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Sr. Applied Research Engineer – Biomechanics – Burlington
What You’ll Do:
We are seeking a highly motivated and experienced scientist or engineer to join our Applied Research team and contribute to the advancement of next\-generation technologies within our Corneal Health (CH) product portfolio. This role will play a central part in driving early\-stage innovation, technology development, and translational research efforts aimed at expanding the capabilities and clinical impact of our ophthalmic medical device platforms.
The successful candidate will work at the intersection of computational modeling, experimental science, engineering, and clinical translation to develop new technologies and methodologies. A key focus of this role will be developing and validating computational and analytical models of corneal and tissue biomechanics, complemented by hands\-on imaging and elastography work that enables improved understanding and quantification of corneal structure and biomechanics.
The ideal candidate will bring deep expertise in tissue and corneal biomechanics and computational modeling, complemented by hands\-on experience with biomechanical imaging, elastography, signal processing, and algorithm design, with a strong interest in translating research concepts into robust, validated technologies.
How You’ll Contribute:
Contribute to the design and development of current and next\-generation ophthalmic medical devices, from early research through translational development.
- Develop, apply, and validate analytical and computational models of corneal and ocular tissue biomechanics, including finite element approaches, to characterize tissue behavior and therapeutic effects.
Use experimental and imaging\-derived data to estimate material properties, perform in\-verse and parameter\-estimation analyses, and validate model predictions.
- Support and perform biomechanical imaging and elastography measurements, including refinement of experimental setups used to quantify corneal biomechanical properties.
- Develop, optimize, and maintain robust modeling, processing, and post\-processing algorithms and software pipelines for biomedical imaging and biomechanical data.
- Collaborate with hardware, software, and systems engineering teams to integrate models, algorithms, and experimental methods into prototype and product\-level medical devices.
- Oversee data collection, preprocessing, model and algorithm development, validation, and performance characterization through rigorous experimental testing.
- Evaluate emerging modeling methods and measurement technologies to advance biomechanical characterization for ophthalmic applications.
- Communicate technical results through design reviews, internal reports, and technical documentation; contribute to publications, patents, or regulatory submissions as appropriate.
How You’ll Get There:
- PhD in Mechanical Engineering, Biomedical Engineering, Physics, or a related field, or an MS/Bachelor’s degree with equivalent industry experience.
- 5–8\+ years of relevant experience (3–5\+ years with an MS degree or 0–4\+ years with a PhD) in soft\-tissue biomechanics, computational modeling, experimental imaging, or quantitative analysis.
- Demonstrated experience developing and validating biomechanical models of soft tis\-sues, including constitutive modeling, finite element analysis, and parameter estimation from experimental data.
- Working knowledge of biomechanical imaging and elastography, including measurement setups, data acquisition, and measurement system validation.
- Hands\-on experience with finite element modeling tools; COMSOL Multiphysics experience is preferred.
\#GKOSUS
Generous. Innovative. Leadership\-driven. Family\-oriented. Socially responsible.
Founded in 1998, Glaukos Corporation is an ophthalmic pharmaceutical and medical technology company focused on developing and commercializing novel therapies for the treatment of glaucoma, corneal disorders, and retinal diseases.
Our mission at Glaukos is to truly transform vision by pioneering novel, dropless therapies that can meaningfully advance the standard of care and improve the lives of patients suffering from chronic, sight\-threatening eye diseases.
Innovation is at the core of everything we do, and we are resolute in our commitment to challenge conventional thinking with new treatment alternatives that are supported by real science, robust clinical evidence, and an unrelenting focus on patients.
Our constant pursuit of game\-changing technologies that disrupt legacy treatment paradigms is encapsulated in the Glaukos mantra “We’ll Go First,” which articulates our willingness to take chances, our determination to forge new ground, and our commitment to continuous improvement in all that we do.
Our company completed an initial public offering in June of 2015, and our shares are traded on the New York Stock Exchange under the ticker symbol “GKOS”. Our global headquarters is in Aliso Viejo, California with additional locations in San Clemente, California, and Burlington, Massachusetts.
Glaukos Corporation is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex including sexual orientation and gender identity, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law.
All offers of employment are contingent upon the successful completion of a background check, including successfully passing a drug screen, based on the position and local regulations.
Salary Context
This $118K-$148K range is below the median for Research Engineer roles in our dataset (median: $188K across 44 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 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Glaukos, 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 in Demand for This Role
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 $280,000 based on 147 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($133K) sits 52% below the category median. Disclosed range: $118K to $148K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Glaukos AI Hiring
Glaukos has 2 open AI roles right now. They're hiring across Research Engineer. Based in Burlington, MA, US. Compensation range: $148K - $148K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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