Top Machine Learning Jobs

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 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.

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.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

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 the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

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.

AI software engineering intern in Boeing’s Graduate Researcher Program, developing and evaluating learned models and contributing to machine learning applications for aerospace. The role involves preparing data, supporting machine learning development and deployment pipelines, and collaborating with distributed engineering teams. Candidates must be PhD students who have completed their first year by July 2027 and can work onsite full-time for 10–12 weeks during Summer 2027.

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.

Lead the Ranking & Relevance team of software and machine-learning engineers. Own retrieval, ranking, recommendation, and personalization systems that match patients with therapists. Drive the transition from filter-based matching to outcome-aware machine learning, establish shared marketplace objectives, improve experimentation and model monitoring, and build evaluation and drift-detection capabilities. Hire and develop engineers, set technical standards, and partner with product, data science, payer, and provider engineering teams in a sensitive healthcare domain.

Develop machine learning capabilities, custom analytics, and mission-critical AI solutions across image, video, text, geospatial, time-series, and structured data. Build and orchestrate data engineering pipelines for agentic and traditional architectures, collaborate with data scientists, software and DevOps engineers, contribute to the Chariot AI operations platform, and support customer-facing deployments.

Designs, develops, and troubleshoots secure, scalable ML software systems and services. Builds production code, architecture artifacts, APIs, and distributed microservices; analyzes data to improve applications and system architecture. Uses AI-assisted development tools responsibly, validates generated outputs, and guides peers on secure engineering practices. Requires Python and ML framework expertise, cloud-native technologies, public cloud experience, distributed systems knowledge, and Cassandra or equivalent NoSQL experience. Preferred experience includes GPU workloads, model serving, distributed inference, model compression, and edge deployment.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead a team of ML engineers building and operating scalable risk features, data pipelines, supervised and unsupervised models, LLM solutions, and explainability systems for financial crime monitoring. Responsibilities include productionizing ML applications, monitoring drift and bias, optimizing performance, developing Databricks pipelines, and collaborating with product, SRE, compliance, and operational teams.