10+ years in applied machine learning/data science with a focus on recommender systems and large-scale personalization.
Hands-on experience with LLMs in production, including feature generation and integration.
Familiarity with Amazon Bedrock or similar managed LLM platforms for inference.
Experience in segment-based personalization, emphasizing performance measurement at the segment level.
Proven ability to design cold-start strategies for users or items with limited history.
Strong communication skills for explaining technical concepts to diverse stakeholders.
Customer-facing experience, adept at managing expectations and building trust in high-stakes situations.
Technical leadership experience, mentoring engineers and driving design decisions.
Responsibilities
Own the technical strategy for a personalization program, making architecture and modeling decisions.
Build features, train models, run experiments, and write critical code hands-on.
Support other engineers through design reviews, mentorship, and pairing.
Act as a senior technical point of contact, communicating progress and managing expectations with stakeholders.
Design and run a proof-of-concept to measure the impact of GenAI-based profiles.
Engineer user-level features from large-scale behavioral data.
Integrate LLM-generated user profiles into ranking models, ensuring proper signal weighting.
Define and run the offline evaluation framework with segment-level analysis.
Establish the path to production, including model serving and A/B testing readiness.
Deliver clear technical documentation and lead knowledge-transfer sessions for customer teams.
Benefits
Full-time employment with a focus on impactful projects.
Opportunity to lead technical direction in a high-stakes environment.
Hands-on involvement in cutting-edge AI and machine learning technologies.
Mentorship opportunities to support the growth of other engineers.
Engagement with senior stakeholders and customers, enhancing communication skills.
Full Job Description
AI Engineer
Location: Virginia
Employment Type: Full-Time
About the Role
We are hiring an AI Engineer to be the lead technical contributor on a personalization and ranking engagement for a large-scale consumer marketplace. You will set the technical direction, make the key modeling decisions, and stay hands-on throughout. You will be a senior technical point of contact with the customer - explaining trade-offs, managing expectations, and turning results into clear recommendations. You will lead a rigorous, POC-first program: engineering user-level features from behavioral data, integrating LLM-generated user profiles into a deep-learning ranking model, and driving the work from offline validation through production-readiness.
What You'll Do
Own the technical strategy for a personalization program on a production recommendation/ranking system, making the architecture and modeling decisions and being accountable for the results.
Stay hands-on: build the features, train the models, run the experiments, and write the critical code.
Set the technical bar and support other engineers through design reviews, mentorship, and pairing.
Act as a senior technical point of contact with the customer, communicating progress, risks, and results to both engineers and senior stakeholders, and managing expectations through ambiguity.
Design and run a structured, parallel-track proof-of-concept that measures the incremental lift of GenAI-based profiles over well-engineered behavioral ML features.
Engineer user-level features from large-scale behavioral data (category/product affinity, time-of-day and price-sensitivity patterns, per-user click/conversion history, recency-frequency signals).
Integrate LLM-generated user profiles into ranking models, including embedding generation, projection-layer tuning, gating, and ablation to ensure the signal is properly weighted.
Own the deep-learning ranking model (multi-task CTR/CVR architectures such as shared-bottom MTL), including feature integration, hyperparameter optimization (Bayesian/grid search), and bias correction (position/popularity).
Define and run the offline evaluation framework - NDCG, MRR, Precision/Recall at K - with segment-level analysis and ablation studies across user cohorts.
Establish the path to production: model serving and scheduled inference integration, shadow-mode testing, A/B framework readiness, and guardrail metrics.
Deliver clear technical documentation and lead knowledge-transfer sessions so the customer's teams can operate and iterate independently after handoff.
Required Qualifications
10+ years in applied machine learning / data science, with deep hands-on experience in recommender systems, learning-to-rank, or large-scale personalization.
Practical experience building with LLMs in production: generating and integrating model-derived features or profiles, working with embeddings, and reasoning about evaluation, latency, and cost.
Experience with Amazon Bedrock or comparable managed LLM platforms for production inference.
Hands-on experience with segment- or cohort-based personalization, including measuring performance at the segment level rather than relying on aggregate metrics.
Experience designing cold-start strategies for users or items with limited history.
Strong communication skills - able to explain modeling decisions, trade-offs, and results clearly to engineers, data scientists, and senior business stakeholders, and to manage expectations through ambiguity.
Customer-facing or stakeholder-facing experience: building trust, navigating competing priorities, and serving as a senior technical voice in high-stakes conversations.
A track record of technical leadership through mentoring engineers, driving design decisions, and setting standards.
Strong track record taking ML models from experimentation to production, owning the offline-to-online validation story (ranking metrics, ablations, segment analysis, shadow testing, A/B readiness).
Deep, hands-on expertise in deep learning for ranking/recommendation - multi-task learning, embedding-based architectures - with a major framework (TensorFlow or PyTorch).
Strong feature engineering on large behavioral datasets using the modern data stack (PySpark, SQL, distributed data lakes).
Rigorous experimental methodology - hyperparameter optimization, bias correction, and a disciplined, hypothesis-driven approach to measuring true lift.
Hands-on AWS experience across the ML lifecycle, and strong proficiency in Python.
Preferred Qualifications
Experience personalizing ranking for marketplaces or consumer platforms at scale (e-commerce, food delivery, media, or similar).
MLOps maturity: model versioning, monitoring, and reproducible training pipelines.
Advanced degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.
Prior experience in a client-facing consulting or professional-services delivery environment.
Compensation
The base salary range for this position is $180,000 - $200,000 USD annually, depending on experience, qualifications, and internal equity. This range reflects the anticipated base pay for this role and does not include potential bonuses, benefits, or other forms of compensation.
