Top Machine Learning Jobs

Design, build, deploy, scale, and monitor production machine learning systems. Develop models, application code, data pipelines, cloud architectures, automated tests, and CI/CD processes. Collaborate with Product and Data Science teams, ensure high availability and performance, govern models for risk and responsible AI, and lead teams developing ML solutions.

Lead the design, development, deployment, and support of scalable AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques to improve AI system scalability, latency, throughput, hardware utilization, and cost. Shape foundational AI roadmaps, collaborate with cross-functional teams, and mentor engineers while applying emerging research to production.

Develop and integrate AI-enabled features (LLMs, agents, ML) into applications, write and test production-quality code, troubleshoot performance, and collaborate with product and engineering teams. Support monitoring, evaluation, and continuous improvement of AI capabilities; may work nights/weekends as needed.

Build and ship production machine learning and computer vision systems processing 11 million daily images. Own the full lifecycle from sensor data collection, dataset curation, training, and deployment through monitoring and iteration. Develop detection, segmentation, classification, embedding, and re-identification models for cloud and roadside edge hardware, balancing accuracy, speed, and power efficiency. Apply both classical computer vision and state-of-the-art methods while writing production-quality software.

Develop wafer, die, and package-level models across semiconductor technology nodes. Collaborate globally to deploy computational, generative AI, agentic AI, multiphysics, and multiscale modeling solutions using sparse and high-dimensional datasets. Interpret predictive modeling insights, identify engineering findings, and communicate results to technical teams and senior leaders.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Develops search relevance solutions for an enterprise question-answering platform. Responsibilities include creating and optimizing ranking features, building frameworks for diversified ranking, enabling parallel ranking experiments, and developing metrics to evaluate search performance. The role applies information retrieval, machine learning, text processing, and natural language understanding to address lexical gaps, semantic understanding, and data sparsity while collaborating with product, UX, customer success, and engineering teams.

Develop and implement generative AI solutions for client engagements, including contact center applications, machine learning and NLP integrations, prompt engineering, LLM evaluation, and LLMOps governance. Use Python, Azure AI Services, Google Vertex AI, LangChain, and LLM APIs to build, deploy, monitor, and refine AI applications while supporting data analytics and client outcomes.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Design, train, test, optimize, and deploy machine-learning and deep-learning models using time-series and biosensor data. Develop robust inference systems for constrained edge and cloud environments, monitor algorithm performance and data quality, support regulated health-feature documentation, and conduct applied research. Collaborate with data science, firmware, and engineering teams to translate research prototypes into scalable production features for wearable technology.

Develops optimization, simulation, predictive modeling, and machine learning solutions for outbound fulfillment, transportation, routing, capacity, and supply chain operations. Translates ambiguous business problems into mathematical models, validates solutions with large datasets, conducts experiments and scenario analyses, and partners with engineering, product, and operations teams to productionize models and improve network efficiency, cost, speed, and service levels.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Design, research, and benchmark machine learning algorithms for anomaly detection, error outlier analysis, and faulty deployment analysis. Build, deploy, scale, and monitor user-facing ML features, models, services, and infrastructure in production. Analyze high-volume streaming data, communicate complex algorithms to nontechnical audiences, participate in research journal discussions, maintain team-owned systems, and join the on-call rotation while partnering with engineering and product teams.

Leads full-stack software engineering and infrastructure platform development for AI-driven, self-healing data center operations. Designs Java, Python, and Node.js applications, scalable APIs, CI/CD automation, cloud-native platforms, VMware integrations, and infrastructure-as-code workflows. Develops machine learning models for forecasting, anomaly detection, and test classification, integrating them into operational systems for predictive alerts and workload orchestration. Provides technical leadership, mentors engineers, reviews code, and drives firm-wide architecture and technology decisions.

Leads and develops a team of autonomy engineers building multi-agent resource allocation, planning, coordination, and edge AI systems for spacecraft. Provides technical oversight on optimization, reinforcement learning, validation, architecture, and constrained real-time deployment. Owns team roadmaps, delivery, cross-functional dependencies, process improvement, and stakeholder communication while managing hiring, performance, career development, and team culture. The role bridges autonomy expertise with spacecraft constraints including compute, latency, power, and communication limitations.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, and quality engineers to deliver scalable software. Translate product requirements into owned solutions spanning design, implementation, testing, and delivery. Apply efficient algorithms, object-oriented design, core AI/ML techniques, unit testing, profiling, and code tuning to create high-quality products and user experiences.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

Leads the design, development, deployment, and maintenance of production machine learning systems at scale. Responsibilities include developing and validating models, building optimized data pipelines, writing application code, automating testing and deployment, monitoring and retraining production models, designing cloud-based ML architectures, and applying responsible AI and governance practices. The role collaborates with Product, Data Science, and Agile engineering teams and may include people leadership.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

The role involves developing LLM applications for automated silicon design, optimizing models, curating datasets, and collaborating with other experts to integrate LLMs into EDA workflows.

The analyst will leverage multi-INT data for curation and development of labeled data for Machine Learning, focusing on SAR imagery. Responsibilities include recommending curation strategies and addressing data gaps for diverse datasets.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.