Top Machine Learning Jobs

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

Build and productionize applied AI and machine learning capabilities for Crucible, including LLM workflows, retrieval, tool use, orchestration, evaluation, and model-serving systems. Integrate optimization and forecasting models into reliable products, select models for constrained cloud, air-gapped, and edge environments, and establish technical standards for production AI. Collaborate with manufacturing, supply chain, data science, software engineering, and product teams to deliver measurable operational value.

Lead design, training, and evaluation of multi-modal ML perception models (camera, lidar, radar) for fallback autonomy. Define metrics, curate datasets, implement training and optimized inference pipelines for on‑vehicle constraints, and integrate models into production. Partner with safety, systems, and product teams for validation, verification, and deployment. Mentor engineers and promote ML and software engineering best practices.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions, including LLM applications, multi-agent systems, agentic workflows, and enterprise automation. Own the AI solution lifecycle in cloud environments, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders to deliver enterprise-scale solutions.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM applications, multi-agent systems, agentic workflows, RAG solutions, and automation using cloud AI platforms. Own the full AI solution lifecycle, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM orchestration, RAG applications, agentic workflows, multi-agent systems, and automation using cloud AI platforms. Own the full AI solution lifecycle, establish architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Build and operationalize LLM applications for financial services. Responsibilities include developing production AI features, Python libraries, data pipelines, prompts, tool-calling workflows, retrieval systems, evaluation suites, monitoring, and responsible-AI safeguards. Diagnose hallucination, latency, and cost issues; define metrics; analyze failures; collaborate with product, engineering, and business teams; and document designs and findings.

Develop and deploy machine learning planning algorithms for autonomous trucks. Own the full ML lifecycle, including data curation, feature engineering, training, evaluation, deployment, monitoring, and improvement. Design trajectory and behavior-planning models, safety guardrails, validation pipelines, and performance metrics. Investigate failures and edge cases, optimize inference, and collaborate with perception, prediction, mapping, runtime, and systems teams to deliver reliable production systems.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Develop and deploy production machine learning models and sensor-fusion algorithms for autonomous-truck ego-motion estimation and localization. Build scalable PyTorch training workflows, analyze large vehicle datasets, and create robust C++ and Python production software. Define validation strategies, optimize performance under real-time constraints, make architecture decisions, and collaborate across autonomous-driving teams. Provide technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Design, develop, and deploy reinforcement learning, behavior cloning, and imitation learning models for autonomous truck behavior planning. Own reinforcement learning pipelines from environment construction through training and modeling, contribute to distributed training infrastructure, analyze model performance and failure modes, and collaborate with simulation and planning teams to deploy models across driving scenarios.

Design and optimize production machine-learning runtime pipelines for autonomous vehicle motion planning. Build high-performance C++ inference, feature extraction, trajectory generation, and orchestration components for embedded platforms. Develop validation, safety guardrails, fallback mechanisms, testing, monitoring, debugging, and observability tools. Investigate complex runtime failures and optimize latency, throughput, memory, and multithreaded performance while collaborating with machine-learning and systems engineering teams.

The Postdoctoral Researcher at Zhou Lab will develop machine learning models for biological data, specifically in genomics and gene regulation, collaborate with experimental biologists, and lead research projects for significant scientific discoveries.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Own and improve Tessellate’s physician-to-organization matching engine by evolving rules-based matching toward semantic matching, embeddings, and vector search. Establish precision, recall, and evaluation practices; resolve healthcare organization identities across millions of records; and maintain a reliable, lightweight data platform. The role requires strong Python and SQL, applied machine learning expertise, production recommendation or ranking experience, and end-to-end ownership in an early-stage startup.

Design, develop, evaluate, and refine machine learning models using Python and leading ML frameworks. Analyze datasets with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and provide data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize models in cloud or enterprise environments.

Design and deploy production-grade machine learning models and data pipelines across image, video, text, geospatial, time-series, and structured data. Collaborate with customers, data scientists, software and DevOps engineers to define requirements, maintain mission-critical deployments, and contribute to the Chariot AI operations platform.

Architect and develop scalable AI-driven IVR, conversational, NLP, and LLM solutions for high-volume customer interactions. Build classification and clustering models, perform SQL-based analysis, design RAG and agentic workflows, and establish LLMOps practices for monitoring and lifecycle management. Partner with platform, speech engineering, and product teams while mentoring junior and offshore engineers.

Architect, train, and optimize multimodal deep learning models for autonomous vehicle perception. Build large-scale data mining, active learning, hard-example curation, and auto-labeling pipelines. Develop high-resolution vision and spatial-temporal transformer architectures, use foundation models for data curation and synthetic edge cases, and optimize models for onboard accelerator inference.

Develop, evaluate, and release learned driving policies for autonomous vehicles, particularly complex navigation and yielding scenarios. Bridge multimodal perception and planning outputs into safe behavioral actions, evaluate models in closed-loop simulation against safety metrics, and transition production-ready models into autonomous navigation systems. The role also advances data-driven policies, foundation-model experimentation, scenario generation, and end-to-end driving architectures.