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About This Role
About Subsense
Subsense is a deep\-tech company developing the world’s first non\-surgical, bidirectional brain\-computer interface powered by plasmonic and magnetoelectric nanoparticles. Our mission is to unlock direct communication between the human brain and AI \- starting with medical applications such as stroke recovery and moving toward cognitive enhancement for healthy users. Headquartered in Palo Alto, Subsense brings together leading scientists and engineers to redefine the future of human–machine interaction.### The Opportunity
We are seeking a highly hands\-on Research Engineer to lead and execute multidisciplinary experiments at the intersection of physics, engineering, nanotechnology, and biology.
This role is centered on experimental development. You will design experiments, build and operate laboratory systems, coordinate complex studies across multiple teams, and translate experimental results into actionable engineering improvements. You will spend a significant portion of your time in the lab running experiments, troubleshooting systems, collecting data, and working closely with scientists and engineers to advance our technology platform.
The ideal candidate enjoys moving quickly between theory and practice, thrives in a fast\-paced startup environment, and is excited about solving difficult problems through experimentation.
### Key Responsibilities
- Design, plan, and execute experiments to evaluate new technologies, devices, materials, and biological systems.
- Lead day\-to\-day laboratory activities, including setup, operation, troubleshooting, and optimization of experimental platforms.
- Coordinate multidisciplinary experiments involving hardware, software, optics, electromagnetics, nanomaterials, and biological systems.
- Work closely with scientists and engineers across departments to develop experimental plans, protocols, and success metrics.
- Build, modify, and maintain research platforms, prototypes, and test systems.
- Develop experimental procedures and ensure repeatable, high\-quality data collection.
- Analyze experimental data and identify trends, opportunities, and next\-step investigations.
- Drive rapid iteration cycles by translating experimental findings into design improvements.
- Support biological experiments involving cell cultures, neural systems, and related laboratory workflows.
- Collaborate with software and data teams to automate experiments, data acquisition, and analysis pipelines.
- Document experimental methods, results, and technical recommendations.
- Present findings to internal teams and contribute to technical strategy discussions.
### What You'll Bring
- MS or PhD in Physics, Electrical Engineering, Mechanical Engineering, Bioengineering, Neuroscience, Applied Physics, or a related field.
- 0–5 years of industry, academic, or research laboratory experience.
- Strong experimental mindset with demonstrated experience designing and executing complex experiments.
- Experience operating laboratory equipment and developing experimental workflows.
- Ability to troubleshoot multidisciplinary systems spanning hardware, software, and instrumentation.
- Strong quantitative and analytical skills, including data analysis in Python, MATLAB, or similar environments.
- Experience with scientific instrumentation, sensors, optics, electromagnetics, control systems, or related technologies.
- Comfortable working with uncertainty and rapidly evolving research objectives.
- Excellent communication and collaboration skills in cross\-functional environments.
- Startup mindset—curious, adaptable, hands\-on, and highly execution\-oriented.
Preferred Qualifications
- Experience with neurotechnology, neuroscience, biomedical devices, or bioelectrical systems.
- Experience working with cell culture, biological assays, or laboratory research environments.
- Familiarity with magnetic systems, electromagnetics, optics, photonics, or nanoparticle\-based technologies.
- Experience developing automated experimental platforms and data acquisition systems.
- Experience with COMSOL, MATLAB, Python, LabVIEW, or similar scientific computing tools.
Why This Role Is Different
------------------------------
This is not a desk\-based design role. You will be in the lab daily, working directly with experiments, instrumentation, and multidisciplinary teams to advance breakthrough neurotechnology. Success in this role comes from curiosity, creativity, rigorous experimentation, and the ability to turn complex experimental observations into engineering progress.
*Subsense is an equal\-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $120K-$140K range is in the lower quartile for Research Engineer roles in our dataset (median: $207K across 63 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 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At subsense, 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 $272,100 based on 227 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($130K) sits 52% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
subsense AI Hiring
subsense has 1 open AI role right now. They're hiring across Research Engineer. Based in Mountain View, CA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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