Top Hybrid Machine Learning Engineer Jobs

Develop machine learning models to predict printability of complex CAD geometries using image, thermal, simulation, and sensor data. Design and maintain structured and unstructured data systems supporting diverse data ingestion, storage, and retrieval. Deploy models into production in collaboration with preprint, process, and applications teams. Experience with geometric data, production software engineering, and Python or C++ is required; powder bed fusion and manufacturing data workflows are preferred.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build data systems and pipelines that support reliable AI workflows in enterprise environments. Responsibilities include collecting, cleaning, labeling, transforming, and evaluating data; creating quality and drift frameworks; developing retrieval, synthetic data, annotation, and feedback-loop tools; analyzing customer datasets; and collaborating with researchers, engineers, and stakeholders to improve production AI performance.

Design, develop, and deploy enterprise-scale AI systems powering intelligent agents, copilots, and autonomous workflows. Build production-grade LLM, RAG, vector database, and agent orchestration solutions; develop APIs, SDKs, and platform services; and optimize reliability, latency, security, cost, and scalability. Lead initiatives through production, implement testing, CI/CD, monitoring, and governance, troubleshoot distributed systems, collaborate cross-functionally, contribute to architecture and documentation, and mentor or guide team members.

Build and ship production machine learning systems for content abuse detection, including data pipelines, classifiers, LLM evaluations, inference infrastructure, monitoring, and model evaluation. Optimize precision, recall, latency, cost, and throughput while partnering with engineering and data teams. Develop moderator intelligence features, build training and serving platforms, establish metrics infrastructure, and mentor teammates. The role requires end-to-end ownership in a startup environment, from messy data and feature engineering through deployment and production monitoring.

Lead development of real-time machine learning and computer vision algorithms for ultrasound imaging in Intuitive’s robotic-assisted biopsy system. Responsibilities include detection, segmentation, classification, temporal tracking, artifact mitigation, image fusion, model optimization, and deployment into high-performance C++ medical-device software. The role also supports system integration, testing, formal design documentation, and patent applications while collaborating with clinical and engineering teams under Good Machine Learning Practice principles.

Build and maintain full-stack systems using TypeScript, React, and Express; manage infrastructure with Terraform, AWS, Docker, and ECS/Kubernetes; integrate and productionize AI systems (LLMs and agents), including fine-tuning, prompting, and building evals/benchmarks to validate generated code.

Design and own agentic systems that produce client-ready financial deliverables end-to-end. Build retrieval, evaluation, and verification systems; transform proprietary data into evidence-backed insight; and advance orchestration, function-calling, and reliability for LLM-powered pipelines.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

Design and productionize algorithms and regression models for real-time bidding and conversion prediction; apply privacy-preserving clustering to conversational data; analyze data to improve ad matching; conduct research, form hypotheses, and communicate findings to technical and non-technical stakeholders.