Top Machine Learning Jobs

Leads client-facing discovery and technical product development for AI initiatives. Designs, prototypes, deploys, and operates agentic systems, retrieval pipelines, enterprise integrations, and production-grade generative AI applications. Establishes evaluation, observability, safety, governance, and reliability frameworks while translating business needs into technical specifications. Requires strong Python and software engineering expertise, cloud and DevOps experience, retrieval architecture knowledge, and at least seven years of engineering experience, including production generative AI experience.

Develop and maintain data integration, ETL, ML, and monitoring pipelines supporting AI/ML and generative AI tools in UCSF Health’s research infrastructure. Productionize models, implement data integrations, design monitoring metrics for safety, bias, drift, performance, and validity, and collaborate with researchers, data scientists, and technical teams. The role requires advanced Python, SQL, MLOps, CI/CD, cloud architecture, and Epic Clarity/Caboodle data-model expertise.

Lead the architecture and development of production AI/ML systems, including LLM applications, RAG, tool use, multi-agent workflows, and real-time inference. Partner with product, design, data, and leadership to ship scalable capabilities. Implement AI security, safety, privacy, reliability, observability, and human-in-the-loop controls. Mentor engineers and help shape technical practices in a fast-moving fintech startup.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Build production agentic applications that automate complex geoscience workflows. Responsibilities include designing multi-agent workflows, integrating tools and APIs, managing persistent state, implementing human-in-the-loop controls, enabling retrieval and evidence grounding, fine-tuning LLMs, evaluating and tracing agent performance, and delivering tested web services with CI/CD. The role also requires sound product judgment, collaboration with domain experts, and responsible use of AI coding agents.

Serves as PDW’s senior AI/ML technical leader, defining enterprise AI strategy, architecture, standards, and roadmap while building production systems. Develops RAG solutions, AI agents, orchestration platforms, evaluation frameworks, APIs, data pipelines, and MLOps infrastructure. Leads AI security and governance, optimizes models across cloud and GPU environments, partners across engineering and business teams, and mentors teams adopting AI technologies. Requires extensive hands-on experience with generative AI, production ML systems, secure architecture, and enterprise deployment.

Own the machine learning platform supporting experiment tracking, distributed multi-node training, model evaluation, and production inference. Build reliable, reproducible, and cost-efficient systems that scale from small experiments to petabyte-scale multimodal training. Collaborate with research scientists, data and software engineers, and product teams to integrate models with downstream agents. Develop training and inference interfaces, support multimodal data, and optimize performance, observability, and infrastructure costs.

Principal Machine Learning Engineer setting technical strategy for ZoomInfo’s data graph, entity resolution, buying intent, and AI agent context. The role requires hands-on production machine learning, evaluation standards, data contracts, model cost optimization, and scalable Python and SQL systems. Responsibilities include leading design reviews, mentoring engineers, influencing hiring, and establishing technical defaults across teams. Preferred expertise includes ranking, retrieval, knowledge graphs, information extraction, PyTorch model serving, GPU planning, agent memory, and LLM security.

Leads the technical strategy and architecture for Cisco’s agentic AI platforms. Designs hyperscale multi-agent and LLM systems, hybrid and graph-based retrieval, AI safety and observability standards, and automated remediation capabilities. Influences architecture across product organizations, advises executives, mentors senior technical leaders, and represents Cisco at industry forums and standards bodies.

Architect and build a multi-cloud ML training platform for decentralized large-model training and inference across consumer devices and cloud instances. Responsibilities include infrastructure orchestration, distributed compute, GPU workloads, checkpointing, data streaming, network-condition simulation, node-churn management, observability, reliability, and resilient job execution. The role requires strong Python, infrastructure-as-code, Kubernetes, distributed systems, decentralized networking, and SRE experience.

Design, develop, and deploy production AI/ML systems involving foundation models, agentic workflows, RAG, multimodal learning, model evaluation, and edge inference. Build data pipelines, cloud architectures, APIs, and software integrations while applying AI assurance practices. Lead technical tasks, mentor junior staff, collaborate with customers and cross-functional teams, contribute to Agile delivery, and support advanced government and commercial R&D proposals.

Build secure, scalable full-stack HR applications integrating enterprise AI services, LLMs, RAG pipelines, web technologies, and SQL data stores. Develop APIs, front ends, data tiers, retrieval and ETL pipelines, and AI-enabled workflows. Ensure data quality, privacy, role-based access, auditability, and performance. Collaborate with product, data, and security stakeholders to identify use cases and deliver reliable production solutions.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Designs and operates evaluation systems for evolving machine learning models. Responsibilities include defining metrics, building data and automated judging pipelines, analyzing model failures, simulating users and environments, running experiments, and documenting findings. The role requires production-quality Python, PyTorch, reinforcement learning evaluation or simulation experience, and strong machine learning research or open-source contributions.

Build and optimize low-level software for large machine learning workloads across specialized, multi-vendor hardware. Tune model serving for speed, throughput, reliability, and cost; write GPU kernels; design and operate heterogeneous compute clusters; monitor production systems; and work directly with customers. The role requires deep computer architecture and operating systems knowledge, infrastructure or systems experience, strong communication, on-site availability in San Francisco, and a computer science degree. Exceptional results and GPU or ML serving experience are valued.

Lead the technical strategy, architecture, and delivery of large-scale machine learning systems for autonomous driving. Develop end-to-end AI methods, define dataset and auto-labeling requirements, mentor senior engineers, influence multiple teams, and integrate autonomy technology across Uber. The role requires expertise in Python, Linux, PyTorch, autonomous systems, and scalable ML infrastructure, with preferred experience in C++, CUDA, ROS, foundation models, and high-performance optimization.

Develop and evaluate machine learning and LLM-based systems, including neural network tuning, model development, benchmarking, experiment design, and performance validation. Use Python or R, deep learning frameworks, SQL, GPUs, and version control systems. Preferred work includes Hugging Face, cloud architecture, semantic search, Tableau, Explainable AI, ONNX, and web development. Requires U.S. citizenship and an active TS/SCI with Polygraph clearance.

Build and improve large-scale machine learning models for aviation prediction products, including runway landing times, gate arrivals, and taxi durations. Develop high-performance systems using historical aviation data and complex data types, contribute ideas in an agile environment, and improve operational efficiency for airlines and airports. Preferred experience includes Kubernetes, AWS, GPU acceleration, graph neural networks, geospatial data, observability, ETL orchestration, and large-scale data processing.

Build and operate production-scale infrastructure for multimodal and video AI systems. Responsibilities include data and ingest pipelines, training and evaluation infrastructure, model inference and serving, vector retrieval, agentic orchestration, monitoring, security, and privacy. The role partners with research scientists, sets technical direction, mentors engineers, and scales reliable AI capabilities across cloud environments.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

The Machine Learning Performance Engineer will build training and inference pipelines, enhance deep learning frameworks, improve performance, and collaborate with researchers and engineers to optimize trading systems.

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 and deploy physical AI systems on real robots, including learned perception, vision-language-action models, imitation and reinforcement learning, world models, autonomous behavior, and sensor-data pipelines. Prototype approaches in simulation and field environments, optimize models for latency and edge deployment, and collaborate across robotics, controls, embedded, mechanical, and product engineering teams. The role also contributes to long-term Physical AI architecture and research direction.

Build and optimize end-to-end machine learning training infrastructure for large-scale multimodal robot data. Responsibilities include distributed training, data ingestion and transformation, checkpointing, orchestration, experiment tracking, sampling and curation, performance profiling, debugging data and training issues, and converting research prototypes into production infrastructure. The role may also involve robot learning, cloud and cluster infrastructure, post-training, on-robot inference optimization, CUDA or Triton kernels, and video-heavy datasets.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.