Top Machine Learning Engineer Jobs

Design and implement scalable ML applications, collaborate with data scientists and engineers, perform data preprocessing, feature engineering and model evaluation, and deploy models using GCP/Vertex AI with BigQuery and NoSQL databases like MongoDB.

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.

Build and deploy ML models and pipelines for entity resolution and a knowledge graph at scale. Fine-tune algorithms, expand data ingestion and enrichment, create evaluation harnesses, and integrate agentic workflows to support research and product teams processing tens of millions of records daily.

Prototype, develop, and deliver AI/ML solutions; architect and integrate models into products; perform ETL, data curation, and cloud integration (including AWS); work with graph and relational databases and diverse data formats; monitor and tune model performance; collaborate with stakeholders and document processes.

Design and implement ML solutions for wireless communications using SDR-captured RF data. Combine signal processing, statistics, and ML to build prototypes and scalable systems from edge devices to cloud, collaborating with UX and infrastructure teams.

Build and deploy production-grade AI/ML inference systems for a cloud security platform. Optimize LLM serving (vLLM, SGLang, KV cache), implement model evaluation/reporting, and collaborate with ML scientists and engineers to execute the AI roadmap.

Lead a small team to research, develop, optimize, and deploy computer vision AI/ML algorithms for remote sensing (EO/SAR) and ATR. Analyze large datasets, research multimodal fusion, train and deploy deep learning models, review literature, produce technical documentation, and mentor junior engineers while interfacing with program leadership and customers.

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.

Design, train, and maintain machine learning models for aerial target detection and classification. Oversee dataset pipeline and deployment of models, ensuring performance in real-world operations.

Develop and maintain ML pipelines for entity classification; collaborate with data scientists; implement monitoring frameworks and improve data workflows.

Define and lead modeling architecture for personalization, matchmaking, recommendations, and social discovery. Build large-scale real-time inference systems, develop models for skill, behavior, trust & safety, and multi-objective optimization. Drive Responsible AI, experimentation, observability, and cross-functional standards while mentoring senior engineers and influencing ML strategy.

Lead the development of ML models and prototypes for Edge AI/ML, integrate with mixed-signal systems, and optimize deployment. Collaborate with cross-functional teams and mentor engineers while exploring new ML trends and applications.

The Senior AI/ML Engineer will design, develop, and deploy scalable ML systems focusing on LLM applications, collaborating with various stakeholders to enhance healthcare data insights.

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.