Top Senior Machine Learning Jobs

Lead performance measurement and release management for the core autonomy stack across passenger, trucking, and mining programs. Define reliability metrics and forecasts, coordinate end-to-end releases to downstream teams, prioritize engineering projects to improve driving performance, and build reporting and dashboards for engineering and customer-facing teams.

Lead development and production deployment of advanced ML/AI pricing models across Home, Renters, Car, and Pet lines. Own end-to-end projects, align stakeholders, mentor junior data scientists, set technical standards, and drive adoption of advanced data science practices within Insurance.

Lead research and development of auction mechanism design for online advertising. Evaluate and improve ranking/pricing algorithms, run large-scale experiments (A/B), build data pipelines and production software, apply ML/RL and optimization, and collaborate cross-functionally to deliver measurable business impact.

Lead end-to-end development, validation, and deployment of large-scale predictive models. Drive rigorous data-driven research, translate business goals into analytical frameworks, and communicate findings to executives. Mentor data scientists and partner with engineering and stakeholders to productionize ML solutions and define long-term analytics strategy.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Lead enterprise-level strategy and delivery for building design, engineering, data readiness, and AI adoption. Define customer success plans, run workshops, shape consulting offerings, mentor staff, drive change management, and ensure high-quality project delivery and measurable customer value.

Lead architecture and implementation of ML-driven features (including generative AI/LLMs and optimization) across cloud and mobile. Build production model deployment, CI/CD and experimentation infrastructure, define technical strategy from ambiguous product requirements, and own Agile delivery and technical risk communication while collaborating with Product, Engineering, and Data Science.

Design, build, and deploy machine learning models to detect fraud, prevent losses, and monitor risk. Work with Product, Engineering, and Operations to turn large-scale data into production-ready pipelines and model-serving solutions, monitor performance, and continuously improve model accuracy and business impact.

Lead integration of learned models into the autonomous behavior planning stack. Collaborate with ML and autonomy teams to design, validate, monitor, and deploy learned planning components while ensuring safety, fallback strategies, debuggability, and production readiness. Mentor engineers and drive cross-functional technical designs using simulation, logs, and real-world data.

As a Senior AI Engineer, you'll develop and deploy innovative AI solutions for the fashion industry, collaborate with cross-functional teams, and refine machine learning models to enhance product offerings.

Join AIS supporting a federal client in the DMV to develop, train, and deploy AI/ML models from ideation to production. Use Python and common data-science libraries, work with database systems, collaborate on hybrid teams, and meet security clearance requirements.

Lead development and operationalization of pricing algorithms to value vehicles, deliver $15-30M/year in incremental value, build and manage a 4-8 person data science team, develop AI/ML models to autonomously price vehicles, run experiments, and implement model monitoring and incident response with cross-functional partners.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

Lead and mentor a team of machine learning engineers, drive ML projects, and collaborate across departments to enhance product features and model improvements.

The role involves developing novel methodologies for conversion modeling in advertising, using machine learning and statistical techniques to enhance performance and optimization.

The role involves developing new methodologies for advertising conversion modeling using advanced machine learning techniques, supporting various quantitative research aspects. Candidates should have extensive experience in statistical and deep learning methodologies, contribute to research, and build generative models for ad optimization.

Develop and optimize predictive models for credit and risk, leveraging advanced machine learning techniques to improve accuracy and decision quality.

You will design and implement high-performance software while applying machine learning techniques to complex problems in electronic design automation.

Lead end-to-end analytics and ML initiatives for PBM member engagement: build, deploy, and monitor large-scale ML/DL models; perform EDA and data quality assessments on prescription claims; collaborate with engineering and stakeholders to create feature stores and MLOps pipelines; communicate results to business leaders; mentor peers and interns.

The role involves designing and implementing large-scale ML solutions for personalized music experiences, collaborating with cross-functional teams to enhance user interactions and fulfill music recommendations.

Oversee AI/ML project development, mentor teams, develop machine learning models, and promote AI solutions for healthcare optimization.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

Lead the technical vision of ML Engineering, overseeing data infrastructure, ML pipelines, and team management to transition AI models into production.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Lead cross-functional data science projects for manufacturing, integrating equipment/process knowledge with analytics. Develop, deploy, and document ML and computer vision solutions within MES and IT/OT ecosystems, qualify automation systems, drive continuous improvement, mentor junior engineers, and present technical findings.