Senior Staff Applied ML Engineer

We’re hiring Applied ML Engineers to partner with multiple product teams to extract insights from data and build AI-powered features and automated workflows across the product suite. 

In this role, you will both: 

• Enable product teams: teach, coach, and guide them on data and ML best practices 

• Lead by example: do complex data analysis and ML modeling, architecture, and implementation work as needed to accelerate teams while mentoring more junior data/ML folks. 

You’ll own the data analysis, ML modeling, and workflow logic that let AI understand user requests, enrich and route them, suggest actions, and in some cases fully automate resolution.  

Data & ML Modeling 

• →Explore and analyze data using Python, pandas, and PySpark (or similar tools). 

• →Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions). 

• →Create, tune, and productionize ML models for: 

• →Categorization / classification 

• →Recommendations and similarity 

• →Other prediction or ranking tasks that power product features 

AI-Powered Workflows & Features 

• →Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use. 

• →Build workflows where AI can: 

• →Auto-fill or suggest key fields and metadata. 

• →Proactively ask users/customers for missing or ambiguous information (e.g., via email or messaging). 

• →Surface similar past items or solutions to assist humans in decision-making. 

• →Fully handle simple, repetitive “Level 1” style requests end-to-end when safe to do so. 

• →Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails. 

Cross-Team Leadership & Enablement 

• →Work with multiple product teams to help them identify and scope AI opportunities in their areas. 

• →Define patterns, templates, and best practices for data ingestion, feature creation, model usage, and evaluation that teams can reuse. 

• →Serve as a trusted advisor and technical lead: 

• →Provide design and architecture guidance on data and ML-heavy features. 

• →Join projects to handle the most complex modeling or workflow automation pieces when teams get stuck. 

• →Mentor and guide junior data/ML engineers and analysts: 

• →Conduct code and model reviews. 

• →Pair with them on tricky problems. 

• →Help them develop good intuitions about metrics, evaluation, and operational reliability. 

• →Help establish and socialize standards for experimentation, documentation, and responsible AI usage across teams. 

 

What You’ll Bring 

Core Skills 

• →5+ years in data science, ML engineering, or a similar applied role, with a strong record of shipping production data/ML features. 

• →Strong Python skills and experience with pandas for data analysis. 

• →Experience with PySpark or other distributed data processing frameworks. 

• →Solid understanding of ML fundamentals, including: 

• →Supervised learning and classification models 

• →Matrix factorization / embeddings / latent factor models 

• →Feature engineering and model evaluation (offline metrics and online experiments) 

• →Proficiency with PyTorch (or a similar deep learning framework) and related ML tooling. 

• →Strong SQL and experience with modern data warehouses / data lakes. 

• →Comfort working with APIs, microservices, and production integration of ML models, including performance and reliability considerations. 

Leadership & Collaboration 

• →Experience serving as a technical lead or senior individual contributor across multiple teams or projects. 

• →Proven ability to translate business problems into data/ML projects, and to clearly explain tradeoffs to non-ML stakeholders. 

• →Track record of mentoring junior engineers/analysts and improving team practices (e.g., review culture, testing, monitoring). 

• →Strong communication skills and the ability to drive alignment across product, engineering, and operations. 

Nice to Have 

• →Experience with LLMs and language-centric workflows (RAG, prompt engineering, fine-tuning, tool/agent orchestration). 

• →Experience building agent-assist features or automated workflows in operational or customer-facing products. 

• →Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex, etc.) and production model monitoring. 

• →Prior experience in a platform/enablement role, supporting many product teams with shared data and ML capabilities.