Top Machine Learning Jobs

Leads enterprise AI/ML engineering teams and defines strategy for AI-ready data and context platforms. Owns semantic architecture, ontologies, knowledge graphs, metadata, data quality, governance, and contextual data products supporting generative AI, intelligent search, decision support, and agentic workflows. Partners with senior stakeholders, translates AI priorities into platform capabilities, evaluates emerging technologies, and mentors technical leaders.

Develop, test, and deploy machine learning models; support feature engineering, preprocessing, evaluation, and automated training and monitoring pipelines. Collaborate with engineering and product teams to define AI solutions, research methods to improve performance, and support urgent IT escalations. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Leads Snap’s Content Machine Learning Engineering organization, setting multi-year strategy for recommendation, ranking, content understanding, reliability, and experimentation. Oversees distributed teams, develops organizational roadmaps, partners with executives and cross-functional leaders, influences scalable architecture decisions, and builds high-performing engineering teams. The role requires strong expertise in machine learning, deep learning, AI, ranking systems, and large-scale engineering leadership.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.

The job description content is not provided. Based on the title, this role would manage machine learning engineering teams and initiatives, including technical direction, project execution, and team development.

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

The job description content is missing. Based on the title, this role involves senior-level machine learning engineering. Atlassian offers flexible work arrangements, location-based compensation, benefits, bonuses, commissions, and equity.

Design, deploy, and monitor production-grade machine learning and deep learning models for healthcare applications. Build scalable data pipelines and automated workflows using Databricks and related platforms, leverage agentic coding tools, and integrate AI services into enterprise applications. Apply responsible AI, governance, security, and monitoring practices while collaborating with software engineers, data architects, and clinical or business stakeholders.

Lead the architecture, development, deployment, and operation of enterprise AI and agentic platforms on Azure. Build LLM, RAG, vector database, and multi-agent solutions while establishing MLOps, LLMOps, responsible AI, governance, security, monitoring, and reliability practices. Provide technical leadership, mentor engineering teams, collaborate with product and business stakeholders, and translate healthcare objectives into scalable AI capabilities.

Leads the architecture, development, and deployment of AI/ML integrations and scalable event-driven backend systems for distributed clinical applications. Builds Kafka-based services, secure APIs, database integrations, CI/CD pipelines, containerized cloud deployments, and observability platforms. Develops agentic AI, LLM, and RAG solutions while collaborating with data scientists, engineers, and product stakeholders. Drives technical standards, design reviews, code quality, and continuous engineering improvement.

Lead technical execution of advanced networking, communications, and machine-learning systems for national-security applications. Translate mission needs into requirements, architectures, development plans, milestones, and test criteria. Lead multidisciplinary engineering and research teams, manage technical dependencies and risks, oversee system integration and demonstrations, and ensure quality, performance, security, maintainability, and reuse. Apply advanced machine-learning and statistical methods to real-time, resource-constrained networked systems while engaging customers, mission partners, and collaborators.

Build, deploy, and scale machine learning models and infrastructure for financial technology applications. Responsibilities include model development, feature and data pipeline engineering, cloud-based production deployment, monitoring, retraining, testing, CI/CD, model governance, and responsible AI practices. The role collaborates with Product and Data Science teams and uses distributed systems, containerized platforms, and cloud technologies to deliver resilient ML solutions.

Build and optimize large-scale machine learning training and inference systems for real-time trading applications. Responsibilities include distributed training pipelines, experiment automation, GPU and low-level performance optimization, low-latency inference, scalable model infrastructure, framework improvements, and evaluation of ML tooling. The role requires production software engineering, strong ML fundamentals, systems thinking, performance measurement, and collaboration with quantitative researchers and engineering specialists.

Designs, builds, and operates scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. The role delivers reliable software and infrastructure, improves platform performance and observability, influences architecture and technical standards, and partners with engineering stakeholders to safely integrate AI/ML solutions at scale.

Design, train, optimize, and deploy efficient machine learning models for consumer audio products and resource-constrained edge devices. Apply quantization, pruning, distillation, and model compression while balancing accuracy, latency, memory, compute, and power requirements. Collaborate with embedded, DSP, hardware, compiler, and software teams on deployment and integration, troubleshoot model issues, develop repeatable ML workflows, evaluate emerging technologies, document findings, and mentor interns and co-op engineers.

Develop and deploy enterprise AI/ML solutions, including GenAI, LLM, NLP, agentic frameworks, and traditional machine learning models. Build scalable Python inference APIs on Kubernetes, analyze large datasets with Python libraries and Databricks, and apply advanced SQL. Responsibilities include model evaluation, responsible AI, semantic search, performance monitoring, documentation, and use of AI-assisted development tools.

Design, develop, deploy, and operationalize enterprise-scale AI and machine learning solutions. Collaborate with engineering, product, and business teams to build scalable, secure, reliable, and responsible AI systems. Evaluate emerging technologies, participate in architecture and security reviews, troubleshoot complex applications, and mentor junior engineers. Apply cloud infrastructure, MLOps, modern programming languages, databases, and agentic AI capabilities to solve healthcare and business challenges.

Designs, develops, deploys, and supports production AI/ML solutions for healthcare operations. Builds scalable machine learning services, data pipelines, cloud applications, and MLOps workflows; implements CI/CD, monitors and optimizes deployed systems, evaluates emerging AI technologies, and collaborates with product, architecture, data science, and engineering teams. The role also mentors junior engineers and contributes to technical standards, code reviews, and architecture decisions.

Develop and deploy production machine learning and AI solutions for customer-facing applications. Build proofs of concept, integrate open-source tools such as MLflow, Spark, LangChain, and Kubeflow on Kubernetes, optimize models, and improve product capabilities. The role requires developing AI agents, collaborating with product and engineering teams, mentoring junior staff, documenting practices, and communicating effectively with leadership and distributed teams.

Research Engineer responsible for identifying weaknesses in voice AI models, defining improvement targets, designing evaluations, curating and generating data, fine-tuning deep learning models, and rapidly shipping production systems. The role emphasizes end-to-end ownership of customer problems, measurable production improvements, practical judgment, and fast experimentation. Candidates should have strong Python and PyTorch skills, production ML experience, and ideally experience with speech technologies, distributed training, publications, open source, or public benchmarks.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

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.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

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.

Build and evaluate applied machine learning and Generative AI solutions for product and business problems. Interns will contribute to models, agents, retrieval, experimentation, evaluation, and prototyping with mentorship from experienced engineers. Responsibilities include translating business needs into technical projects, analyzing errors and user feedback, measuring outcomes, and clearly documenting findings, trade-offs, limitations, and next steps.