Top Machine Learning Jobs

Leads the design, development, deployment, and operationalization of enterprise AI/ML, generative AI, and automation solutions. Provides technical leadership across architecture, engineering practices, evaluation, monitoring, governance, security, and responsible AI. Partners with cross-functional stakeholders, mentors engineers, establishes reusable standards, evaluates production readiness, and ensures scalable, reliable, compliant, and cost-effective delivery of AI/ML capabilities.

Build scalable data and machine learning pipelines for cloud-based model training and deployment. Develop, fine-tune, and productionize traditional, deep learning, generative AI, multimodal, and agentic systems. Implement feature engineering, event-driven ETL/ELT, model monitoring, CI/CD, RAG, prompt engineering, and REST APIs. Optimize model performance and compute costs while collaborating with Product and Engineering teams on enterprise Generative AI adoption and roadmap development, including applications for semiconductor manufacturing data.

Build software connecting Fabri’s digital manufacturing process, including manufacturing data collection, process analytics, workflow automation, machine integration, backend services, and internal tools. Develop and deploy machine-learning models for outcome prediction, anomaly detection, inspection, and process monitoring. Analyze process data and geometry, improve manufacturing quality and throughput, and collaborate directly with operators, engineers, and manufacturing teams in an on-site factory environment.

Lead design and deployment of production-grade ML systems (ranking, retrieval, LLMs). Drive architecture decisions, run experiments and evaluations, build scalable feature pipelines and monitoring, mentor engineers, and collaborate cross-functionally to integrate AI into products and scale ML capabilities.

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.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

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.

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.

Build, deploy, optimize, and monitor production machine learning models and infrastructure. Develop ML applications, data pipelines, cloud architectures, and distributed systems using technologies such as PyTorch, TensorFlow, Spark, Ray, and Kubernetes. Collaborate with Product and Data Science teams, automate testing and deployment, maintain model governance, and apply responsible and explainable AI practices.

Lead Cisco’s agentic AI evaluation strategy from architecture through production. Design layered evaluation systems using automated checks, LLM judges, human calibration, simulation, synthetic data, and adversarial testing. Monitor live traffic for regressions and drift, convert production traces into test cases, and validate fixes through shadow and A/B testing. Establish measurement standards across teams, manage datasets and experiment tracking, mentor engineers, and represent AI evaluation practices to senior leadership.

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.

Develop and scale machine learning and optimization systems for Uber's rider pricing, discounts, promotions, and personalized messaging. Build real-time and offline models and infrastructure, apply deep learning and generative AI, optimize Spark-based data workflows, and collaborate with product and science teams to deliver measurable revenue and growth outcomes.

Lead machine learning and optimization initiatives for Uber’s rider pricing systems. Develop, improve, evaluate, and deploy scalable pricing and promotion-targeting algorithms. Own technical problems end to end, shape product and technical roadmaps, collaborate across ML, serving, and product teams, and mentor engineers. Apply deep learning, generative AI, causal modeling, representation learning, personalization, and reinforcement learning to improve pricing, affordability, ridership, and revenue.

Design and deploy quantitative market-making and systematic trading models across FICC products using statistical methods and advanced machine learning. Build calibration frameworks, risk management and portfolio optimization tools, work with large time-series data, and collaborate with traders, quant developers, and engineering to integrate pricing, trading, and risk systems.

Build algorithms, metrics, scalable pipelines, and production inference systems that analyze, filter, deduplicate, and curate large-scale speech and audio datasets. Translate research into production software, evaluate model and dataset quality, architect resilient ML systems, and collaborate with operations and cross-functional teams to improve customer-facing data products.

Own EmberWorks’ wildfire fire-behavior modeling function, evaluating existing models and developing empirical, physical, and machine-learning approaches. Build production models using airborne thermal observations and fuel, weather, and terrain data; define validation standards; diagnose model and data issues; specify cloud GPU and distributed-computing needs; and collaborate with platform engineers and academic fire-science partners.

Develop and deploy machine learning capabilities for radar systems, including labeling automation, active learning, classification, tracking, data augmentation, and self-supervised models. Build training and evaluation pipelines, assess robustness across rare targets and field conditions, analyze failures, and integrate models into production radar products. Optimize accuracy, latency, computation, and reliability for embedded, resource-constrained systems.

Designs and operates production-grade generative AI, machine learning, and clinical data platforms for regulated life sciences environments. Builds MLOps/LLMOps and scalable data pipelines, integrates structured and unstructured clinical data, implements quality monitoring and interoperability solutions, and ensures compliance with HIPAA, GDPR, 21 CFR Part 11, CDISC, HL7, and FHIR. Partners with clinical, regulatory, data, and IT stakeholders to deliver validated, traceable, and scalable AI-enabled transformation solutions.

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.

Develop production-grade machine learning models and infrastructure to evaluate autonomous vehicle behavior in simulation and real-world environments. Collaborate across teams to deploy state-of-the-art ML systems, improve model quality, and support large-scale data and simulation workflows.

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.

Develop and deploy machine learning, computer vision, and data-driven features across embedded, cloud, and application platforms. Responsibilities include building sensor-data pipelines, integrating models into user interfaces, evaluating performance on edge devices, developing APIs, supporting proof-of-concepts, maintaining CI/CD infrastructure, and collaborating with machine learning, UX/UI, data operations, and software engineering teams.