Top Senior Machine Learning Jobs

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

Leads applied AI and machine learning initiatives, architecting scalable production systems using LLMs, AI agents, RAG, real-time predictions, and big-data processing. Provides technical direction, develops and reviews secure code, guides model and platform quality, and advises executives on ML strategy. Responsibilities include MLOps, model monitoring, data-quality improvements, production feedback loops, and building reliable solutions that improve operational efficiency and customer outcomes.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Design and build large-scale mapping algorithms and geospatial data systems for automated map reconstruction and maintenance. Develop lane, road-network, geometry, topology, and map-conflation systems; process sensor and third-party data through distributed pipelines; apply machine learning and computer vision for feature extraction and change detection; and create automated validation and quality systems. Partner with perception, localization, simulation, and platform teams while mentoring engineers and driving technical problems into production.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.

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.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Build and scale customer-facing generative AI applications for Atlassian Rovo Chat. Develop agent harnesses that improve quality, reliability, and latency; apply advanced machine learning innovations; lead complex projects from technical design through launch; mentor senior engineers; and collaborate cross-functionally with internal teams and customers.

Lead development of cloud and on-premises data, ETL, analytics, and machine learning solutions. Responsibilities include building scalable data pipelines with AWS services, deploying serverless applications, automating infrastructure with Terraform, developing fraud detection models, and implementing supervised, unsupervised, reinforcement, and deep learning systems. The role also involves model monitoring, data governance, security, compliance, cost optimization, and integration with enterprise analytics and business intelligence platforms.

Own and evolve Braze’s ML platform for globally scaled training pipelines, model serving, deployment, orchestration, and observability. Lead complex infrastructure initiatives, improve reliability and cost efficiency, manage incident response, and establish production quality standards. Partner across teams, shape technical strategy, mentor senior engineers and data scientists, and connect platform decisions to customer and business outcomes.

Lead Adyen’s Personalize Intelligence team developing machine-learning systems for real-time Checkout optimization and AI products. Provide technical leadership on architecture, reliability, and scalable services; coach machine learning scientists and data engineers; define goals and product roadmaps with Product; collaborate across Design, Engineering, Data, and leadership; monitor system performance; foster an inclusive, high-performing culture; and recruit new team members.

Build enterprise-scale machine learning and agentic search systems enabling AI agents to discover, retrieve, evaluate, and synthesize information. Work spans retrieval, ranking, LLM reranking, multi-step search, tool intelligence, grounding, citations, evaluation, and production infrastructure while addressing permissions, freshness, latency, trust, and multi-tenant requirements.

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 technical direction for ML-powered search serving, including ranking, retrieval, inference pipelines, and serving infrastructure. Drive improvements in search quality, latency, cost, and reliability through experimentation and model optimization. Establish ML systems standards for evaluation, rollout safety, and observability; pursue advanced search initiatives; mentor senior engineers; and partner across search, ML platform, and product teams.

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.

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 a cross-functional team developing behavior evaluation, human benchmarking, and behavioral inference capabilities for autonomous vehicles. Designs and deploys production ML models, establishes technical roadmaps, and integrates behavior critics into simulation-based validation, release decisions, and performance monitoring. Partners with autonomy, simulation, safety, and product teams to assess driving behavior against human expectations for safety, comfort, and intuitiveness.

Leads software engineering for a Machine Learning I/O team, developing secure production code and guiding design, testing, debugging, and operational stability. Drives enterprise adoption and governance of AI-assisted development tools, establishes validation standards for AI outputs, promotes secure and responsible AI practices, and influences technical decisions across teams. Provides technical direction, serves as a subject matter expert, and advocates for scalable cloud-native engineering practices.

Leads AI/ML engineering teams and technical strategy for enterprise-scale healthcare and pharmacy solutions. Oversees development, deployment, governance, MLOps, security, and operations for generative AI, LLM, NLP, predictive analytics, and agentic AI capabilities. Partners with product, engineering, data science, and executives to align technology with business goals, evaluate emerging technologies, improve automation, and develop engineering talent.

Leads the AI/ML engineering roadmap and provides technical direction across machine learning, generative AI, agentic AI, NLP, and predictive analytics. Designs, trains, evaluates, and deploys production models; builds LLM, RAG, and AI-agent applications; establishes MLOps pipelines, monitoring, governance, responsible AI, and cloud infrastructure. Mentors engineers and data scientists, partners with business leaders, and communicates solutions and outcomes to executives.

Leads the strategy and execution of AI model performance and on-vehicle inference for autonomous driving. Responsibilities include managing engineering leaders, setting performance budgets, optimizing models and GPU systems, establishing benchmarking and profiling practices, detecting regressions, building performance dashboards, and guiding cross-functional architecture and production decisions. The role also oversees hiring, coaching, staffing, KPIs, technical leadership development, and trade-offs across latency, memory, throughput, accuracy, power, and numerical parity.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

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.