Top Machine Learning Engineer Jobs

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.

Design, implement, and scale production-ready ML systems (computer vision and LLMs) for blueprint understanding, 3D model generation, document extraction, and materials forecasting. Build ML pipelines, deploy and monitor models, integrate retrieval/RAG and rules-based methods, and collaborate with product, engineering, and domain experts to deliver explainable, business-impacting features.

Lead design, development, and deployment of production-scale ML and AI systems. Own end-to-end ML lifecycle, architect scalable pipelines and infrastructure, mentor engineers, integrate emerging AI (LLMs, RAG, agents), optimize models for edge inference, collaborate with cross-functional teams, and ensure monitoring, reliability, and ethical considerations for law enforcement and homeland security solutions.

Design, build, and operate large-scale offline data infrastructure and pipelines for ML training and experimentation. Improve reproducibility, observability, performance, and cost-efficiency across batch and streaming workloads while integrating orchestration systems and partnering with ML engineers.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Work as a hands-on junior ML engineer building generative AI apps, traditional ML models, and MLOps solutions. Implement and maintain model training, inference pipelines, and LLM systems while being mentored by senior engineers and delivering client projects.

Build and productionize scalable ML infrastructure for training, evaluating, and serving large language models. Develop distributed training and inference pipelines, optimize LLM latency, create automation abstractions, and collaborate cross-functionally to scale ML systems and best practices.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

Develop datasets, models, and evaluation systems for content safety, product security, robustness, and ML fairness for multimodal LLMs. Research and implement bias detection/mitigation, define safety metrics, contribute MLOps automation and safety tooling, and collaborate with engineers, researchers, and data scientists to improve scalable model safety and inclusivity.

The Machine Learning Engineer will develop and enhance ML-powered products focusing on models, metrics, pipelines, and collaboration with product teams.

Lead ML Engineer responsible for designing, building, deploying, and maintaining production ML systems and data pipelines at scale. Collaborate with product and data science teams, ensure model governance, monitoring, CI/CD, and leverage cloud-based architectures to deliver performant, explainable ML solutions.

Design, evaluate, and productionize agentic AI systems and LLM-driven components to improve agent behavior, reliability, latency, and user-facing experiences. Build eval benchmarks, tune models, manage data quality, and collaborate with platform teams to deploy scalable, safe, and high-performing AI features.

Design, prototype, and productionize large language models and architectures for heterogeneous financial data. Implement pre-training objectives, optimize training/serving at scale, mentor engineers, and collaborate across teams to deliver reliable, secure, and interpretable ML systems.

Design, fine-tune, and deploy LLMs and RAG pipelines in private, secure environments. Build multi-agent systems, private inference infrastructure, evaluation frameworks, and observability tooling. Collaborate with platform teams to ensure secure, scalable production AI for enterprise customers.

Develop, train, evaluate, and deploy spatial generative AI models for home renovation and design. Define inference framing, prepare image-based datasets with labeling partners, iterate with user feedback, and deploy performant models into production architecture.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

As an ML Engineer, develop and deploy advanced ML solutions for OCR, document processing, and voice technologies, collaborating with teams to optimize models for performance.

The Machine Learning Engineer will develop and implement ML algorithms for improving communication system performance, collaborating on various use cases and data analysis.

Architect, build, and operate ML inference and deployment infrastructure (model serving, orchestration, CI/CD, feature stores). Improve developer experience, optimize GPU/CPU scheduling and performance, and establish ML deployment best practices across Riot’s production ML ecosystem while shaping long-term platform direction.

Join PatternAI's data team as a Machine Learning Engineer, building ML pipelines and managing MLOps infrastructure for model optimization.

Seeking a Perception ML Engineer to develop and deploy advanced perception models for robotics, focusing on manipulation and navigation in dynamic environments.

The Machine Learning Engineer role involves optimizing models for performance, utilizing CUDA, GPU scheduling, and collaborating on ML systems design.

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

You'll lead the development of AI-driven systems for a dating platform, focusing on personalized matching algorithms and voice interactions. Responsibilities include designing ML pipelines, deploying models, and conducting experiments to improve user experience.