Technical Lead Manager, Machine Learning Operations
Owns the Data Science platform and leads a 4-person ML Ops team to build scalable infrastructure, embed into science projects, and drive AI tooling adoption. Requires 4-6+ years of ML engineering experience with strong Python and cloud/ML platform skills.
Responsibilities
- Lead and grow a team of four engineers spanning ML infrastructure, ML operations, and embedded data science project work
- Improve our internal ML platform: standardize and improve ML infrastructure, improve how DS services are created, deployed, and operated (service performance, permissioning, environment setup, integration with upstream/downstream systems)
- Set the roadmap for improving Machine Learning and Operations Research infrastructure
- Embed engineers into major science initiatives (forecasting, network orchestration, pricing) so every project is technically sound and lessons learned feed back into the platform
- Drive AI usage across DS: collaborate with Agentic Developer Experience team to ensure new tooling has high impact on velocity; set standards, introduce patterns, and drive adoption of AI in data science workflows (EDA, model iteration, ML/OR methodologies)
- Be part of the on-call rotation for data science production systems
- Write code, review designs, and set the technical bar
- Partner closely with Agentic Developer Experience and Builder Experience teams
Requirements
- Bachelor’s Degree plus at least 6 years of experience in Machine Learning Engineering, or Master’s Degree plus at least 4 years in Machine Learning Engineering
- ML platform experience: training and serving infrastructure, feature stores, orchestration, monitoring, deployment pipelines
- Experience managing impactful, high-velocity ML Platform / ML Ops teams in smaller scale companies
- Experience driving AI/agentic tooling adoption inside an organization
- Hands-on experience with open-source tooling for large-scale ML (e.g., Ray, Flink, Feast)
- Strong knowledge of Cloud-based data engineering and data science tools (AWS preferred) and Data Warehouses (Redshift, Databricks, Snowflake)
- Strong proficiency in Python
- Interest in building systems in a Supply Chain setting
Nice-to-Haves / Benefits
- Competitive equity package
- Comprehensive medical, dental, and vision coverage
- 401k and generous PTO for full-time roles
Senior Machine Learning Operations Engineer
Build and operate Mercury's real-time ML inference platform for fraud risk decisioning. Own model deployment, observability, and lifecycle tooling with strong backend Python fundamentals.
Machine Learning Engineer - Embedded Insights
Drive ML initiatives from concept to production on the Embedded Insights team. Identify opportunities, build and deploy models using Plaid's financial datasets, and partner with product teams to deliver scalable customer-facing intelligence products.
Machine Learning Engineer
Advance Plaid’s foundation models by developing novel architectures, pretraining objectives, and fine-tuning strategies. Work across the full ML stack from data engineering to production serving and monitoring.
Senior Machine Learning Engineer
Build and deploy cutting-edge Agentic AI and LLM systems to transform Airbnb's customer service experience, including Chat and Voice AI assistants. Requires 6+ years experience with production ML/AI systems at scale.