Top Machine Learning Jobs

Open general application for Rapsodo, a sports analytics company using computer vision and machine learning. Seeking driven team players passionate about sports and technology to contribute to state-of-the-art analytics solutions and growth across product teams. Applicants encouraged to apply to join a fast-paced, innovative company partnered with major sports organizations.

Lead design, development, and deployment of machine learning models to drive finance revenue and predict spending/lending patterns. Apply causal inference, advanced analytics, and visualization to produce actionable insights, collaborate with stakeholders and validation teams, and ensure regulatory compliance.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

Research and develop improvements to machine learning models, collaborate with research and product teams, and evaluate model performance.

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

Lead end-to-end supply chain planning science: own forecasting tournaments, inventory placement optimization, vendor performance systems, and raw-material signals. Define methodology, production models, and iteration loops; drive AI-native workflows (LLM-aided EDA, agentic feature engineering), partner with planning operators and engineering, and mentor junior scientists to raise methodological standards and deliver measurable business impact.

Lead development of ML, anomaly detection, and predictive maintenance analytics for naval/defense systems. Build and validate data pipelines, cloud-based tools, dashboards, and reliability/failure models. Translate findings for technical and executive audiences, support system readiness and configuration analyses, contribute to proposals and reports, mentor team members, and travel periodically to remote sites.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Nine- to ten-week summer internship where interns receive training and work alongside quantitative strategists to build quantitative models, perform research and analysis, and apply mathematics, programming, and machine-learning techniques to solve financial-market problems within Core Engineering.

Design, test, and implement consumer credit underwriting and pricing strategies using data extraction, regression and machine learning. Perform segmentation, sensitivity and competitive analyses; build optimization frameworks; ensure data quality and automate reporting. Present insights to management and collaborate across teams to drive profitable growth and loss mitigation.

The ML Bioengineer will conduct research on next-generation clinical and genome language models, collaborating with experts to develop machine learning methodologies and analyze data while contributing to project milestones and documentation.

The Senior Staff Machine Learning Engineer will lead innovative AI engineering projects and optimize algorithms for e-commerce applications, collaborating across teams to drive significant business impact.

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.

Design and deploy scalable production ML systems powering Diffbot's Knowledge Graph. Improve model accuracy via research, feature engineering, and hyperparameter tuning; own end-to-end performance for 60+ ML problems; assess new research for production feasibility; collaborate on training data and error analysis with product and customers.

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 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.

Embed onsite with a major automotive customer to identify operational inefficiencies and design, build, deploy, and monitor AI/ML and optimization solutions integrated with TMS/WMS/ERP. Drive automation, translate data into actionable insights, iterate on models, and support adoption across technical and non-technical stakeholders in high-stakes production environments.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

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.

Develop and improve ML models for structural biology, lead computational discovery projects with external partners, translate research into scientific impact, design and execute protein engineering/discovery campaigns, collaborate with experimental scientists, and apply software engineering best practices.

Work with customers to define problems and design ML solutions; build and deploy predictive models (feature engineering, model selection, HPO) for big and streaming data; write clean, version-controlled code; apply geospatial ML best practices; create data visualizations and communicate results to non-technical stakeholders; stay current with machine and deep learning advances.

Develop and scale learning models for behavior cloning and reinforcement learning. Build data pipelines and evaluate model performance for integration into autonomous systems.

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.