Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers. By leveraging proprietary AI designed for high-volatility environments, Afresh empowers partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise. Afresh has scaled to 6 enterprise-grade solutions, live in over 10% of the U.S. grocery market, and prevented over 200 million pounds of food waste last year alone.
About the Role:
The Afresh Intelligence team is responsible for the development and performance of AI/ML models that power Afresh's core replenishment technology, which is directly responsible for ordering millions of dollars of fresh inventory across the world every day. Fresh food ordering is an extremely complex high-dimensional decision-making problem, with challenges including decaying product, uncertain shelf lives, varying consumer demand, stochastic arrival times, extreme weather events, and tight performance constraints. The team tackles these problems with a mix of machine learning, large-scale simulation, and optimization technologies. Afresh is looking for a Senior Applied Scientist to drive R&D work, applying existing knowledge of machine learning, forecasting, operations research, and stochastic optimization to the challenging problem of perishable inventory control - researching, implementing, and rigorously validating improvements to the core replenishment system, including modeling consumer demand, item-level perishability, and complex multi-echelon supply chains.
Responsibilities:
• Model complex problems such as inventory decay, promotions, price elasticity, and inventory uncertainty, and implement solutions to multi-stage and multi-echelon inventory optimization problems
• Drive fundamental changes to the core system from research through production, writing rigorously tested and scalable code
• Advance research and development for new product and business challenges
• Raise the technical bar across the Intelligence team: mentor scientists and engineers, set standards for experimental rigor, and review designs and results
• Push the boundaries of AI capabilities in both products and scientist workflows
Requirements:
• MS or PhD in Operations Research, Industrial Engineering, Computer Science, Electrical Engineering, or another quantitative field, or equivalent practical experience
• For candidates with an MS, 4+ years of industry experience; for candidates with a PhD, some industry experience preferred
• Experience researching and building systems that support large-scale decision making under uncertainty
• Prior experience or academic knowledge in areas such as inventory optimization, supply chain management, network optimization, forecasting, game theory, decision analysis, stochastic optimization, or approximate dynamic programming is a plus
• Excellent communication and presentation skills - able to explain complex mathematical ideas to product teams in plain English and translate business requirements into constrained optimization problems
• Ability to independently deliver high quality software implementations in the Python data stack (numpy/torch/pandas/etc.); prior experience with Python is not required
• Nice to have: understanding of ML Platform and a passion for mentorship
Salary Range: $156,060 - $211,140 + meaningful early-stage equity + benefits
Benefits: Comprehensive medical, dental, and vision coverage with the majority of premiums covered by Afresh, plus mental health support and counseling services. Competitive base salary, meaningful equity, and a 401(k) program with company match. Home office stipend and Coworking Wallets for flexible workspace access. Annual professional development budget. Monthly stipends for wellness/lifestyle and telecommunications. Flexible paid time off.
For more information or to apply, visit the Afresh careers site. Location: Remote - United States (must reside in an eligible state). Afresh provides equal employment opportunities to all employees and applicants for employment.
