Top Senior Machine Learning Jobs

Develops and deploys physics-based, statistical, and machine learning models for semiconductor manufacturing. Builds AI-enabled VPM and SPOCK workflows using machine learning, generative AI, LLMs, and agentic AI. Responsibilities include model lifecycle management, automation, validation, monitoring, process optimization, yield improvement, and collaboration with global engineering teams. The role applies predictive and prescriptive modeling to DRAM, HBM, and technology-transfer applications.

Leads four teams responsible for evaluating autonomous-driving performance, classifying scenarios, analyzing subsystem failures, and translating results into engineering and release decisions. Owns roadmap, staffing, technical quality, metrics, statistical rigor, and stakeholder trust. Hires and develops managers, engineers, and data scientists while guiding evaluation strategy, regression detection, simulation and road-test analysis, and AI-assisted classification initiatives using VLMs and LLMs.

Develops core AI capabilities for Atlassian’s DevAI products, including LLM-based methodologies, prompt engineering, end-to-end AI features, data preprocessing, feature engineering, experimentation, model evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and cross-functional partner teams to solve complex problems and apply advances in machine learning and AI.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.