Top Machine Learning Jobs

As a Robotics ML Research Engineer, you will develop algorithms for robot manipulation, lead software infrastructure, and innovate in robotics learning research.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

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.

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 Software Engineer intern, you'll work on AI-driven projects spanning data management, onboard systems, ML infrastructure, simulations, and technical infrastructure, collaborating with experts in various fields.

Build and deploy machine learning solutions (recommendation systems, NLP, time series, optimization), collaborate with stakeholders and engineers, set technical strategy for data-science products, run A/B tests, and communicate results to drive business impact.

Support USASOC data science efforts by developing data-driven and predictive ML solutions, evaluating data sources, building APIs and models, monitoring model performance, collaborating with stakeholders, and mentoring junior staff. Work is on-site at Fort Bragg and requires TS/SCI clearance and 5-8 years of experience.

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.

Lead roadmap and product strategy for vehicle autonomy. Define autonomy behavior, UX, data and ML lifecycle, benchmarks, and deliver cross-functional capabilities from data collection through deployment and monitoring.

The role involves developing synthesis-aware virtual screening workflows for small-molecule drug discovery, integrating various modeling and design strategies.

Conduct ML research for query understanding, ranking, clustering, and AI-powered search. Prototype and productionize algorithms, define data and evaluation requirements, design scalable systems, collaborate with engineers, run experiments, and publish patents/papers to integrate state-of-the-art ML into Yahoo Search products.

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.

Design, develop and maintain production-ready machine learning and AI solutions to support student engagement, recruitment and service delivery. Build scalable data pipelines, integrate models into enterprise environments, optimize ETL/ELT processes, and translate analytical outputs into actionable insights for stakeholders. Collaborate with IT and business teams to deliver measurable business impact.

Lead AI solutions development in finance using statistical methods and machine learning. Mentor, implement models, ensure compliance, and collaborate for effective tool integration.

Design, build, and deploy transformer-based ML and LLM systems to automate risk investigations and decisioning. Own end-to-end pipelines (data ingestion, prompt tuning, orchestration, productionization, monitoring) and collaborate with product, engineering, and operations to deliver copilot/autopilot workflows that improve analyst productivity and eliminate manual reviews.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.

Lead the design and advancement of AI platform infrastructure, focusing on distributed training, efficiency, and optimization of ML models. Collaborate with teams and guide architectural decisions.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Hands-on ML internship working on real production ML projects with mentor guidance. Build and optimize ML models and workflows using a large GPU cluster for noisy financial data; collaborate with engineers and attend ML-specific training and technical interviews.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.