Top Hybrid Machine Learning Engineer Jobs

Work with enterprise customers to identify high-value tabular ML use cases, design production-grade architectures, prototype solutions, lead deployments, coordinate cross-functional teams, and codify reusable deployment patterns to scale adoption of the NEXUS tabular foundation model.

Lead development and deployment of hybrid ML and mechanistic modelling pipelines (PK/PD, QSP) to generate actionable insights across drug discovery and development. Drive reproducible workflows, integrate diverse datasets, advance AI-augmented modelling, and communicate results to influence translational decision-making.

Responsible for building ML applications focused on risk and fraud, tackling data challenges, innovating in feature engineering, and establishing ML infrastructure.

Design, train, evaluate, and deploy state-of-the-art perception models (cloud and edge). Build ML pipelines, contribute to MLOps, collaborate cross-functionally, and deliver production-ready AI solutions for transit and government customers.

Research and implement state-of-the-art vector indexing and retrieval algorithms. Build and optimize high-performance index components (indexing, query paths, quantization, compression, memory layout) for CPU/GPU, design benchmarks, debug performance issues (SIMD, concurrency, I/O), and productionize prototypes for Milvus, Zilliz Cloud, and Vector Lakebase.

Develop ML features, models, and analytical methods from device, network, browser, mobile, session, and behavioral telemetry to detect fraud and improve identity confidence. Handle large-scale, noisy, high-cardinality data, design validations, assess drift and leakage, collaborate with engineering/product/risk for production rollout, document models and features, and mentor junior team members.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

Partner with Product and engineering teams to analyze and optimize the application funnel, develop and deploy AI/ML solutions, automate reporting/monitoring with SQL and Python, assess third-party vendors, and support test-and-learn plans to improve business KPIs.

Design and implement IR transformations, graph optimizations, kernel lowering, and code generation for novel hardware. Decompose LLM/transformer workloads into primitives, build performance models and profiling tools, collaborate with ML and hardware teams, prototype end-to-end improvements from framework passes to custom kernels, and shape system architecture for an inference engine.

As an intern, you'll develop software and machine learning systems for robots, focusing on perception, intelligence, behavior, and real-time software integration.