Top Machine Learning Jobs

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

This is a senior machine learning engineering role at Atlassian, but the job description contains no specific responsibilities or qualification details. The position offers flexible work arrangements where Atlassian has a legal entity and includes location-based compensation, benefits, bonuses, commissions, and potential equity.

Lead the design, development, deployment, and optimization of production machine learning systems for search, recommendations, personalization, NLP, deep learning, and AI applications. Own projects across the full ML lifecycle, design scalable architectures and evaluation strategies, collaborate with product and engineering teams, translate research into reliable systems, and mentor other engineers.

Build and deploy enterprise AI/ML solutions, including generative AI, agentic systems, RAG pipelines, LLM-powered chatbots, and model-serving microservices. Develop model training, evaluation, monitoring, and production integration workflows while collaborating with data science, engineering, MLOps, and product teams. Prototype emerging AI techniques, improve scalability and performance, and support critical IT escalations. The role requires strong Python and machine learning expertise, with preferred experience in Azure, Databricks, Kafka, Kubernetes, and GPU-based model deployment.

Conducts machine learning research focused on AI tokenomics, LLM routing, adaptive controllers, compute budgeting, and sustainable AI. Builds prototypes, benchmarks, telemetry, simulations, and evaluation environments across models and deployment platforms. Performs experiments and statistical analyses, collaborates with researchers and engineers to transfer technologies into AI platforms, and prepares publications, presentations, and patent disclosures. May lead research prototypes and mentor junior professionals and interns.

Design, build, and operate production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, retrieval and RAG pipelines, model integration and evaluation, safety guardrails, observability, governance, architecture ownership, code reviews, and mentoring engineers.

Design, develop, deploy, and maintain production AI/ML solutions for behavioral health workflows. Translate stakeholder needs into prototypes, technical designs, user stories, and engineering tasks. Build scalable models and data pipelines, integrate AI into enterprise applications, and apply responsible AI, coding, testing, MLOps, and cloud deployment practices. Collaborate directly with clinicians, business users, operations leaders, data scientists, and software engineers while owning complex AI features and user-facing delivery.

Designs, develops, deploys, and maintains production AI/ML solutions for healthcare, including predictive models, generative AI, statistical analysis, MLOps pipelines, and model monitoring. Builds scalable APIs and microservices, applies responsible AI practices, and collaborates with engineering, data, product, and clinical teams to improve care delivery and operational efficiency.

Design and build scalable, reliable ML training infrastructure for advanced AI research and model development. Optimize distributed training performance, resource utilization, data loading, and costs across heterogeneous GPU and cloud environments. Improve platform observability, debuggability, operational excellence, and user experience. Collaborate with ML engineers, research scientists, and cross-functional partners to integrate new technologies supporting intelligent driving systems.

Paid summer internship in NBCUniversal’s Media Tech program, focused on data, AI, and machine learning initiatives. Interns may support content research and measurement, data products, AI and data engineering, SAP data foundations, forecasting analysis, and enterprise AI solutions. The role offers hands-on technical experience, mentorship, networking, and career development in Universal City, California, with a 40-hour weekly commitment.

Designs, develops, tests, validates, and deploys AI/ML models and algorithms for intelligence and mission-critical applications. Responsibilities include feature engineering, data pipeline development, federated data retrieval, model monitoring, and validation against real and simulated data. The role collaborates with data engineers, assesses emerging AI methodologies, briefs stakeholders and customers, and supports deep learning, NLP, computer vision, and LLM use cases across classified enterprise environments.

Lead the technical direction, architecture, and roadmap for production agentic AI systems processing global trade data. Build ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback pipelines. Adapt and post-train large language models, improve system reliability, mentor senior engineers, shape ML hiring standards, and collaborate with product, engineering, data science, and customers to deliver practical risk, compliance, and enforcement capabilities.

Lead the technical direction and development of production machine learning and agentic AI systems for global trade risk, compliance, and enforcement. Responsibilities include designing ingestion and inference pipelines, adapting and post-training LLMs, building evaluation and observability systems, mentoring engineers, setting architecture across teams, and partnering with product and customers to deliver measurable capabilities.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

Leads the architecture, development, deployment, and monitoring of enterprise machine learning and AI solutions. Builds MLOps platforms and pipelines, evaluates emerging AI technologies, establishes CI/CD and responsible AI practices, and collaborates with software engineering, data science, and product teams. Provides technical leadership and mentorship while delivering scalable, reliable production models across the healthcare enterprise.

Designs, develops, and deploys computer vision and machine learning pipeline components for GEOINT data. Responsibilities include synthetic data generation with generative AI, data engineering, model training and production deployment, cloud and Kubernetes workflow operations, system architecture, production support, and briefing teams on project progress and results.

Leads multiple AI/ML engineering teams delivering enterprise AI, generative AI, agentic AI, and automation solutions. Oversees platform strategy, software delivery, responsible AI governance, cloud adoption, and operational excellence. Partners with product and engineering leaders on healthcare solutions, mentors technical teams, and manages AI initiatives spanning claims automation, conversational AI, predictive analytics, and intelligent workflows.

Design, develop, deploy, and optimize scalable AI/ML solutions, including LLM applications, RAG pipelines, AI agents, and MLOps workflows. Build production-grade ML pipelines, automation frameworks, monitoring, evaluation, and governance capabilities using Python and AWS services. Collaborate with technical and business teams to integrate reliable AI solutions into production systems and continuously improve model performance, scalability, and compliance.

Build and deliver cloud-based AI and machine learning solutions for ServiceNow’s enterprise search platform. Develop high-quality product features using efficient algorithms, collaborate with developers, product managers, and quality engineers, and own software through design, implementation, testing, and deployment. Ensure solutions are reliable, scalable, performant, modular, and well tested while improving intelligent search and enterprise workflow experiences.

Develops AI/ML automation solutions, data pipelines, agentic workflows, malware analysis automation, and integrations across AWS, Databricks, and cloud services. Builds ML data preparation, feature engineering, monitoring, APIs, and pilot-to-production workflows. Requires Python, AWS pipeline, LLM, Amazon Bedrock, integration, and orchestration experience, plus active Secret clearance, U.S. citizenship, and DHS suitability eligibility.

Leads end-to-end generative and agentic AI initiatives, from research and experimentation through production deployment. Designs foundation-model solutions, evaluation frameworks, semantic modeling standards, and responsible AI controls. Partners with product, engineering, risk, and business stakeholders to define outcomes, optimize reliability and performance, and deliver scalable AI services. Mentors engineers and data scientists while communicating technical recommendations to diverse audiences.

Leads a team developing AI capabilities for CrowdStrike’s cross-platform endpoint sensor. Owns the AI-on-endpoint roadmap, including machine learning model operationalization, telemetry data pipelines, on-device inference, architecture, feature delivery, and cross-functional collaboration. Manages hiring, mentoring, engineering quality, testing, performance, and releases across Windows, macOS, and Linux sensor platforms.

Leads the strategy, architecture, engineering, and production delivery of AI, machine learning, and generative AI solutions. Directs cross-functional teams, establishes engineering and responsible AI standards, oversees LLM applications, RAG, agents, evaluation, deployment, monitoring, security, and lifecycle management. Manages talent, budgets, resources, stakeholder alignment, and continuous improvement while connecting AI investments to business value.

Build and scale enterprise machine learning platforms and production ML systems. Develop models, data pipelines, distributed training workflows, Kubernetes operators, SDKs, and developer tooling using Python and cloud technologies. Deploy, monitor, retrain, and govern ML models while applying CI/CD, testing, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams, and contribute to open-source Kubeflow.

Design, deploy, and operate production agentic AI systems for healthcare applications. Build multi-agent orchestration, backend services, LLM integrations, healthcare guardrails, and cloud infrastructure. Collaborate with product and clinical stakeholders to evaluate workflows, improve performance, and monitor metrics such as latency, token usage, and user satisfaction. Apply responsible AI practices and support systems serving millions of health plan members.