Top Machine Learning Engineer Jobs

Build and operate scalable machine learning systems for Atlassian’s Growth organization, including personalized recommendations, ranking, decisioning, experimentation, and customer journey optimization. Partner with product, engineering, data science, analytics, marketing, and sales teams to translate growth opportunities into measurable production capabilities. Develop datasets, features, models, evaluation frameworks, and monitoring systems while optimizing for customer value, fairness, quality, and long-term business outcomes.

Build and operate scalable machine learning systems for personalized recommendations, growth journeys, cross-product expansion, and sales experiences. Design models, datasets, features, evaluation frameworks, and decisioning services; run offline policy evaluations and online experiments; and monitor attribution, latency, quality, and fairness. Partner with product, engineering, data science, analytics, marketing, and sales teams to turn ambiguous growth opportunities into measurable production capabilities and strategic ML initiatives.

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.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

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.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

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.

Develop a modular robot autonomy stack combining Vision-Language-Action models with manipulation modules. Build safety layers, data pipelines, model-agnostic interfaces, and high-performance inference software for production robot fleets. Advance dexterous manipulation through reinforcement learning, imitation learning, and novel research while supporting real-world customer applications.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

Lead the technical vision and long-term machine learning strategy for Consumer Incentives. Design personalization, causal measurement, experimentation, and constrained optimization solutions; architect scalable end-to-end ML systems; evaluate reinforcement learning, foundation models, and generative AI; and drive cross-functional initiatives from formulation through production. Mentor technical leaders, establish engineering best practices, and connect model improvements to measurable consumer and business outcomes.

Lead the technical direction for Uber’s rider pricing machine learning systems. Develop and improve pricing and promotion-targeting models, optimization algorithms, and production serving systems. Own technical roadmaps, collaborate cross-functionally, mentor engineers, and lead teams delivering scalable real-time and offline solutions. Apply deep learning, generative AI, causal modeling, personalization, and reinforcement learning to improve ridership, revenue, and profitability.

Own the technical direction and long-term architecture of Copilot Discover’s AI-driven content, ranking, personalization, and discovery platforms. Design scalable systems for ingesting, enriching, ranking, personalizing, and serving content across multiple surfaces. Establish architecture, reliability, testing, monitoring, and operational standards while influencing teams across Microsoft. Partner with product, design, data science, privacy, trust, and engineering leaders to deliver secure, scalable, cost-efficient, and personalized experiences.

Build and productionize agentic AI and machine learning platforms for employer market-access use cases. Responsibilities include multi-agent orchestration, data and ML pipelines, model serving, deployment, observability, evaluation, governance, partner integrations, and secure handling of healthcare data. The role partners with data scientists, product leaders, and business teams to create reusable, scalable infrastructure and reliable AI applications in a regulated environment.

Build and integrate Atlas humanoid robot agent systems across harness design, context engineering, tool use, memory, evaluation, observability, dashboards, and diagnostics. Develop reliable planning and reasoning capabilities using VLMs and agentic systems, benchmark performance and safety, collaborate with robotics and product teams, and ship full-stack applications for real-world use cases.

Design, build, and operate scalable production machine learning platforms and MLOps systems. Own the ML lifecycle, including feature engineering, deployment, monitoring, governance, retraining, model versioning, and real-time inference. Build Feature Stores, Model Registries, CI/CD/CT pipelines, and data pipelines across cloud and streaming environments. Ensure performance, reliability, observability, security, regulatory compliance, and explainability while collaborating with data, software, and business teams and mentoring junior engineers.

Design, test, deploy, monitor, and improve machine learning models and algorithms supporting compliant data management applications. Lead model architecture, data pipeline integration, metrics interpretation, governance, and scalable production solutions. Serve as a primary AI/ML point of contact while leading geographically distributed teams and communicating with customers and stakeholders.

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.

Own the machine learning model lifecycle from experimentation through production deployment. Train and evaluate generative image and video models, design evaluation suites, build curated datasets from user activity and expert feedback, and develop compact in-house models optimized for speed and quality. This individual contributor role requires ownership of modeling work in a small team and practical experience delivering generative or computer vision systems.

Own machine learning modeling for a software platform, adapting large language models, building training data pipelines, and developing evaluation methods. Run experiments, refine models, and collaborate with researchers and systems engineers to deploy dependable models in production. The role focuses on electronic design automation and hardware design data, including netlists, RTL, surrogate models, and graph neural networks. It also involves post-training LLMs, distributed training or inference, and model optimization.

Build and deploy production machine learning and computer vision systems for edge devices. Responsibilities include data preparation, model selection and optimization, inference pipeline development, MLOps infrastructure, monitoring, customer solution delivery, and mentoring. The role emphasizes latency, throughput, accuracy, cost, and reliability tradeoffs for physical AI applications.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Design, build, deploy, monitor, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Apply modeling, feature selection, validation, responsible AI, and explainability practices while collaborating with product, data science, and engineering teams in an Agile environment.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.