Senior Machine Learning Engineer, Personalization & Decision Science

About Cooklist

Cooklist is the AI grocery-intelligence platform powering meal planning and shopping for millions of consumers across our consumer app and white-label enterprise suite. Our mission is to combine the intelligence of a personal shopper, chef, and nutritionist to help people save time, eat better, and enjoy happier lives.

We're profitable, process billions of dollars in transactions at the nation's largest retailers, and our mobile experiences reach millions of people. We're backed by Techstars, Mercury Fund, and industry leaders including the former Chief Technology Officer of a leading U.S. grocer and the former Chief Product Officer of Amazon Fresh.

Role Overview

We're hiring a Senior Machine Learning Engineer to build and operate the models that decide which meals, recipes, products, substitutions, replenishment items, and complete baskets Cooklist should recommend. You will turn household context, food knowledge, retailer assortment, purchase history, and live intent into decisions that are individually relevant and collectively coherent.

A conventional recommender might correctly predict that a household likes five chicken recipes. Cooklist must recognize that this creates a repetitive week and instead assemble a plan that fits servings, budget, nutrition, dietary restrictions, cooking time, variety, package sizes, ingredient reuse, pantry state, inventory, and the household's actual preferences.

This is a senior individual-contributor role with direct production ownership. You will translate Cooklist's personalization strategy and system goals into models, constraints, optimization approaches, and evaluation plans, then own their execution and ongoing performance.

Responsibilities

• Build Cooklist's recommendation stack: design and productionize multi-stage systems for candidate generation, retrieval, filtering, scoring, ranking, reranking, and final selection across recipes, products, meals, collections, substitutions, and replenishment items.
• Model household preferences: learn individual and shared household affinities from explicit preferences, order history, recipe interactions, cart edits, substitutions, removals, repeat purchases, and conversational feedback.
• Distinguish taste from circumstance: identify when an interaction represents a durable preference versus a temporary need, guest purchase, holiday, party, work lunch, children's food, one-time recipe, or another contextual exception.
• Optimize complete baskets: formulate meal and grocery planning as constrained, multi-objective problems balancing household value with budget, servings, nutrition, allergens, convenience, variety, ingredient reuse, package sizes, pantry carryover, leftovers, and availability.
• Predict replenishment and substitutions: estimate recurring needs from purchase timing, quantity, household size, seasonality, cadence, and uncertainty; rank replacements using functional equivalence, recipe context, diet, brand, price, package size, inventory, and predicted acceptance.
• Handle cold start and catalog variation: personalize for new households, members, recipes, products, and sparse users while connecting canonical foods to store-specific SKUs and adapting to assortment, price, promotions, out-of-stocks, regional availability, and catalog drift.
• Evaluate at the basket level: go beyond item relevance to measure constraint violations, diversity, calibration, coherence, waste, ingredient reuse, acceptance, cart edit distance, substitution acceptance, order behavior, repeat usage, and retention through offline replay and online experiments.
• Take models to production: own pipelines, batch and low-latency inference, caching, versioning, monitoring, drift detection, fallbacks, and rollback; expose candidates, scores, confidence, constraints, reason codes, and alternative plans through services Cooklist's applications and AI agents can safely consume.

Qualifications

• You have typically 5+ years of experience in machine learning engineering, applied science, recommender systems, operations research, or a related quantitative field.
• You have shipped a meaningful recommendation, ranking, forecasting, personalization, or optimization system into production and owned its performance after launch.
• You have deep expertise in recommender systems, retrieval, ranking, embeddings, or user modeling and meaningful working knowledge of forecasting, mathematical optimization, or other decision-science methods.
• You understand modern recommendation methods such as collaborative filtering, content-based models, two-tower retrieval, sequence models, learning-to-rank, reranking, calibration, exploration, and contextual bandits - and know when a simpler approach is better.
• You are highly proficient in Python and SQL and write maintainable, rigorously tested production code - not only research notebooks.
• You understand implicit-feedback problems including exposure bias, negative sampling, delayed outcomes, sparse labels, popularity effects, feedback loops, and the difference between 'not selected' and 'disliked.'
• You understand experimentation and causal reasoning well enough to distinguish genuine model improvement from noise, leakage, bias, or misleading offline movement.
• You can operate across the production ML lifecycle, including feature freshness, training-serving consistency, latency, caching, monitoring, data and model drift, graceful degradation, and retraining.
• You communicate clearly with engineers, product leaders, culinary stakeholders, and founders, and you care about what makes a meal complete, a week varied, a substitution valid, and a shopping cart believable.

Bonus Points

• Experience in grocery, meal kits, food delivery, retail, e-commerce, marketplaces, subscriptions, or another constrained recommendation environment.
• Experience with consumption or repeat-purchase forecasting, survival analysis, mathematical programming, constraint optimization, or real-time recommendation systems.
• A master's degree or PhD in computer science, operations research, statistics, applied mathematics, industrial engineering, economics, or a related field.

Our Stack

• Application & interfaces: Python/Django backend; JavaScript/React Native frontend; GraphQL and real-time streaming over WebSockets
• Data domain: recipes, ingredients, products, retailer catalogs, transactions, carts, prices, promotions, inventory, nutrition, preferences, and pantry signals
• Modeling: Python-first recommendation, ranking, forecasting, experimentation, and optimization workflows using the right mix of modern ML, classical statistics, and mathematical programming
• Serving & evaluation: batch and real-time model services integrated with Cooklist's Django backend; offline replay, synthetic household scenarios, basket-level regression suites, online experiments, and production monitoring

What We Offer

• Competitive compensation + meaningful equity
• Austin, TX based with WFH flexibility
• Work directly with founders and an elite, tight-knit team
• Ship products and experiences that materially improve the lives of millions
• A high-intensity, high-ownership environment designed for builders