale ML technologies.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Design and develop machine learning algorithms for target ROAS, automatic bidding, and ads pacing. Explore bid landscapes, optimize advertiser value and revenue, conduct A/B experiments, and collaborate with product, sales, and engineering teams on new ad products and bidding strategies. Provide staff-level technical leadership, drive cross-team alignment, and use AI-assisted development and productivity tools to improve engineering workflows.

Lead end-to-end decision science engagements for Federal clients using mathematical optimization, simulation, machine learning, statistical modeling, and analytics. Design, develop, deploy, and maintain production models; build agentic workflows; write Python; mentor junior scientists; manage projects; communicate complex findings to stakeholders; support proposals and business development; and contribute to internal practice development.

Lead the AI & Analytics stream to design, build, and deploy predictive and prescriptive ML models, integrate analytics into enterprise platforms (Salesforce, ServiceNow), automate root-cause and log analysis, translate stakeholder needs into production solutions, and manage analytics projects end-to-end while communicating results to technical and executive partners.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring models, applying responsible AI practices, and collaborating with product, data science, and engineering teams. The role requires advanced programming, machine learning, distributed computing, Kubernetes, and cloud experience.

Provides senior systems engineering leadership for integrating AI/ML capabilities into MQ-9 unmanned aviation systems. Develops requirements, architectures, implementation strategies, roadmaps, trade studies, technical evaluations, and engineering analyses. Oversees AI/ML data management, model validation, integration, testing, and deployment while coordinating government, industry, and engineering stakeholders. Supports defense acquisition, fielding, sustainment, deficiency investigations, and executive-level technical decision-making within the NAVAIR program environment.

Develop and deploy AI-enabled computational methods for small-molecule hit and lead generation. Responsibilities include selecting and optimizing machine learning, deep learning, cheminformatics, molecular generation, affinity prediction, pharmacological property prediction, and retrosynthetic analysis workflows. The scientist collaborates with medicinal chemistry, structural biology, biochemistry, and computational teams to define project criteria, communicate methodology, and support early-stage drug discovery campaigns.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Design, train, test, optimize, and deploy machine-learning and deep-learning models using time-series and biosensor data. Develop robust inference systems for constrained edge and cloud environments, monitor algorithm performance and data quality, support regulated health-feature documentation, and conduct applied research. Collaborate with data science, firmware, and engineering teams to translate research prototypes into scalable production features for wearable technology.

Leads logistics analytics and optimization across distribution and contact center networks. Develops analytical models, machine learning algorithms, automated processes, forecasting solutions, and scalable optimization tools. Partners with operations, engineering, and technology teams to improve productivity, capacity, service, cost, and decision-making. Deploys and monitors machine learning models in production, measures business impact, and recommends continuous improvements.

Apply machine learning techniques to high-performance computing, large-scale data analytics, and advanced information technology programs. Develop solutions using established machine learning frameworks such as PyTorch and TensorFlow. The role requires an advanced degree in artificial intelligence, data science, computer science, deep learning, or advanced mathematics, with substantial applied machine learning experience.

Build, deploy, scale, and monitor production machine learning models and platforms. Responsibilities include developing ML applications, optimizing models and infrastructure, creating data pipelines, operating distributed systems and cloud-based services, managing Kubernetes environments, automating testing and deployment, and applying responsible AI practices. The role collaborates with product, data science, and Agile engineering teams to deliver reliable ML solutions at scale.

Build, deploy, scale, and operate production machine learning models and platforms for risk management. Responsibilities include developing ML applications, distributed training and serving systems, cloud architectures, data pipelines, model monitoring, retraining, testing, CI/CD, governance, and responsible AI practices. The role collaborates with Product, Data Science, GRC, and cross-functional Agile teams while solving complex business problems with advanced algorithms and open-source frameworks.

Leads hands-on architecture, development, and production deployment of scalable AI, machine learning, and LLM solutions for asset and wealth management. Designs data-processing pipelines, model inference services, and cloud-based platforms; experiments with and productionizes ML models; and partners with technical, product, and business teams. The role also requires leadership in AI innovation, solution design, data management, scalability, security, and communication with senior stakeholders.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, hybrid search, model integration, evaluation, observability, safety guardrails, and governance. The role provides architectural leadership, mentors engineers, leads design reviews, and develops scalable AI infrastructure across hyperscalers and company platforms.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.