Top Hybrid Machine Learning Engineer Jobs

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.

Research Engineer responsible for developing and productionizing post-training methods for diffusion and flow models, including reward modeling, preference optimization, supervised fine-tuning, distillation, and reinforcement learning. The role owns research planning, evaluation standards, training pipelines, and the feedback loop between model behavior and product data. Models will be deployed to professional designers, requiring rigorous engineering, reproducible evaluations, and close collaboration with product and founders.

Partner with business stakeholders and IT delivery teams to design, implement, and deploy AI-enabled production applications, integrations, and data pipelines. Focus on solution feasibility, deployment, operationalization, and driving adoption. Support benchmarking and internal development across divisions (travel up to 50%).

Develop and optimize AI models and training systems for robotics applications, focusing on design, implementation, and performance optimization of ML models.

Design, build, validate, and deploy statistical and ML models on claims and healthcare data; perform EDA and feature engineering; manage projects with minimal oversight; debug pipelines; collaborate with engineers; document and present results; mentor junior data scientists.

Design, optimize, and scale low-latency, high-throughput inference serving systems for large multimodal models. Implement quantization, batching, speculative decoding, KV cache management, multi-GPU/multi-node parallelism, autoscaling, and load balancing while optimizing GPU utilization, reliability, and observability for production deployments and research evaluation.

Build and maintain production-quality Python code for portfolio construction, optimization, and statistical analysis. Automate investment workflows, validate models, and create interactive visualizations to support investment decision-making and reporting.

Design and build full-stack features for an LLM-powered coaching platform: architect backend services and APIs, integrate models, craft responsive front-end experiences, ensure enterprise-scale performance and security, drive CI/CD and observability, debug across the stack, and mentor engineers while collaborating with product and design.

Build the analytical foundation for Trust & Safety: measure abuse, build datasets and dbt models, develop risk and anomaly-detection models, design offline evaluations and experiments, define thresholds and decision frameworks, detect coordinated abuse, embed predictive risk signals into workflows, monitor model drift, and communicate findings to cross-functional partners.