Principal Machine Learning Engineer
About this role
Professionals who thrive at the intersection of hands-on infrastructure execution and academic research will find a strong fit in this opportunity at Thrive Market. The engineering team builds and maintains advanced machine learning systems that power search and discovery for an online membership market focused on sustainable living. Day-to-day work involves translating alternative model architectures from research papers into production-grade pipelines, managing infrastructure like Redis clusters, and communicating complex technical results to broad audiences.
This position exists to guide the long-term vision of search capabilities as large language models continue to reshape the e-commerce landscape. Candidates who enjoy operating across the entire lifecycle of a machine learning model—from initial experiment to business impact measurement—will succeed here. An ideal applicant brings deep technical expertise while remaining eager to collaborate cross-functionally and debate statistical methodology with peers.
What you will do
- Develop new machine learning systems from scratch, address underlying data gaps, and optimize existing pipelines.
- Analyze alternative model architectures from research literature and execute training experiments to benchmark performance against current production baselines.
- Design and administer necessary infrastructure, such as Redis clusters, to support demanding machine learning workloads.
- Collaborate with the broader engineering team to guide architectural decisions and statistical methodology.
- Formulate and drive the long-term search and discovery strategy in light of recent advancements in large language models.
- Communicate model outcomes, technical roadmaps, and resulting business impact to executive and broad audiences.
What you need
- Bring over eight years of machine learning engineering experience that bridges both MLOps execution and research-oriented tasks.
- Demonstrate principal-level impact through the complete lifecycle, moving from research papers to production implementations and business presentations.
- Show hands-on capability in reading and executing machine learning literature for comparative model evaluations.
- Possess practical experience building and maintaining machine learning infrastructure and pipelines.
- Maintain an informed perspective or direct background regarding search and discovery systems, particularly involving large language models.
- Act as a cross-functional driver capable of debating architecture and statistical methodology across different teams.
Nice to have
- Hands-on experience with search, personalization, recommendations, ranking, or lifecycle modeling.
- Familiarity with MLOps best practices including model versioning, CI/CD for machine learning, monitoring, and containerized environments like Docker and Kubernetes.
- Prior background working in e-commerce, marketplace, or subscription-based business models.
- Experience navigating environments with moderate technical debt or evolving data foundations.
- Demonstrated ability to define and own metrics, experimentation frameworks, or production model performance monitoring.
Benefits
- Comprehensive health benefits including medical, dental, vision, life, and disability coverage
- Competitive salary and equity
- 401k employer match
- Flexible paid time off and 9 observed holidays
- Subsidized ClassPass membership and coverage for life coaching and therapy sessions
- Free Thrive Market membership with exclusive employee discounts
Skills and tech stack
Questions candidates ask
Where is this job located and what is the remote policy?
This role is based in Playa Vista, California or offered as remote. Employment with Thrive Market requires that employees be based in the United States. Additionally, the position requires traveling to the headquarters office in Los Angeles twice a year for all-company summits.
What is the compensation range for this role?
The base salary range for this position is $175,000 to $250,000 per year. Total compensation also includes stock options, health and wellness benefits, flexible PTO, and other perks.
What technologies and systems will I work with?
You will build new machine learning systems, maintain existing pipelines, and manage infrastructure such as a Redis cluster. The role also involves working with containerized and orchestrated environments like Docker and Kubernetes.
What kind of background is expected for this position?
Candidates need 8+ years of machine learning engineering experience spanning MLOps and research-oriented work. You should have demonstrated principal-level impact across the full loop from research paper to production implementation.