Summer 2026
Software Engineering Intern
Posted on 10/31/2025
Cadence Solutions
201-500 employees
Remote patient monitoring for chronic illnesses
Compensation Overview
$55/hr
Remote in USA
Remote
 Software Engineering (1) |
|---|
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- Currently pursuing a Bachelor's or Master's degree in Computer Science (or equivalent), with an expected graduation date in December 2026 or May 2027.
- Prior internship or equivalent experience in software engineering.
- Strong problem-solving skills and eagerness to learn new technologies.
- A product-first mindset — you care about shipping quickly, but also about the real-world impact of your code in improving patient outcomes.
- Excellent communication and collaboration skills.
- Collaborate with engineers, product managers, and designers to ship features from concept to production.
- Contribute to services that enable clinicians to deliver care nationwide and improve patient experience.
- Write clean, maintainable, and testable code.
- Apply best practices for software development in a cloud-based, high-scale environment.
Cadence Solutions provides remote patient monitoring and chronic disease management services to healthcare providers to support patients with chronic conditions like hypertension and type 2 diabetes. Its technology collects patient health data outside the clinic and pairs it with a multidisciplinary clinical care team to monitor and manage care. The system helps clinicians track status, support guideline-directed therapies, and coordinate care while freeing in-person staff for acute cases. The approach is centered on a provider-facing platform that demonstrates improved health outcomes and financial ROI, including lower care costs, higher adherence to therapies, and fewer emergency department visits. Cadence aims to improve patient health while saving providers time and resources by reducing unnecessary in-person visits and enabling more efficient patient management.
Company Size
201-500
Company Stage
Series C
Total Funding
$241M
Headquarters
New York City, New York
Founded
2020
Get referred to Cadence Solutions
See people who can refer or advise you
Simplify's Take
What believers are saying
- Cadence raised $100 million on July 17, 2026, after tripling ARR in 2025.
- CMS accepted Cadence into ACCESS, opening payer-aligned expansion across Traditional Medicare.
- Peer-reviewed data show 27% fewer admissions and $1,302 lower annual cost per patient.
What critics are saying
- TEMPO is enforcement discretion only; any FDA rejection would stall HypertensionOS commercialization.
- ACCESS payment ties reimbursement to outcomes; weak Medicare results hit revenue starting July 2026.
- Big health systems can insource RPM workflows, compressing Cadence's moat by 2027.
What makes Cadence Solutions unique
- HypertensionOS entered FDA TEMPO on August 13, 2026, under clinician supervision.
- Cadence integrated deeply with 20-plus health systems, including Duke Health and Texas Health Resources.
- Its supervised AI agents resolve 55% of vitals alerts without human adjustment.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
401(k) Company Match
401(k) Retirement Plan
Paid Parental Leave
Competitive PTO
Home Office Stipend
Charitable Donation Match program
Growth & Insights and Company News
6 month growth
↑ 1%1 year growth
↑ 2%2 year growth
↑ 0%FDA opens GenAI consultation while ISO 13485 timelines tighten. Sherif Elkhadem 19 August 2026 FDA just opened public consultation on generative AI in medical devices, the same week Greenlight Guru published updated ISO 13485 certification timelines showing the process now routinely exceeds 18 months. These are not unrelated events. As regulatory authorities grapple with rapid AI innovation, they are simultaneously tightening the baseline quality infrastructure manufacturers must demonstrate before any device reaches market. For regulatory affairs and quality teams, this dual signal reveals where the regulatory centre of gravity is moving. The FDA consultation, announced through a Request for Information, acknowledges that generative AI-enabled devices introduce unique risks compared to traditional software or even deterministic AI. The agency is seeking feedback on risk assessment, validation, transparency and post-market monitoring for devices that can generate novel outputs not explicitly programmed. Meanwhile, ISO 13485:2016 certification, the recognised foundation for medical device quality systems globally, now requires a timeline most startups dramatically underestimate. The intersection of these two developments is not coincidental. It is structural. Why FDA is asking now, and what the questions reveal. FDA's Request for Information on generative AI devices is notable less for what it asks than for when it asks. The agency already has a framework for AI and machine learning devices, updated in 2021 with the concept of predetermined change control plans. It already cleared multiple AI diagnostic tools under existing pathways. But generative AI presents a different challenge. These devices do not simply classify or detect. They produce content, recommendations or outputs that may not exist in training data. That fundamentally changes the risk profile. The consultation signals FDA is building a regulatory approach before widespread submissions arrive, not after. It follows a similar pattern to the MHRA's ambient voice technology guidance, where the regulator published classification criteria for a technology category at the point of clinical adoption, not market saturation. For manufacturers developing generative AI devices, this is a narrow window to shape regulatory expectations before they solidify. The questions FDA is asking, particularly around validation and transparency, suggest the agency is looking for evidence frameworks that extend well beyond traditional V&V protocols. Critically, the consultation arrives as FDA names participants in its TEMPO digital health pilot, a Medicare-linked pathway that exempts certain devices from some premarket requirements in exchange for real-world evidence collection. Cadence, the second participant announced this week, develops AI-enabled software for hypertension management. The juxtaposition is deliberate. FDA is signalling parallel tracks: streamlined pathways for devices with established risk profiles, and heightened scrutiny for generative AI where risk models are still forming. ISO 13485 timelines stretch as certification bodies tighten scrutiny. Greenlight Guru's updated ISO 13485 certification timeline reveals a process that now routinely takes 18 months or more from gap analysis to certificate issuance. This is not a bureaucratic delay. It reflects certification bodies conducting deeper audits, particularly around risk management, design control traceability and post-market surveillance integration. For startups approaching their first certification, the timeline represents a funding and planning challenge that compounds if not anticipated early. The timeline is driven by several structural factors. Notified bodies and certification bodies are under heightened regulatory scrutiny following MDR implementation in Europe and ongoing FDA collaboration through mutual recognition agreements. Auditors are spending more time on evidence architecture, particularly the linkage between risk assessments, design inputs, verification outputs and post-market data. They are also requiring more robust supplier management documentation, especially for SaMD teams relying on third-party components or cloud infrastructure. As LLM-enabled SaMD development becomes common, supplier qualification is a growing audit flashpoint. The extended timeline has knock-on effects for regulatory submissions. Many jurisdictions, including Canada under MDSAP and Australia under TGA, accept ISO 13485 certification as part of the conformity assessment route. Without the certificate in hand, market entry timelines stretch further. For manufacturers targeting multiple markets simultaneously, as RevealDx recently demonstrated with its triple-clearance AI lung nodule tool, ISO 13485 certification becomes the long pole in the schedule. Where generative AI and quality systems intersect. The FDA consultation and ISO 13485 timeline pressure converge on a single compliance question: how do you validate and maintain a quality system for a device that produces outputs you cannot fully enumerate at the point of design freeze? Traditional design control processes assume a defined specification. You design to it, verify against it, validate in intended use, then lock it. Generative AI devices challenge every step of that sequence. ISO 13485 requires manufacturers to define acceptance criteria for design outputs and maintain traceability between design inputs, risk controls and verification results. For a generative AI device, those acceptance criteria must account for stochastic behaviour, edge cases that emerge post-deployment and model drift over time. That means quality systems must integrate continuous monitoring, version control for model retraining and a change control process that can distinguish between acceptable variation and a change requiring regulatory notification. This is not a theoretical challenge. Aidoc's generative AI radiology tool, submitted to FDA earlier this year, required a validation strategy that extended beyond traditional sensitivity and specificity metrics. The company had to demonstrate how the system's generative outputs would be bounded, how clinical users would be alerted to uncertainty and how post-market data would feed back into model governance. That level of integration between algorithm development and QMS infrastructure is not standard in most ISO 13485 systems today. What this means for your team. If your device pipeline includes any form of generative AI, the FDA consultation window is a strategic opportunity. The Request for Information is open for comment, and early input has historically influenced how FDA structures subsequent guidance. This is the moment to surface your validation challenges, evidence gaps and operational constraints before the agency formalises its expectations. Once guidance is published, the compliance baseline is set. For teams approaching ISO 13485 certification, the 18-month timeline is no longer an outlier. It is the median. That means certification planning must begin well before first submission, ideally during design and development phases when QMS integration is least disruptive. Waiting until a device is feature-complete to build a quality system around it is a recipe for non-conformities, rework and timeline slippage. Certification bodies are increasingly focused on whether the QMS was used to develop the device, not simply documented after the fact. The intersection of these timelines matters most for startups targeting multiple markets. If your strategy includes FDA clearance, CE Mark under MDR and markets requiring ISO 13485 certification, the certification timeline becomes your critical path. You cannot compress it meaningfully once you start. That makes early investment in QMS infrastructure a funding and operational priority, not a compliance overhead to defer. Key takeaways. * FDA's generative AI consultation signals the agency is building regulatory expectations before widespread submissions arrive, creating a narrow window for manufacturers to shape guidance through public comment. * ISO 13485 certification now routinely exceeds 18 months, driven by deeper audits on risk management, design control traceability and post-market surveillance integration. * Generative AI devices challenge traditional design control assumptions, requiring quality systems that integrate continuous monitoring, version control and change management from the outset. * For multi-market strategies, ISO 13485 certification is often the long pole in the timeline, making early QMS investment critical for funding and submission planning. * Certification bodies are scrutinising whether the QMS was used to develop the device or simply documented retrospectively, shifting audit focus from paperwork to evidence architecture. The regulatory environment for AI-enabled devices is moving from reactive to anticipatory. FDA is consulting on generative AI before the submission wave peaks. Certification bodies are auditing quality systems with an eye to post-market resilience, not just premarket compliance. For manufacturers, this shift demands earlier, deeper integration between product development and regulatory infrastructure. The teams that recognise this are building quality systems and evidence architectures in parallel with their algorithms, not sequentially. That is the competitive signal in this week's announcements.
Cadence announced that its HypertensionOS software has been selected as the second participant in the FDA's Technology-Enabled Meaningful Patient Outcomes (TEMPO) pilot programme. The prescription software uses AI to support clinician-supervised medication management for patients with Stage 2 hypertension. HypertensionOS helps carry out routine steps in medication initiation and titration within predefined safety checks. It is designed for use by licensed healthcare professionals with prescriptive authority. Cadence currently partners with over 20 health systems and treats more than 100,000 active patients with chronic disease. Peer-reviewed research shows the company's platform drives a 70% relative increase in blood-pressure control among hypertension patients, alongside reductions in hospitalizations and total cost of care. Participation in TEMPO does not constitute FDA approval or clearance of HypertensionOS.
Cadence secures $100 million to scale ai-powered chronic care. Published. July 17, 2026 Cadence has raised $100 million to expand an AI-powered chronic care platform that now treats more than 100,000 patients and, according to the company, saves Medicare roughly $2.7 million each week. Spark Capital led the Series C, with participation from Thrive Capital, General Catalyst, Coatue, B Capital, Corewell Health Ventures, Memorial Hermann, and Duke Health. Cadence plans to use the funding to add health system partners, develop its AI agents, and expand its work in value-based care. The company now works with more than 20 health systems and announced new affiliations with Duke Health and Texas Health Resources alongside the financing. Integrated into electronic medical records and clinical workflows, Cadence's supervised AI agents monitor patient vitals, support medication adjustments, and provide personalized lifestyle coaching between office visits. The company tripled its annual recurring revenue in 2025. Cadence says its platform has reduced hospital admissions by 27% and lowered annual healthcare costs by $1,302 per patient across peer-reviewed studies and real-world deployments. Its AI agents currently resolve 55% of incoming vitals alerts without human adjustment, while maintaining a median response time of 3.5 minutes. "We built Cadence to solve the clinical labor constraint at the heart of the chronic disease crisis," said Founder and CEO Chris Altchek. He said the new funding will help build the infrastructure needed to expand from 100,000 patients to millions. Spark Capital Partner and new Cadence Board Member Will Reed said the company had already demonstrated clinical outcomes, earned the trust of major health systems, and shown that AI could be deployed safely inside care delivery. Cadence's broader goal is to automate routine chronic care work so clinicians can focus on decisions that require direct medical judgment.
AI health startup hits $1.2 billion valuation. June 24, 2026 Cadence, a health technology company using artificial intelligence to monitor seniors with chronic conditions, has reached a $1.2 billion valuation. The milestone signals growing investor confidence in digital tools that promise earlier intervention, steadier treatment plans, and fewer emergency visits for older adults managing complex health needs. The company's model focuses on continuously tracking patient health data and alerting care teams to changes that might require action. Investors are betting that this approach can improve outcomes and reduce costs for health systems and insurers facing aging populations and rising rates of long-term disease. "Cadence reached a $1.2 billion valuation by using AI to track seniors' chronic health conditions and improve their care." Background: aging populations and chronic disease. As more people live longer with conditions like heart failure, diabetes, and COPD, the demand for monitoring beyond clinic walls has surged. Traditional care often relies on periodic appointments that can miss early warning signs. Remote monitoring aims to fill that gap by using connected devices and data analysis to flag trouble sooner. Over the past decade, health systems have tested ways to manage chronic disease at home. Programs that combine regular check-ins, adherence support, and timely medication adjustments have shown promise in reducing hospitalizations. AI promises to scale these practices by sorting large volumes of data and focusing attention on patients who most need it. How ai-driven monitoring could work. AI systems in chronic care typically analyze signals such as vital signs, symptom reports, and medication patterns. When the data suggests a worsening trend, the system can alert clinicians or care coordinators. The goal is simple: act before a small issue becomes a serious event. For seniors, consistency matters. Many live with multiple conditions and complex treatment plans. Automated monitoring may help by providing early guidance on when to adjust medications, schedule a visit, or arrange home support. In turn, families and caregivers can gain more visibility into day-to-day health changes. Promise and pressure for results. The valuation places pressure on the company to prove it can deliver better outcomes at scale. Health plans and hospitals will look for clear evidence of fewer hospitalizations, improved control of chronic conditions, and high patient satisfaction. They will also want proof that the service can integrate with existing clinical workflows without adding administrative burden. Cost is another test. To sustain adoption, the service must show that savings from avoided emergency care and admissions outweigh program costs. Payers are receptive to solutions that bend the cost curve while maintaining quality, but require rigorous data before committing to broad rollouts. Data privacy, equity, and clinical oversight. Data privacy and security remain central concerns. Seniors and caregivers will expect clear consent practices, strict controls on data use, and protection against unauthorized access. Clinical oversight is equally important. AI can triage information, but licensed providers must make care decisions, ensure safety, and document rationale. Equity also matters. Not every senior has reliable internet, smartphones, or comfort with technology. Programs must account for language, accessibility, and device support to avoid leaving patients behind. Training, simple user interfaces, and alternative outreach methods can help close these gaps. Signals for the digital health market. The funding climate for digital health has cooled in some areas, but tools tied to measurable outcomes continue to draw interest. Chronic care, which drives a large share of health spending, remains a focal point. A high valuation suggests investors see near-term paths to revenue through partnerships with health systems and insurers. * Evidence will be key: reductions in acute events and improved quality metrics. * Integration with electronic records can speed clinician adoption. * Clear reimbursement pathways will support wider use. What to watch next. Analysts will watch for peer-reviewed studies, large health system partnerships, and performance across diverse patient groups. Regulatory engagement and transparent reporting on outcomes will also shape confidence. If results hold, AI-guided monitoring could become a standard layer of care for seniors with chronic disease. For now, the valuation marks a vote of confidence in a model that aims to keep patients stable at home and give clinicians timely insights. The next phase will test whether promise translates into consistent, equitable, and proven results across real-world settings.
Cadence raises $100M to expand ai-powered chronic care platform. 23 June 2026 Key Takeaways * Cadence raised $100 million in Series C funding led by Spark Capital. * The company now supports more than 100,000 patients through over 20 health system partnerships. * New funding will accelerate AI development, health system expansion, and value-based care initiatives. Cadence raises fresh capital to scale clinical AI operations. Cadence closed a $100 million Series C funding round to expand its AI-driven chronic care solution nationwide. Spark Capital led the investment, joined by Thrive, Coatue, General Catalyst, B Capital and major investors today. The infusion came after rapid expansion that saw Cadence triple annualized recurring revenue and forge new partnerships within the year. Cadence will expand AI capabilities, strengthen health partnerships, and extend services into new care settings. The company's founding stemmed from efforts to manage chronic disease, merging technology and clinical care across gaps between appointments. Cadence uses predictive care to improve outcomes while helping physicians reduce healthcare costs. AI platform gains momentum across major health systems. In addition to the investment round, the announcement marks a partnership between Cadence and Duke Health, and Texas Health Resources. The additions bring the company's total partnerships to 20-plus of the nation's leading health systems. Cadence's systems manage well over 100,000 active patients. They slot into EMRs, and through their tech, supervisors of a supervised AI system track patient activity in near real-time for medical threats, then alert medical teams to address the problem sooner. Chronic disease is a main area of concern for the company, as chronic health conditions, including heart disease, diabetes, and high blood pressure, are large drivers of the health economy and "often result in preventable hospitalizations and complications," based on Cadence. Chris Altchek, CEO of Cadence. The firm was established, "to deal with chronic care's looming shortage," and "uses artificial intelligence to reduce and streamline chronic care monitoring - with physicians continuing to manage patient care plans and treatment decisions." Clinical results support expansion strategy. Clinical results from AI-powered care models demonstrate improved patient outcomes, faster response times, reduced hospital admissions, and significant cost savings, strengthening confidence in scalable digital healthcare systems. Source: Created by Ventureburn. Cadence has published several clinical studies highlighting the effectiveness of its care model. The company reported significant improvements in treatment adherence among heart failure patients and stronger blood pressure control for individuals managing hypertension. The platform also delivers rapid responses to patient alerts. Cadence stated that median response times for incoming vital sign alerts have fallen to just 3.5 minutes. More than half of alerts are resolved appropriately without requiring direct clinician intervention. The company estimates its model reduces hospital admissions by 27%. It also reports annual healthcare savings of approximately $1,302 per patient. According to Cadence, the programme generates more than three dollars in Medicare savings for every dollar spent on care delivery. These outcomes have helped strengthen investor confidence. Spark Capital partner Will Reed noted that Cadence has demonstrated both clinical effectiveness and economic value at scale. He added that few healthcare AI companies have achieved similar levels of adoption and peer-reviewed validation. Growing demand for ai-driven chronic disease management. Growing pressures from high costs and shortages of clinicians have been a long and consistent story for the health care sector. Chronic disease drives healthcare spending, increasing demand for efficient technologies without compromising care quality. Cadence's Clinical Intelligence platform is designed to meet that need by generating real-time data with always-on patient monitoring between appointments that leads to faster interventions and personalized treatment plans. Health systems, recognizing the constraints that their care teams are facing, have also been actively seeking to adopt AI tools that complement rather than replace the clinician, something the company touts as key to its relationships with hospital and provider systems. With fresh capital now secured, Cadence plans to continue expanding its national footprint. The company also intends to enhance its AI agents and deepen its involvement in value-based care programmes designed to reward better patient outcomes and lower healthcare costs. To stay updated on crypto venture capital funding and market trends, visit its venture capital news section for more insights. Clinton Nwachukwu is a crypto and finance writer with an MBA in Artificial Intelligence and 6+ years of experience creating content for leading global brands. He turns complex topics into clear, actionable insights for readers worldwide. Disclaimer VentureBurn is a media platform covering the latest in cryptocurrency, artificial intelligence, venture capital, and the startup ecosystem. Opinions expressed on VentureBurn are for informational purposes only and do not constitute investment advice. 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