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About This Role
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
$168,000\-$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.*
Salary Context
This $168K-$210K range is above the median for Research Engineer roles in our dataset (median: $185K across 51 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,824 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Hungryroot, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $260,000 based on 401 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($189K) sits 27% below the category median. Disclosed range: $168K to $210K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Hungryroot AI Hiring
Hungryroot has 1 open AI role right now. They're hiring across Research Engineer. Based in New York, NY, US. Compensation range: $210K - $210K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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