Senior Machine Learning Engineer (Search)

Scribd•San Francisco, CA

1d•$129,500 - $230,000

About The Position

We’re looking for a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML discovery features that serve millions of users in near real time. You’ll work across the entire lifecycle — from data ingestion to model training, deployment, and monitoring — with a focus on creating fast, reliable, and cost-efficient pipelines. In this role, you will: Lead complex, cross-team projects from conception to production deployment. Drive technical direction for end-to-end, production-grade ML systems for advanced search capabilities and document understanding. Develop and operate services that power high-traffic pipelines for content discovery and knowledge synthesis. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Mentor other engineers and establish best practices for building scalable, reliable ML systems.

Requirements

6+ years of experience as a professional ML engineer or software engineer, with a proven track record of delivering production ML systems at scale.
Proficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered).
Expertise in designing and architecting large-scale ML pipelines and distributed systems.
Deep experience with distributed data processing frameworks (Spark, Databricks, or similar).
Strong cloud expertise (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda).
Experience with embedding-based retrieval, large language models, advanced information retrieval and ranking systems.
Experience working with Search systems like query parsing, query intent classification, bm25, reranking, etc.
Proven ability to optimize system performance and make informed trade-offs in ML model and system design.
Experience leading technical projects and mentoring engineers.
Bachelor’s or Master’s degree in Computer Science or equivalent professional experience.

Responsibilities

Lead complex, cross-team projects from conception to production deployment.
Drive technical direction for end-to-end, production-grade ML systems for advanced search capabilities and document understanding.
Develop and operate services that power high-traffic pipelines for content discovery and knowledge synthesis.
Run large-scale A/B and multivariate experiments to validate models and feature improvements.
Mentor other engineers and establish best practices for building scalable, reliable ML systems.
Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks.
Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks.
Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services.
Design and run A/B and N-way experiments to measure the impact of model and feature changes.
Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.

Benefits

Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
12 weeks paid parental leave
Short-term/long-term disability plans
401k/RSP matching
Onboarding stipend for home office peripherals + accessories
Learning & Development allowance
Learning & Development programs
Quarterly stipend for Wellness, WiFi, etc.
Mental Health support & resources
Free subscription to the Scribd Inc. suite of products
Referral Bonuses
Book Benefit
Sabbaticals
Company-wide events
Team engagement budgets
Vacation & Personal Days
Paid Holidays (+ winter break)
Flexible Sick Time
Volunteer Day
Company-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace.
Access to AI Tools: We provide free access to best-in-class AI tools, empowering you to boost productivity, streamline workflows, and accelerate bold innovation.