Top Senior Machine Learning Jobs

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.

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.

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.

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.

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.

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.

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.

Design, build, and operate production-grade agentic AI systems for ServiceNow. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, RAG and search pipelines, safety guardrails, observability, model integration, evaluation frameworks, and cost and latency optimization. The role also provides technical leadership through architecture decisions, code reviews, and coaching.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

Develop and optimize search relevance for an enterprise question-answering platform. Build ranking features and frameworks, enable parallel ranking experiments, and create metrics to evaluate ranking performance. Analyze query logs to identify search-quality improvements and apply information retrieval, machine learning, text processing, and natural language understanding techniques. Collaborate with product, UX, customer success, and engineering teams to deliver improved answer quality.

Develop machine learning and generative AI capabilities for conversational enterprise search, including multi-turn question answering, information retrieval, ranking, evaluation, and LLM-enabled features. Build scalable platform frameworks that help product teams prototype and deploy search applications. Establish metrics for search quality, security, privacy, and scalability while collaborating with product, design, infrastructure, data science, and engineering teams. Contribute to a high-performing and inclusive engineering culture.

Build and operate production small language models for real-time AI governance. Responsibilities include model training, fine-tuning, distillation, high-performance inference, synthetic-data generation, evaluation, telemetry, adversarial testing, failure diagnosis, and closed-loop model improvement. The role also provides technical leadership and partners with product, security, research, and customer-facing teams to deploy reliable, low-latency models at enterprise scale.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Design, build, and deploy core ML systems and components for an AI agent platform, including search ranking, proactive suggestions, orchestration, routing, and planning; integrate cutting-edge LLMs and ensure high-quality user experiences while working autonomously in a fast-paced, collaborative environment.