Top Machine Learning Jobs

Leads end-to-end delivery of production AI and machine learning use cases, including LLM and agentic systems. Owns architecture, deployment, evaluation, guardrails, monitoring, reliability, and operationalization. Develops reusable AI patterns and reference architectures, partners with business and technical stakeholders, influences without formal authority, and mentors engineers. The role also contributes to AI governance, prioritization, engineering standards, and organizational adoption.

Lead the architecture and development of a GenAI search platform supporting conversational, multi-turn question answering and enterprise search. Build machine learning and LLM-enabled features, ranking frameworks, evaluation methodologies, and scalable platform abstractions emphasizing quality, security, privacy, and performance. Provide technical direction, coach engineers, collaborate across product and engineering teams, and help recruit and grow the team.

Develop and deploy machine learning algorithms and models for Atlassian products. Design model and system architectures, run experiments and evaluations, solve product problems with AI/ML, and contribute to Atlassian Intelligence features. Collaborate with product, engineering, and analytics teams while building scalable, production-quality solutions using large datasets, deep learning, and LLM-related applications.

Develop and scale machine learning algorithms, models, infrastructure, and data pipelines supporting AI features across Atlassian products. Responsibilities include designing model and system architectures, conducting experimentation and evaluations, collaborating with product, engineering, and analytics teams, building production-quality solutions using large datasets, and mentoring junior ML engineers.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Validate machine learning, NLP, generative AI, and agentic AI models within Model Risk Management. Responsibilities include reviewing data, sampling plans, modeling frameworks, replications, performance assessments, monitoring, and test model development; building reusable validation code; preparing governance reports; and communicating with stakeholders, regulators, and auditors.

Validates NLP, generative AI, and agentic AI models within Model Risk Management. Responsibilities include reviewing data, sampling plans, modeling frameworks, replications, performance assessments, monitoring, and test model development; creating reusable validation code; preparing governance and regulatory reports; and communicating findings to stakeholders, regulators, and auditors.

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.

Six-month research co-op developing and evaluating multimodal sensor fusion, DSP, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; developing models, training datasets, and evaluation infrastructure; and implementing real-time prototypes. The role requires Python, MATLAB, TensorFlow or PyTorch, signal processing, deep learning, acoustic modeling, and real-time processing knowledge.

Machine Learning/DSP Research Engineer Intern developing and evaluating multimodal sensor fusion, digital signal processing, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; building machine learning models and training datasets; and developing evaluation infrastructure for real-time 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.

Develops end-to-end AI features for Atlassian’s developer AI products, including LLM integration, prompt engineering, machine learning algorithms, data preprocessing, feature engineering, experimentation, evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and partner teams to solve complex problems and apply advances in AI and machine learning.

Leads the design, development, and delivery of production machine learning and AI systems for government and commercial customers. Responsibilities include data shaping, analytics, model development and optimization, production deployment, technical planning, customer engagement, and mentoring cross-functional engineering teams. The role requires translating complex technical concepts for internal and external stakeholders and traveling up to 25% of the time.

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 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 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.