Staff Machine Learning Engineer

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $220k - $265k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

👻 Ghost score 1%

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Tilt (formerly Empower)

WebsiteLinkedInAll Job Openings

201 - 500 employees

💳 Fintech

👥 B2C

💸 Finance

Fintech • B2C • Finance

Tilt is a financial technology company that provides cash advance, lines of credit, and credit card services aimed at empowering customers financially. With an emphasis on accessibility, Tilt offers flexible repayment plans and eligibility irrespective of a customer's credit history, enabling individuals to qualify based on their real-time financial habits. The platform is user-friendly, boasting positive customer experiences and a high rating, making it a convenient option for those seeking to manage their finances and build credit effectively.

📋 Description

• Run the managed feature store, including tooling for defining, backfilling and serving features • Make the feature store the starting point for model training and deployment • Build and improve automated ML training and deployment platforms • Create self-service agents and skills for feature registration, training jobs, model promotion and monitoring • Standardize schedulable, reproducible training and retraining backed by feature-store data • Scale batch scoring and low-latency online inference with autoscaling, resource isolation and cost controls • Extend monitoring for data drift, feature drift, prediction quality and serving health • Define and lead adoption of standards for model and feature versioning, lineage, testing and documentation • Partner with data scientists and backend engineers to improve platform adoption and deployment speed

🎯 Requirements

• Deep hands-on experience as a platform or software engineer working in ML • Track record of platforms serving many models in production across multiple teams • Experience with feature stores and model deployment platforms such as Databricks Feature Store, Feast, Tecton or equivalent • Strong CI/CD for ML, including GitHub Actions, Jenkins, Azure Pipelines or similar • Experience with model registries and infrastructure as code with Terraform • Cloud expertise in AWS or Azure for ML deployment • Experience with Kubernetes and workflow orchestration • Strong command of batch and real-time serving, autoscaling, latency and cost trade-offs • Experience building self-service tooling adopted by data scientists • Experience collaborating with backend engineers to deploy models • Ability to gather requirements, propose solutions and drive projects to completion amid ambiguity • Sufficient depth in model development and common algorithms to partner technically with data scientists • Familiarity with PySpark, Databricks and Evidently AI is a plus

🏖️ Benefits

• Equity • Bonus • WFH office reimbursement • Flexible health plans at every premium level • Substantial health-plan subsidies • Paid global onsites twice yearly • Growth opportunities based on contributions • Virtual-first teamwork

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