Top Hybrid Machine Learning Engineer Jobs

Design, build, and deploy on‑prem, air‑gapped AI/agent systems: integrate LLMs, MCP interfaces, RAG retrieval, and orchestration; optimize local CPU/GPU inference, harden Linux environments, run experiments, and collaborate across engineering and mission teams to move prototypes into operational capabilities.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

Drive post-training experiments to improve agent performance for legal tasks: optimize harnesses, design grading/reward systems, study agent behavior, and collaborate with internal and external researchers to convert findings into training data, evals, and model improvements.

Lead research, design, and deliver Pearson's scalable learner model and AI-driven features. Partner with product, engineering, and design to define data requirements, build prototypes, maintain data pipelines, ensure data quality, and translate research into production ML capabilities and business growth.

Design, build, and optimize software applications and systems; implement software architecture and design patterns; improve performance, scalability, and reliability; collaborate with cross-functional teams; conduct code reviews and ensure adherence to best practices.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Design and optimize scalable geospatial data pipelines to convert high-frequency vehicle sensor telemetry into high-fidelity map features. Build map-matching, trajectory smoothing, and sensor-fusion algorithms, apply statistical tests for change detection and map validation, and lead system architecture for low-latency spatial indexing and storage while collaborating with embedded, perception, and cloud teams.

The AI Engineer will design, develop, and deploy machine learning models, work on APIs for AI applications, and manage ML pipelines on AWS. Responsibilities include productionizing AI systems and developing scalable solutions in a cloud-native environment.

Research, prototype, and implement methods to improve efficiency and deployability of LLMs, VLMs, multimodal models and agents. Focus on compression, quantization, inference and reasoning optimizations, novel architectures (MoE, SSMs, Looped Transformers), gradient-free methods, and inference-time optimizations. Build evaluation pipelines, run benchmarks, and contribute to publications, open-source, and IP.