Top Hybrid Machine Learning Engineer Jobs

Work on AI-powered Notion features: prototype and ship model-powered product improvements, integrate and productionize models, improve model performance and reliability, and build evaluation frameworks and experiments to measure and improve quality.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Build, iterate, and evaluate production underwriting models (DecisionAssist); own feature engineering and predictive/risk modeling; design and analyze A/B tests and experiments; perform analytics and ad-hoc research in Snowflake/dbt; collaborate with Product and Engineering to translate rental risk into models and clear recommendations.

Lead design, development, and deployment of large-scale production ML systems and platform strategy. Research and prototype ML and big-data technologies, integrate ML services across the org, mentor engineers and data scientists, and communicate complex ML solutions to technical and non-technical stakeholders.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Own AI product strategy and roadmap across clinician- and member-facing surfaces. Partner with engineering, applied ML, design, and clinical teams to ship, scale, and measure LLM/ML-powered features; set safety and quality guardrails, run evals, make model build-vs-buy decisions, and translate ML capability into clinical workflow outcomes.

Embed with clinical and operations teams to identify high-impact problems and build production AI systems. Design and deploy LLM/RAG-driven tools, data pipelines, APIs, models, and user-facing applications using messy clinical and claims data. Iterate quickly based on real-world feedback and operational needs.