Top Remote Machine Learning Engineer Jobs

Lead the development of healthcare data products using machine learning. Design models, collaborate with teams, and mentor others in statistical methods.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Lead technical direction for applied ML and AI Context systems across HubSpot. Build and productionize models, architecture, retrieval, feature pipelines, evaluation, experimentation, and monitoring. Mentor engineers, collaborate cross-functionally, and address privacy, bias, reliability, and data governance for customer-facing AI features.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Drive technical direction and architecture for AI Context systems across HubSpot's CRM: develop, evaluate, productionize, and monitor ML/LLM models; build scalable data processing, retrieval, feature generation, and serving systems; lead cross-team initiatives, mentor engineers, and measure customer and business impact while addressing privacy, bias, security, reliability, cost, and data governance.

Design, build, and operate cloud-native data and ML infrastructure for real-time and batch workloads. Implement reproducible training, evaluation, and inference pipelines; integrate event streams; monitor data quality, model performance, latency, and cost; automate deployments with IaC and CI/CD; collaborate with product, data science, and security teams; produce docs, dashboards, and runbooks.

Build, deploy, and optimize production AI/ML and LLM-driven decisioning systems for claim offer generation and negotiation. Own end-to-end model development, evaluation, experimentation, and operational reliability while balancing fairness, business objectives, and product requirements.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Analyze malware and detection data to improve machine learning models, manage false positives, and respond to internal inquiries regarding threat detections.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.

Lead design, deployment, and operation of production ML systems and inference pipelines. Build and scale APIs and services on Kubernetes, optimize pipelines, monitor model performance and drift, and partner with cross-functional teams to deliver ML insights to customers.

Own end-to-end ML projects focused on recommendation and LTV signals: develop, evaluate, and deploy models; build training/scoring pipelines; run A/B tests; monitor production models; collaborate with product and analytics; mentor colleagues.

The role involves designing and building AI components for a platform, specifically focusing on NLP and ML for catalog mapping and anomaly detection, along with feedback mechanisms.

Design, build, and deploy Azure-based AI/ML pipelines for data migration reconciliation: anomaly detection, exception classification, LLM evaluation, automated data-quality scoring, production model monitoring, and dashboards. Mentor team members, produce documentation, and transfer knowledge to embedded staff in regulated environments.

Lead design, build, and operation of end-to-end production ML systems for ranking, rewards, ROAS/LTV prediction, personalization, and offer optimization. Own feature pipelines, training, inference, experimentation, monitoring, retraining, and MLOps. Partner with Data Engineering, BI, Product, and Engineering to connect ML to business outcomes, mentor engineers, and drive an AI-first engineering culture.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Design, develop, and architect scalable, fault-tolerant cloud AI/ML services and pipelines. Contribute to R&D, integrate with emerging frameworks, operate and troubleshoot production systems, build automated data preparation/annotation workflows with secure storage, and fine-tune, train, validate, test, and deploy models. Implement vision-based AI including automated labeling, object detection, and image segmentation while collaborating with a remote global team.

Lead design and management of search and content recommendation services and models. Build personalized recommender systems using ML/AI, define success metrics with product and business teams, drive core module development, and translate PB-scale data insights into measurable product impact.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.