Top Machine Learning Jobs

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Develop and deploy signal-processing algorithms and machine-learning models that convert wearable sensor data into real-time physiological insights. Responsibilities include data analysis, model training, Python prototyping, optimized C/C++ implementation, embedded firmware integration, and production validation. The role requires collaboration with firmware, hardware, data science, and domain teams to optimize accuracy, power, memory, compute, and latency while conducting rigorous offline, laboratory, real-world, and on-device testing.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Own the AI roadmap for battery manufacturing, identifying, benchmarking, piloting, and scaling use cases that improve yield, quality, cost, and launch speed. Build proofs of concept and reference architectures for process optimization, defect vision, yield genealogy, and predictive maintenance. Establish reusable data, labeling, and model standards; evaluate vendors; guide engineering teams; align with IT and OT on security and scalability; and apply generative and agentic AI to plant workflows.

Develop and deploy AI/ML solutions for workforce and HR challenges, from research and validation through production delivery and monitoring. Partner with product managers, engineers, and business experts to define needs, build technical solutions, evaluate model performance, and establish verification systems for accuracy, safety, and reliability. Conduct research advancing AI and data science capabilities while supporting strategic initiatives across a global financial institution.

Develop and improve reliable LLM and deep learning capabilities, build scalable real-time machine learning models, advance Responsible AI practices, and define technical strategies for deep learning, RAG, and LLMs. Collaborate with AI scientists and engineers, conduct ML and reinforcement learning research, fine-tune models, and drive adoption of AI best practices across service teams.

Design, build, and operate scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. Develop reliable software and APIs, improve production reliability and performance, resolve technical issues, and establish scalable platform architecture. Partner with engineering stakeholders to drive adoption of shared ML infrastructure and safely integrate AI/ML-enabled solutions.

Designs, develops, tests, validates, and deploys AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Builds data and model-training pipelines, leverages cloud platforms, evaluates model performance, documents methodologies, collaborates with technical teams, and guides prototype-to-production transitions. Requires five years of relevant experience and an active TS/SCI clearance with polygraph.

Develop, train, and evaluate machine learning and deep learning algorithms for audio applications. Research audio ML and digital signal processing methods, prototype algorithms for software and hardware platforms, curate datasets, and collaborate with interdisciplinary researchers and engineers. The role includes evaluating audio technologies, presenting findings, and potentially contributing to Bose products, patents, and publications.

Research and develop machine learning and deep learning algorithms for audio applications, including speech enhancement, spatial audio, voice pickup, and hearing augmentation. The intern will build datasets, prototype and integrate models into software or hardware, evaluate audio algorithms, collaborate with interdisciplinary teams, and present findings. Work may contribute to Bose products, patents, and research publications.

Build and lead production machine learning capabilities for Snapchat+ subscription growth and monetization. Responsibilities include developing personalization, ranking, propensity modeling, offer decisioning, retention, and lifecycle optimization systems; translating ambiguous business problems into ML solutions; conducting experimentation; deploying scalable services; and establishing technical direction. The role partners with Product, Data Science, Backend, and Mobile Engineering teams while measuring impact on revenue, subscriber value, and retention.

Develop production-ready machine learning and signal-processing algorithms for wearable physiological sensor data. Responsibilities include designing and evaluating deep learning models, analyzing large datasets, optimizing algorithms for embedded edge deployment, and balancing accuracy, power, memory, latency, and scalability. The role owns projects from research and prototyping through validation, deployment, monitoring, and continuous improvement while collaborating with scientists, engineers, hardware experts, and product teams.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Leads Adyen’s Credit Risk Modeling team, overseeing the design, productionization, deployment, monitoring, and improvement of machine learning models and rule-based decision systems for credit underwriting. The manager provides hands-on technical leadership, mentors machine learning scientists and data engineers, shapes architecture and product roadmaps, collaborates cross-functionally, recruits team members, and builds an inclusive, high-performing engineering culture.

Leads AI strategy, teams, and production-scale machine learning initiatives. Responsibilities include establishing MLOps practices, integrating AI technologies, guiding build-versus-buy decisions, delivering intelligent solutions, implementing governance and responsible AI, and driving adoption across products and enterprise platforms. The role requires cross-functional leadership, technical expertise in machine learning and advanced AI methods, stakeholder influence, mentoring, and delivery of measurable business outcomes.

Leads development, optimization, implementation, and evaluation of AI/ML algorithm solutions for complex datasets and national security applications. The role focuses on computer vision, 3D modeling, multimodal sensor fusion, open-set recognition, and deployment across cloud and edge environments. Responsibilities include collaborating with researchers, engineers, and academic partners; transitioning prototypes into operational capabilities; and communicating results to technical and non-technical audiences.

Designs, develops, deploys, and optimizes AI and machine learning solutions for business and technical challenges. Responsibilities include anomaly detection, predictive maintenance, root-cause analysis, chatbot operations, digital-twin development, closed-loop simulations, model evaluation, cloud deployment, and cross-functional integration. The role also researches emerging AI techniques, analyzes data, documents architectures, and troubleshoots conversational agents.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Develop and implement advanced machine learning algorithms and models for Atlassian products. Design system and model architectures, conduct experiments and evaluations, collaborate with product, engineering, and analytics teams, and mentor emerging machine learning engineers. The role also contributes to shared AI infrastructure and capabilities supporting search, knowledge discovery, and conversational experiences across the company.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, product owners, and quality engineers to design, implement, test, and deliver scalable software. Apply AI/ML techniques, high-performance algorithms, object-oriented programming, modular design, unit testing, profiling, and code optimization to improve enterprise workflows and user experiences.

Leads machine learning and software engineering teams building models, agentic systems, data pipelines, and ML infrastructure for advertising integrity and recommendations. Owns technical strategy, roadmap, model quality, reliability, privacy, and responsible AI. Partners cross-functionally to deliver measurable outcomes, scales LLM-powered ad review, contributes technically, and hires and mentors high-performing engineers.

Develop production LLM-powered agents and machine learning solutions for customer risk identification and threat detection. Translate threat-research methods into agent logic, build data requirements and pipelines, evaluate precision and recall, backtest and tune models, and deploy and monitor reliable solutions using CI/CD. Collaborate with threat researchers and data engineers while addressing production reliability, observability, data quality, cost, latency, and accuracy.