About Us Hungryroot is using AI to build the most consumer-centric food and wellness company to ever exist. We act as your personal assistant for healthy living—getting to know your goals, lifestyle, and budget, and recommending and delivering healthy groceries, easy recipes, and essential supplements for you and your family. It’s the easiest way to eat healthy, achieve your goals, save time, and discover new foods. We believe food is the foundation of health, convenience should not mean compromise, and that everyone is unique in how they eat and live. That’s why we’re building a future in which healthy living is both easy and enjoyable. Hungryroot is a distributed team of top talent across 28+ U.S. states. While we have a headquarters in New York City, our remote-first culture emphasizes collaboration, team-building, and flexibility. Expect regular virtual team events, strong ownership and accountability, and an annual company retreat. About the role We’re looking for a Senior Operations Research Engineer to be a part of our growing Data Science team. You’ll be responsible for working directly on our core Machine Learning/Operations Research algorithms for: grocery personalization, recipe selection, supply chain forecasting, clustering and more. As a member of the team, you will be building models collaboratively with others, and implement them in experimentation and production settings. You will be analyzing results of experiments, and will deep dive into the results to understand their impact, as well as plan next iterations. This role will join Hungryroot’s Data Science team and you’ll report directly to our Director of Data Science. In this critical role you will work closely with our data scientists and operation research experts to tackle the core challenges at the company that will be crucial to the success of Hungryroot. If you want to help us change how people grocery shop and eat for the better, we’d love for you to apply! Responsibilities - Building out Operations Research modeling for our box filling algorithms, inventory forecasting, supply chain optimization - Machine learning modeling for our core grocery personalization algorithms - Data analysis, discussion and interpretation of experiment results - Data analysis and interpretation of production data to inform future experiments and decisions Qualifications - Experience with optimization tools and libraries (Gurobi, CPLEX, PuLP) Gurobi is our primary platform for linear optimization - Knowledge of Machine Learning algorithms and systems, with at least one application built and deployed to production - Strong programming skills in Python - Proficiency in data manipulation, analysis, and visualization. - Excellent problem-solving skills and the ability to translate business problems into mathematical models. - Demonstrated ability to work independently and collaboratively in a fast-paced environment. - Be able to discuss the pros and cons of various solutions to a problem. - Ability to explain complex technical concepts to non-technical stakeholders. Nice to Haves - Machine learning frameworks (e.g., scikit-learn, PyTorch) - Masters or PhD in Operations Research Perks & Benefits - Remote-first: work from home, work from our NYC office, work from anywhere in the U.S. - you decide! - Equity - Unlimited vacation policy - Universal paid parental leave - Monthly Hungryroot credit for delicious, healthy groceries - Comprehensive health, vision, dental, and life insurance - 401k with Company Match - A work from home stipend to support your initial home-office setup Expected Salary Range $168,00-$210,000 #LI-REMOTE The employer will not sponsor applicants for work visas. Our mission to help make healthy eating easy, accessible, and joyful is better served by a diverse workplace. We are a proud Equal Opportunity Employer committed to building an inclusive workplace. We have zero-tolerance for harassment or discrimination. We do not discriminate on the basis of any protected class.

• Building out Operations Research modeling for our box filling algorithms, inventory forecasting, supply chain optimization • Machine learning modeling for our core grocery personalization algorithms • Data analysis, discussion and interpretation of experiment results • Data analysis and interpretation of production data to inform future experiments and decisions

This internship offers hands-on experience supporting high fidelity simulation, modelling, and analysis optimization of laser-based quantum enrichment systems to improve isotope selectivity, throughput, reliability, and scalability. Working with guidance from the Quantum Enrichment Simulation Lead and in collaboration with experimental teams, contribute to verification and validation (V&V) efforts and ensuring that simulation outputs are actionable for optical train design, flowfield architecture, separation chamber layout, and process optimization. The role will provide exposure to rarefied gas dynamics, particle/DSMC methods, and laser–matter interaction concepts, and will contribute to verification and validation activities and clear technical documentation. This role is based in Austin, TX / remote and is intended for a current student or recent graduate seeking hands‑on experience in advanced multiphysics simulation in a fast‑paced development environment. - Solid understanding of core fluid mechanics, including compressible flow concepts; exposure to high‑speed or rarefied gas dynamics (e.g., in coursework or projects) is a plus. - Interest in kinetic descriptions of gases (e.g., Boltzmann‑type approaches, DSMC, gas‑kinetic schemes) and willingness to learn how these are applied to rarefied or separation devices. - Exposure to or curiosity about laser–plasma or laser–molecule interaction, internal energy mode excitation, or radiation–matter interaction; prior project or research experience in this area is a plus. - Interest in isotope‑selective laser excitation schemes (e.g., AVLIS, MLIS, condensation repression, multiphoton dissociation, or photoionization); prior familiarity is a plus, not a requirement. - Willingness to learn verification and validation principles for multiphysics simulations, including basic concepts of code and solution verification, validation hierarchies, and uncertainty quantification. - Ability to work collaboratively with experimental teams and to use data to help calibrate and improve models with guidance. - Strong problem‑solving and systems‑thinking mindset, with an interest in using simulations to inform design changes and improve hardware, optical layouts, or process performance. - Clear written and verbal communication skills, including the ability to document modeling work and explain assumptions and limitations to technical audiences. - Experience with Star-CCM+ or other relevant codes - Currently pursuing or recently completed an M.S., or Ph.D. in Mechanical Engineering, Aerospace Engineering, Nuclear Engineering, Applied Physics, or a closely related discipline with a focus on fluid mechanics, high-speed aerodynamics, or related fields. - Prior experience (course projects, research, internships, or personal projects) involving CFD, gas‑kinetic simulations, rarefied flows, or high‑speed compressible flows is strongly preferred but not strictly required. - Experience working with experimental programs (e.g., wind tunnels, lab test rigs, plasma or laser experiments, or separation devices) is a plus. - Demonstrated interest in advanced simulation, laser‑based technologies, or nuclear/isotope separation applications through coursework, clubs, research, or internships is a plus. - Under the guidance of senior team member, support high-fidelity simulation campaigns for laser-based quantum enrichment systems, including continuum and rarefied gas flows, DSMC-based molecular beams, and coupled excitation/ionization physics in the separation region. - Assist with geometry definition, mesh or particle-resolution setup, basic model selection, numerical setup, and post-processing workflows for laser enrichment unit simulations, following established procedures and review. - Help implement and test hybrid continuum–kinetic frameworks (e.g., CFD–DSMC coupling) under guidance, including straightforward domain decomposition and selecting appropriate models across different Knudsen number regimes. - Contribute to the setup and analysis of simplified laser–matter interaction and excitation models (with mentoring), including isotope-selective excitation concepts and basic relaxation/ionization pathways as appropriate to the system. - Work with experimental teams to support targeted tests (nozzle expansions, beamlines, laser interaction zones, optical diagnostics), providing pre-test modelling support and helping compare simulation outputs to post-test data. - Assist in simulation-based sweeps and parametric studies to help identify sensitivities and trends in enrichment factor, efficiency, and process robustness. - Support basic uncertainty quantification, sensitivity analysis, and optimization activities under the supervision of senior engineers, contributing inputs and analyses that strengthen simulation-based decisions. - Prepare clear plots, summaries, and short technical notes that document modelling assumptions, setups, and key findings for internal stakeholders, incorporating feedback from the team. - Collaborate with process engineering and product teams, with support from the simulation lead, to connect simulation outputs to system-level metrics (e.g., separation factor trends, qualitative impacts on capacity or efficiency). - Perform literature reviews and basic benchmarking of relevant CFD, DSMC, and laser-interaction methods, summarizing findings to support ongoing model development.

Role Description Templar is looking for a Research Engineer to work across the post-training and pre-training stacks in a decentralized, community-driven training environment. You will contribute to state-of-the-art post-training pipelines running on real-world decentralized infrastructure, implement and evaluate ideas relevant to scaling large-scale post-training, and help push the frontier of what distributed LLM training can do. This is a research engineering role — you will be both building systems and contributing to the ideas that shape them. The environment is fast-moving, highly technical, and fully remote. What You'll Do - Contribute to the development of decentralized training of large language models - Work across the post-training and pre-training stacks - Implement and evaluate ideas relevant to scaling large-scale post-training on decentralized infrastructure - Contribute to training runs and writing technical reports Qualifications - Strong programming skills with experience training models across multiple devices - Solid foundations in machine learning - Clear written and verbal communication skills - Ability to work independently in a fast-moving, remote environment Requirements - Experience with LLM RL post-training or large-scale pre-training - Publications or research experience in relevant areas such as distributed learning, reinforcement learning from human feedback, or scalable training infrastructure