Top Machine Learning Jobs

Design, develop, train, optimize, and deploy machine learning models, including NER, personalization, predictive, and LLM fine-tuning systems. Collaborate with engineering, product, and analytics teams to integrate AI features into Atlassian products. Conduct experimentation, evaluate model performance, monitor product metrics, and improve scalability and reliability. Contribute to foundational model development and advanced AI initiatives while communicating findings to senior leaders.

Designs, deploys, and monitors AI safeguards for prompt injection, jailbreaks, toxicity, sensitive topics, and data exposure. Builds evaluation datasets and metrics, integrates and fine-tunes detection models, balances quality, latency, and cost, develops configurable customer policies, and partners with Security to convert adversarial findings into mitigations. Requires extensive production software and applied machine learning experience, strong LLM safety knowledge, evaluation expertise, and advanced Python skills.

Design, develop, validate, and deploy machine learning, GenAI, LLM, and agentic solutions for enterprise healthcare operations. Build scalable Python inference APIs on Kubernetes, implement semantic search and retrieval capabilities, and maintain AI/ML pipelines. Evaluate emerging technologies, apply responsible AI practices, use AI-assisted development tools, and collaborate with technical and business stakeholders while ensuring strong documentation, coding standards, and performance monitoring.

Develop, optimize, implement, and evaluate AI/ML algorithm solutions for computer vision, 3D modeling, multimodal sensor fusion, and imagery recognition. Collaborate with research and engineering teams to transition prototypes into cloud- and edge-deployed operational capabilities supporting national security applications. Lead research initiatives, communicate technical concepts to varied audiences, and maintain high-quality Python software using modern development practices.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Develop machine learning and generative AI capabilities for conversational enterprise search, including multi-turn question answering, information retrieval, ranking, evaluation, and LLM-enabled features. Build scalable platform frameworks that help product teams prototype and deploy search applications. Establish metrics for search quality, security, privacy, and scalability while collaborating with product, design, infrastructure, data science, and engineering teams. Contribute to a high-performing and inclusive engineering culture.

Lead hands-on development of AI and machine learning solutions for asset and wealth management. Design scalable data pipelines, develop and productionize ML and LLM models, implement inference services, and deploy platforms to AWS or Azure. Collaborate with business, product, and technical teams while driving architecture, innovation, security, and adoption of shared AI capabilities.

Build production AI/ML software for supercomputers and AI infrastructure, including anomaly detection, forecasting, root cause analysis, automation, and operational intelligence. Develop services with Go, Python, and React; integrate machine learning models, LLM workflows, agentic systems, and analytics. Own the lifecycle from data engineering and model development through deployment, monitoring, and improvement while collaborating with engineering, architecture, product, and customers.

Build AI agent orchestration, routing, planning, memory, and action systems. Develop and deploy machine learning models for search ranking and proactive suggestions, integrate modern LLMs, and design evaluations to improve quality, speed, and cost. The role requires independent ownership, strong product sense, and collaboration in a fast-paced environment.

Leads development of production-grade AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, and predictive models. Designs, evaluates, deploys, and improves secure, scalable services while implementing responsible AI controls, observability, testing, and CI/CD practices. Integrates AI with enterprise data, APIs, cloud platforms, and digital channels; mentors engineers and drives initiatives from proof of concept through production.

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 enterprise AI-ready data and context platforms supporting generative AI and agentic workflows. Responsibilities include defining strategy and architecture for semantic models, ontologies, knowledge graphs, metadata, and contextual data products; integrating structured and unstructured data; establishing governance, quality, security, and interoperability standards; partnering with senior stakeholders; evaluating emerging technologies; and leading and mentoring multidisciplinary AI/ML engineering teams.

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 the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.

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.

Build, deploy, and operate production-grade multimodal machine learning services for document extraction, visual understanding, and agentic automation. Responsibilities include integrating LLMs, designing APIs and pipelines, deploying services on Kubernetes, improving scalability and observability, writing tests, evaluating model quality, resolving production issues, and collaborating across product and engineering teams.

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.

Leads AI strategy, teams, and production-scale machine learning initiatives. Establishes MLOps practices, model lifecycle management, governance, and ethical AI frameworks; evaluates external technologies; integrates AI across products and platforms; and drives measurable business outcomes. Provides technical leadership across deep learning, generative AI, NLP, computer vision, and predictive analytics while mentoring teams and influencing executive stakeholders.

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.

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.

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.