Top Senior Machine Learning Jobs

Develop and deploy advanced AI and machine learning models for branch network optimization, resource allocation, sales effectiveness, and workforce performance. Work with geospatial, demographic, transactional, and behavioral data; collaborate with business, governance, UX, and technology stakeholders; translate model results into business recommendations; and support regulatory model reviews. Apply techniques including spatial and graph-based modeling, reinforcement learning, optimization, and behavioral modeling.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Develop and deploy reliable machine learning, LLM, agentic, and data-centric systems for government and enterprise customers. Build and evaluate AI agents, workflows, tool integrations, and retrieval-augmented systems; measure performance, reliability, and safety; and improve the Chariot AI operations platform. Collaborate with customers, data scientists, software engineers, and DevOps teams on mission-critical deployments. The role is fully remote or hybrid in Austin, with travel up to 20%.

Build and operate cloud-based AI and machine learning solutions for enterprise services. Develop efficient, modular software in Go or Python; collaborate with product, development, and quality teams; and own solutions through design, testing, deployment, and delivery. Deploy and scale services in Kubernetes using Helm and CI/CD, maintain observability, resolve production incidents, participate in on-call rotations, and perform root-cause analysis against service-level objectives.

Design, build, deploy, and operate production LLM-powered agents for JPMorganChase’s Commercial and Investment Bank. Responsibilities include Graph RAG, agent memory, context and authorization management, multi-agent workflows, MCP integrations, evaluations, guardrails, cloud deployment, observability, resiliency, and security. The role partners with business, product, engineering, and external stakeholders to deliver supported AI solutions.

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 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.