Top Senior Machine Learning Jobs

Leads the design, deployment, and optimization of production AI and machine learning systems, including LLMs and generative AI. Architects scalable solutions, drives MLOps, manages model evaluation and monitoring, and improves system reliability and cost efficiency. Collaborates with product, data, and platform teams while providing technical leadership through design reviews, mentorship, and AI strategy.

Provides technical leadership for enterprise AI and machine learning engineering, including data integration, governance, architecture, model development, deployment, monitoring, and innovation. Partners with data scientists, engineers, business teams, and enterprise data warehouse teams to build cloud-based big data solutions using Google Cloud and AWS. The role supports advanced analytics, communicates roadmaps and business value, and develops scalable tools and methodologies for data science initiatives.

Build and operate large-scale machine learning systems for ads personalization, including ad ranking, conversion-rate prediction, recommendation, behavioral modeling, and bidding optimization. Monitor model quality, calibrate predictions, address data drift and delayed feedback, and guide technical direction and project execution. The role requires collaboration with engineering leads and communication of complex technical concepts to non-engineering stakeholders.

Leads the development and global scaling of Fuelight, an AI-enabled resource allocation and investment optimization product. Responsibilities include building production machine learning applications, MLOps capabilities, scalable architectures, decision-support experiences, explainable AI features, and integrations with data and optimization products. Partners with engineering, data science, product, and business stakeholders while establishing engineering standards and ensuring cybersecurity, privacy, responsible AI, and governance compliance.

Leads the design, development, deployment, and measurement of large-scale machine learning, information retrieval, LLM, and agentic AI solutions. Defines scientific strategy, technical roadmaps, experimentation frameworks, and evaluation methods for web content quality, trust, safety, and understanding. Partners across engineering, product, and research teams, mentors scientists, influences organizational direction, and develops systems addressing spam, abuse, fraud, and low-quality AI-generated content.

Architect and build scalable engineering systems while developing and optimizing machine learning models for Uber’s delivery marketplace. Lead organization-wide frameworks and engineering projects, solve low-latency and high-correctness problems, oversee incident management, and collaborate with Product, Science, and Operations. Mentor engineers, drive technical roadmaps, improve developer velocity, and support reliable systems at global scale.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

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.

The Applied Scientist will develop and deploy machine learning products for content understanding, creator sponsorship, advertising, and brand safety. Responsibilities include selecting and evaluating ML approaches, working with behavioral, metadata, text, and video data, prototyping models, deploying them through data pipelines and ML services, and collaborating with scientists, engineers, product teams, and business stakeholders. The role also contributes to innovation in LLMs, computer vision, inference, and model evaluation.

Leads the design, development, validation, deployment, monitoring, and tuning of machine learning pipelines and models for IoT product performance, installation quality, outage detection, and fleet health. Builds predictive, classification, time-series, and anomaly-detection solutions using AWS, Elasticsearch, Redshift, and large-scale sensor data. Defines project scope and validation standards, collaborates cross-functionally, reports findings, supports production integrations, and mentors engineers.

Designs and evaluates machine learning solutions for government mission data. Responsibilities include assessing dataset quality and readiness, developing models and reproducible workflows, defining evaluation metrics, conducting error analysis, addressing bias and drift, and using AWS SageMaker for experimentation, pipelines, training, model registration, and deployment. Provides architecture and implementation recommendations while supporting batch or real-time processing decisions in secure operational environments.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.

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.

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.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Design, deploy, and scale production AI/ML solutions for healthcare, including predictive models, generative AI, statistical analysis, microservices, APIs, and MLOps pipelines. Analyze complex clinical datasets, implement model monitoring and governance, evaluate emerging AI technologies, and collaborate with engineering, data, product, and clinical teams to improve care delivery and operational efficiency.

Build and operate production machine learning systems enabling autonomous medical coding at scale. Responsibilities include data discovery, feature engineering, model training and evaluation, deployment, monitoring, MLOps pipelines, LLM integration, observability, governance, fairness assessment, and A/B testing. The role also involves designing highly available low-latency architectures, defining technical roadmaps, and collaborating with product, design, data, and platform teams.

Lead the technical strategy, architecture, modernization, and company-wide adoption of Pinterest’s AI/ML evaluation platform. Design reliable evaluation systems for GenAI and machine learning workloads using automated metrics, human feedback, expert judgment, safety assessments, and business outcomes. Drive cross-team workstreams, resolve technical tradeoffs, establish evaluation standards, and improve the quality, reliability, and trustworthiness of AI-powered products.

Own the architecture, deployment, performance, and reliability of Stickerbox’s production machine learning pipeline. Optimize latency, cost, batching, caching, GPU utilization, model serving, and inference across image generation, speech recognition, and character models. Lead deployment strategies, capacity planning, observability, safety enforcement, automated quality checks, migrations, release engineering, and on-call operations for a global consumer hardware launch.

Lead architecture and development of advanced perception systems for Level 4 autonomous vehicles. Build multimodal foundation models, synthetic data and sensor simulation capabilities, knowledge-distillation pipelines, and efficient real-time onboard models. Deploy solutions to autonomous vehicle fleets while mentoring engineers and providing technical leadership. The role focuses on object detection, segmentation, tracking, classification, sensor fusion, closed-loop evaluation, and robust generalization across vehicle generations.

Owns the end-to-end improvement of machine learning models after initial training, including compute setup, dataset preparation, simulated environment design, and automated self-improving workflows. The role combines applied AI research with software engineering, requiring expertise in reinforcement learning, evaluations, interpretability, PyTorch, ML pipeline debugging, and curated dataset production.

Develop and deploy machine-learning and autonomous-driving algorithms for parking assistance and low-speed autonomy. Responsibilities include perception, localization, sensor fusion, planning, state estimation, threat assessment, deep-learning model development, scalable training pipelines, data-centric optimization, and real-world validation. The role collaborates across software, systems, validation, and vehicle integration teams while supporting safety-critical production deployments through a rigorous V-cycle process.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Design, develop, test, debug, and maintain scalable software applications and prototypes using Java and Python. Work in a Linux full-stack environment, integrate customer-tailored large language models, design accessible interfaces, connect external requirements tools, troubleshoot performance issues, conduct code reviews, support planning, and mentor junior engineers. The role requires extensive software engineering experience, a bachelor’s degree or equivalent experience, DoD8570 IAT2 compliance, and TS/SCI clearance with polygraph.

Lead large-scale machine learning initiatives, develop and deploy production ML models, optimize data pipelines and distributed systems, and build cloud-based ML platforms. The role uses Python, Java, Go, Scala, and modern ML frameworks and requires technical leadership across architecture, modeling, infrastructure, and engineering talent development.