Top Machine Learning Engineer Jobs

Lead the design, development, and deployment of AI and machine learning solutions, mentor teams, and drive innovations in healthcare data analysis.

Scale and optimize a generative, agentic conversation engine for enterprise use: build product infrastructure and neutral APIs for chat clients, minimize latency and memory, enable private-domain knowledge and multilingual translation, implement logging/tracing/metrics, provide debugging tools, and collaborate with ML, product, and support teams.

Build and productionize agentic AI systems using LLMs. Contribute to agent orchestration, sandboxed execution, latency optimization, agent memory, and multimodal I/O. Collaborate with ML, security, product, and UX teams, mentor engineers, and drive roadmap and scalable infrastructure to deliver reliable enterprise AI features.

Lead design and implementation of agentic AI features for conversational and web interfaces. Collaborate with GAI framework experts, PM/UX/GTM teams, and cross-functional partners to ship scalable, extensible enterprise ML products and expand product reach globally. Tech-lead end-to-end delivery and advance the Agentic AI framework.

Design, scale, and optimize a production conversation engine for enterprise generative AI. Build APIs and infrastructure supporting multiple chat clients, low-latency multilingual dialogue, logging/tracing, monitoring, and tools for engineers. Collaborate with ML, product, and support teams to improve performance, robustness, and configurability for hundreds of millions of users.

Expand NLU and agentic AI capabilities by training, evaluating, and productionizing LLMs and compound AI systems. Improve conversational, multimodal, multilingual agents, ensure performance (latency, reliability), implement responsible AI, red-teaming, and privacy safeguards, and ship production-quality code integrated with platform infrastructure.

Build and own runtime infrastructure for agentic AI: orchestration engine for long-running sessions, distributed session management, event-driven pipelines, asyncio-based concurrency, observability with OpenTelemetry, caching with Redis/DynamoDB, and real-time streaming via gRPC and Socket.IO.

Build and improve enterprise conversational search and question-answering systems using IR and generative AI. Implement retrieval, matching, ranking, and summarization across heterogeneous knowledge sources, develop LLM-enabled features, and create evaluation metrics addressing enterprise constraints like security, privacy, and scalability.

Design and scale Moveworks' generative conversational platform: build product infrastructure, APIs, and optimizations for low-latency, memory-efficient dialog engines; implement logging/tracing/metrics; enable multilingual, domain-specific experiences; and collaborate with ML, product, and support teams to deliver robust enterprise AI at scale.

Design and implement agentic, LLM-driven conversational and web UI features; collaborate with product, UX, and GAI experts; lead end-to-end delivery, expand product reach, and improve the Agentic AI framework for enterprise workflows.

Design and implement agentic, conversational AI features for enterprise workflows. Lead end-to-end delivery, collaborate with PM/UX/GTM, expand product reach globally, and advance the Agentic AI framework to create scalable, high-quality web and conversational UI experiences.

Improve enterprise search relevance for a question-answering platform by crafting and optimizing ranking features, building algorithm frameworks, enabling parallel ranking experiments with tooling, analyzing query logs, and developing metrics to evaluate and boost answer quality across heterogeneous knowledge sources.

Design, scale, and optimize Moveworks' generative conversational engine and product infrastructure. Build scalable APIs for chat clients, implement low-latency dialog features using private enterprise knowledge, add logging/tracing and monitoring, create developer-facing tooling, and collaborate with ML, product, and support teams to drive performance and feature development.

Partner with quantitative researchers to design, build, optimize, and scale deep learning models and tooling for research and production. Work includes model architecture, distributed training, inference optimization, ML libraries, and high-performance computing to improve training speed, scalability, reliability, and cost-efficiency in systematic investing workflows.

The Staff Machine Learning Engineer will lead ML initiatives, partnering with multiple teams to enhance customer support through AI-driven solutions, focusing on conversational agents and research in chatbot architectures.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Design, build, and implement web applications and back-end services. Work with generative AI models and cloud components, ensuring adherence to coding standards and managing project timelines.

The role involves designing and implementing machine learning solutions for geospatial data, participating in multi-discipline teams, and mentoring internal teams while ensuring efficient project execution and client communication.

Lead design and delivery of enterprise AI/ML and intelligent automation platforms. Define architecture strategy, build backend APIs and event-driven pipelines, deploy cloud-native solutions, drive adoption of generative and agentic AI, mentor engineering teams, and ensure security, scalability, and governance across AI initiatives.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

Analyze data, design and implement machine learning models, build data pipelines, collaborate with teams, and keep up with advancements in AI.

Lead design and deployment of production-grade ML systems (ranking, retrieval, LLMs). Drive architecture decisions, run experiments and evaluations, build scalable feature pipelines and monitoring, mentor engineers, and collaborate cross-functionally to integrate AI into products and scale ML capabilities.

Build and productionize large-scale data processing, featurization, and inference pipelines for multimodal and vision models. Improve inference frameworks for reliability and cost-efficiency, drive scalability across compute, storage, and queries, and develop evaluation methods to support rapid experimentation and production deployment.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.