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About This Role
Noctrix Health is redefining the treatment of chronic neurological disorders with clinically validated therapeutic wearables. Our team of medical device specialists, neuroscientists, and consumer electronics engineers is dedicated to delivering prescription\-grade therapy with an outstanding user experience. We have pioneered the world's first drug\-free wearable therapy, clinically proven to alleviate symptoms in adults with drug\-resistant Restless Legs Syndrome (RLS). Be part of our mission to transform healthcare, improve lives, and drive meaningful change with Noctrix Health.
We are seeking a versatile and highly motivated Clinical Research Engineer to support research, development, testing, and data analysis initiatives within Noctrix Health's Clinical Research organization. This role sits at the intersection of engineering and clinical science, contributing to software and firmware development, physiological signal processing, clinical data analysis, device characterization, verification and validation, and clinical research activities.
The ideal candidate is a strong technical generalist who enjoys solving complex problems across multiple disciplines and can quickly adapt to new technical challenges. This individual will work closely with Clinical Research, Engineering, Quality, Regulatory Affairs, and Product teams to generate clinical insights, evaluate device performance, and support the development of next\-generation wearable medical technologies.
This position reports to the Chief Scientific Officer.
*This is a full\-time, hybrid position located in our Pleasanton, CA office, with a minimum of three days per week onsite.*
Responsibilities:
- Develop Python\-based tools, algorithms, analysis pipelines, and automation frameworks to improve research efficiency, engineering productivity, and data quality
- Analyze small\- and large\-scale clinical, physiological, and device datasets to identify trends, generate insights, and support product and clinical decision\-making
- Perform physiological signal processing, waveform analysis, and interpretation of biosensor and device\-generated data
- Develop and execute verification and validation protocols for software, firmware, algorithms, and medical device systems
- Contribute to the design, development, and testing of firmware components and embedded systems used within Noctrix medical devices
- Support device characterization, use\-case testing, performance evaluations, and root cause investigations
- Develop automated testing and data analysis workflows to improve the efficiency and repeatability of research and engineering activities
- Apply statistical methods to experimental and clinical datasets and interpret results in support of clinical research and product development
- Support algorithm and machine learning model development, evaluation, and testing where applicable
- Collaborate with cross\-functional teams to define technical requirements, design experiments, develop and test systems, and solve complex technical problems
- Support clinical research studies through data collection, analysis, technical troubleshooting, and interpretation of study results
- Prepare clear technical documentation, including research findings, technical specifications, test protocols, test reports, and engineering records
- Ensure technical and research activities are appropriately documented in accordance with applicable Quality System and regulatory requirements
- Partner closely with Clinical Affairs, Regulatory Affairs, Quality Assurance, Product Management, and Engineering to advance safe, effective, and patient\-centered solutions
Requirements:
- Bachelor's degree in Engineering, Biomedical Engineering, Electrical Engineering, Computer Science, or a related technical discipline with 5\+ years of relevant industry experience, or a Master's degree with 3\+ years of relevant experience
- Experience developing, testing, or supporting medical devices, wearable health technologies, or similar regulated products
- Experience working across multiple technical domains, including software development, data analysis, testing, and embedded systems
- Experience supporting research or product development activities from concept through commercialization within a Design Control environment
- Hands\-on experience developing and executing verification and validation testing within a regulated product development environment
- Experience analyzing physiological signals, biosensor data, device data, or similar time\-series datasets
- Strong proficiency in Python for data analysis, algorithm development, automation, or software development
- Experience with statistical analysis and interpretation of experimental and clinical datasets
- Understanding of signal processing and digital signal analysis methodologies
- Familiarity with algorithm and machine learning model development and testing methods
- Strong technical writing skills with experience producing protocols, reports, specifications, and other controlled documentation
- Strong analytical and troubleshooting skills with the ability to investigate and resolve complex technical problems
- Ability to independently manage multiple projects and priorities in a fast\-paced, cross\-functional environment
- Excellent communication skills with the ability to clearly communicate technical findings to both technical and non\-technical stakeholders
Preferred Qualifications:
- Experience supporting clinical research studies, clinical data analysis, or human subjects research
- Experience with C\+\+, SQL, Git, and modern software development workflows
- Experience developing automated test systems, data processing pipelines, or research tools
- Experience developing or evaluating algorithms and machine learning models using physiological or clinical data
- Experience with embedded firmware development and testing
- Strong understanding of software development principles and version control practices
- Familiarity with FDA Design Controls and medical device Quality Management Systems
- Knowledge of Good Clinical Practice (GCP), HIPAA, patient data privacy requirements, and healthcare data security best practices
- Experience working with wearable medical devices, neurostimulation technologies, biosensors, or other connected health products
Compensation:
- Base Pay: $130,000–$150,000 per year
- Annual Bonus Eligibility
Salary Context
This $130K-$150K range is below the median 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 Noctrix Health, 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 ($140K) sits 49% below the category median. Disclosed range: $130K to $150K.
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.
Noctrix Health AI Hiring
Noctrix Health has 1 open AI role right now. They're hiring across Research Engineer. Based in Pleasanton, CA, US. Compensation range: $150K - $150K.
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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