Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Phia, you’ll build and scale production ML systems that power core product experiences and decision-making. You’ll work across the full ML stack, from data and modeling to deployment and iteration, on problems like ranking, personalization, and optimization. This role sits at the intersection of machine learning, product engineering, and data platforms, with ownership over systems that directly impact growth and user experience. You’ll ship models to production, run experiments at speed, and help define how machine learning is done as Phia scales.

Phia has raised $43M from Notable Capital, Khosla Ventures, and Kleiner Perkins, with backing from founders and operators like Vlad Tenev (Robinhood), Mellody Hobson (Ariel Investments), Naomi Gleit (Meta), and Mati Staniszewski (ElevenLabs), plus a roster of cultural leaders, to build the AI alignment layer for commerce. In just over a year, Phia's consumer shopping agent has surpassed 1.5 million users and partnered with 9,600+ retail brands across contemporary, resale, and luxury, representing billions in annual gross merchandise volume. We scan more than 350 million products to help shoppers find the right pieces at the best price, cutting return rates by 50%, and we're on pace for nine-figure sales growth this year.

In an era where AI vertical agents are reshaping every industry, commerce is on the verge of a complete transformation. Phia is reinventing shopping from a fragmented, impersonal experience into one that feels intelligent, trusted, and built around each user's intent. This foundation of trust is our wedge to become the end-to-end shopping destination for the next generation of buyers.

Phia is a lean, high-ownership team building at startup speed. If you want to ship at high velocity and solve complex problems in consumer AI and commerce, this is the place to do it.

• Design, develop, and deploy production machine learning models that power core product experiences and decision-making systems

• Own the end-to-end machine learning lifecycle, including data analysis, feature engineering, model training, evaluation, deployment, and monitoring

• Partner closely with Product, Engineering, Data, and Operations to translate product requirements and business goals into scalable ML solutions

• Develop experimentation frameworks and causal measurement strategies to evaluate model impact and inform product decisions

• Build and maintain forecasting, ranking, personalization, or optimization systems operating at scale

• Drive improvements to model performance, reliability, and scalability in production environments

• Contribute to the ML platform and infrastructure, improving tooling for training, experimentation, and monitoring

• Influence technical direction through design reviews, code reviews, and mentorship of other engineers
  

• 3+ years of industry experience building and deploying machine learning systems in production

• Strong proficiency in Python and experience with common ML frameworks and libraries (e.g., PyTorch, TensorFlow, XGBoost, LightGBM, scikit-learn)

• Experience owning the full ML lifecycle, from data exploration to production deployment and iteration

• Experience working with large-scale, real-world datasets and noisy or incomplete data

• Solid understanding of experiment design and causal inference, including A/B testing and offline evaluation

• Ability to collaborate effectively with cross-functional partners and communicate technical concepts clearly

• Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field, or equivalent practical experience