Principal Machine Learning Engineer

Thrive Market; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Thrive Market seeks a Principal Machine Learning Engineer to bridge hands-on MLOps with research-level thinking. You will read cutting-edge ML papers, debate architectures, and translate model results into clear business impact at company All Hands.

Given how large language models reshape what's possible, you’ll help shape Thrive Market's long-term vision for search and discovery, spanning research to production with latitude to define how the work gets done.

Qualifications

• 8+ years of ML engineering with MLOps and research experience.
• Proven ability to move from research to production and present results.
• Experience building and operating ML infrastructure and pipelines (e.g., Redis cluster).
• Knowledge of search and discovery systems.

Responsibilities

• Build ML systems from scratch and maintain the ML pipeline.
• Read and implement ML papers and run training experiments.
• Help define long-term search vision, considering LLMs.
• Stand up and manage infrastructure, such as a Redis cluster.
• Contribute to architecture decisions and statistical methodology.
• Shape Thrive Market's long-term search vision informed by LLMs.
• Present model results and roadmap with business impact.

Skills

MLOps

Research-oriented work

Reading ML papers

Training experiments

Production deployment

Tools

Redis

Job description

ABOUT THRIVE MARKET

Thrive Market was founded in 2014 with a mission to make healthy and sustainable living easy and affordable for everyone. As an online, membership-based market, we deliver the highest quality healthy, and sustainable products at member-only prices, while matching every paid membership with a free one for someone in need. Every day, we leverage innovative technology and member-first thinking to help our over 1,700,000+ members find better products, support better brands, and build a better world in the process. We are also a Certified B Corporation, a Public Benefit Corporation, and a Climate Neutral Certified company.

Join us as we bring healthy and sustainable living to millions of Americans in the years to come.

THE ROLE

As Principal Machine Learning Engineer, you'll bridge hands-on MLOps execution with research-level thinking, a combination that spans everything from infrastructure to publication-grade experimentation. You'll read and implement cutting-edge ML papers, debate architecture and statistical methodology with the engineering team, and translate model results into clear business impact at company All Hands. Given how much large language models have reshaped what's possible, you'll also help shape Thrive Market's long-term vision for search and discovery. It's a role that runs the full arc from research to production to business outcomes, with real latitude to define how that work gets done.

RESPONSIBILITIES

• Build new ML systems from the ground up, close data gaps as they arise, and maintain and improve the existing ML pipeline.
• Read and implement ML papers on alternative model architectures, then run training experiments to benchmark results against current production models.
• From your scopehelp define the long-term search vision, especially given how LLMs have shifted what's possible
• Stand up and manage infrastructure, such as a Redis cluster, to support ML workloads.
• Weigh in on and help drive architecture decisions and statistical methodology alongside the broader engineering team.
• Help shape Thrive Market's long-term vision for search and discovery, informed by how large language models have changed what's possible to build.
• From your scopehelp define the long-term search vision, especially given how LLMs have shifted what's possible
• Present model results and roadmap, including business and revenue impact

QUALIFICATIONS

• 8+ years of machine learning engineering spanning both MLOps and research-oriented work, with demonstrated principal-level impact across the full loop from research paper to production implementation to business impact presentation
• Hands-on experience reading and implementing ML papers, and running comparative training experiments to evaluate new model architectures against production baselines
• Experience building and operating ML infrastructure and pipelines, such as standing up a Redis cluster
• Experience with, or an informed perspective on, search and discovery systems, partic