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About This Role
About Us
Higharc is a VC-backed startup that is changing how new homes are designed and built. Join a founding team who’ve shipped products for Autodesk, Electronic Arts, Nike, and Apple. We have raised over $175M with support from top-notch venture capital firms and more than 18 strategic investors: industry leaders in construction, building products manufacturing, and distribution.
Higharc is seeking a hands-on Applied Research Engineer to turn our spatial AI research into capabilities that homebuyers and builders use. You'll join our Platform R&D team, taking models that have proven themselves in research and shipping them into the product.
What You'll Do
Our team builds AI that understands homes, and our generative floor plan engine has moved from proof of concept to a core workflow in Higharc's product. You'll own the applied side of that work. The research team hands you a defined problem and a metric. You train and adapt the models, build the pipelines around them, and ship a component that meets the product's latency and cost budget.
You'll work inside an architecture that already exists. The engine, the schema, and the evaluation frameworks are in place, and your job is to make them faster, reproducible, and easy for others to build on.
Expect to:
- Build the component layer around our layout synthesis engine: a documented API contract, a service the product can call, and an evaluation gate that runs on every change.
- Turn model retraining into a one-command job, with benchmarks built in and results the whole team can read.
- Own the latency and cost budgets for learned capabilities.
- Extend core research into new capabilities, such as multi-story plans and real-time editing.
- Partner with infrastructure engineers on MLOps and deployment, and mentor researchers and interns on engineering practices.
- Share your work in writing through weekly status updates, clear pull requests, and design notes before big changes.
About You
You're happiest when a model you worked on is running in the product. You like taking a well-defined problem with a metric attached and closing it, and you improve an existing codebase without feeling the need to rewrite it. You think about what a 45-second wait means for a user before you think about what it means for a benchmark. You're honest about evaluation: you can tell when a result is an artifact of the test set, and you'd rather report a regression in writing than hide it behind a demo.
Most of your collaborators are remote and some are part-time, so writing things down comes naturally to you. You can go from a research conversation to a product conversation to an infrastructure conversation in the same week and be understood in all three. You also know enough about buildings to see that a bedroom door opening onto a kitchen is wrong before any metric tells you.
You have:
- 5+ years of professional software development, including at least 2 years training and shipping ML models to real users
- Taken at least one model end to end: data preparation, training, evaluation, serving, and the integration that put it in front of users
- Fluency in Python and PyTorch (TensorFlow or Keras is fine if you're ready to move to PyTorch)
- Experience with geometric or structured data such as floor plans, CAD or BIM, graphs, or 2D and 3D geometry
- Built or maintained an evaluation harness or benchmark and used it to gate changes
- Packaged a model behind an API (FastAPI or similar) and owned its latency and cost
- A master's degree in computer science, machine learning, or a related field, or equivalent shipped work (a PhD is not required)
- Comfort working remotely with a team on US Eastern hours
A major plus if you also bring:
- A background in architecture or building, whether that's a degree, professional practice, or AEC software work
- Experience with constraint solvers like OR-Tools CP-SAT, optimization in general, or generative models for layouts and floor plans
- Plugin or tooling work in Rhino, Grasshopper, or Revit, or experience with Hugging Face, experiment tracking, and cloud model serving on AWS or Modal, plus any publications or open source contributions
Working at Higharc
Remote work and travel: Our company is entirely remote, and has been since we were founded in 2018. Remote work means more time with family, less time commuting, and the flexibility to blend work and life. We value in-person collaboration and asynchronous deep-work time, which is why we schedule regular team meet-ups in our hubs across the US and, depending on the role, prioritize hiring in those hubs. If your role requires frequent travel beyond pre-scheduled team meet ups, we will represent that to the best of our ability as early as possible in the interview process.
Compensation and benefits: Higharc offers competitive salaries with significant equity, in a fast-growing, well-funded company. We provide comprehensive medical, dental, and vision coverage, with flexible PTO, and meaningful maternity/paternity leave to all U.S based employees that are full-time. You'll also have access to other big-company benefits such like short and long-term disability plans and a 401K. We also provide a stipend to create the ideal home office and support ongoing L&D.
Please note: we are seeing an uptick of fraudulent recruiting activity claiming to be associated with Higharc. All communication and outreach from our in-house team will come from an @higharc.com email address.
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 8,943 AI roles we're tracking, Research Engineer positions make up 3% of the market. At HighArc, 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 $252,000 based on 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
HighArc AI Hiring
HighArc has 1 open AI role right now. They're hiring across Research Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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