Improving Scalability in Engineering Research Projects

A junior reached out to me last week. One of our APIs was collapsing under 150 requests per second. Yes — only 150. He had tried everything: * Added an in-memory cache * Scaled the K8s pods * Increased CPU and memory Nothing worked. The API still couldn’t scale beyond 150 RPS. Latency? Upwards of 1 minute. 🤯 Brain = Blown. So I rolled up my sleeves and started digging; studied the code, the query patterns, and the call graphs. Turns out, the problem wasn’t hardware. It was design. It was a bulk API processing 70 requests per call. For every request: 1. Making multiple synchronous downstream calls 2. Hitting the DB repeatedly for the same data for every request 3. Using local caches (different for each of 15 pods!) So instead of adding more pods, we redesigned the flow: 1. Reduced 350 DB calls → 5 DB calls 2. Built a common context object shared across all requests 3. Shifted reads to dedicated read replicas 4. Moved from in-memory to Redis cache (shared across pods) Results: 1. 20× higher throughput — 3K QPS 2. 60× lower latency (~60s → 0.8s) 3. 50% lower infra cost (fewer pods, better design) The insight? 1. Most scalability issues aren’t infrastructure limits; they’re architectural inefficiencies disguised as capacity problems. 2. Scaling isn’t about throwing hardware at the problem. It’s about tightening data paths, minimizing redundancy, and respecting latency budgets. Before you spin up the next node, ask yourself: Is my architecture optimized enough to earn that node?

At Recombee, in collaboration with Czech Technical University in Prague and Charles University, researchers have tackled a core challenge in recommender systems: embedding scalability. Their latest research introduces CompresSAE, an innovative embedding compression method leveraging sparse autoencoders to efficiently manage massive embedding tables. Dense embeddings are powerful, yet as their size increases, so do costs-storage, latency, and computational demands escalate significantly. To address this, CompresSAE projects dense embeddings into high-dimensional sparse spaces. Instead of traditional linear approaches or retraining intensive methods, this model employs a sparsification strategy that maintains representation quality while drastically cutting down memory usage. Under the hood, CompresSAE compresses embeddings by activating only a small subset of dimensions (e.g., 32 out of 4096), effectively capturing the directional information crucial for similarity retrieval tasks. This sparsity not only reduces storage requirements dramatically but also enhances retrieval efficiency through rapid cosine similarity computations. In online experiments with catalogs containing over 100 million items, CompresSAE achieved nearly the same click-through rate (CTR) performance as models eight times larger, outperforming comparable compression methods. Crucially, this approach doesn't require retraining the backbone models, making it highly practical for large-scale deployments. This highlights the future potential of sparse embeddings in enhancing the efficiency and scalability of next-generation recommender systems. 

Why Processes That Work in the Lab Often 𝐅𝐚𝐢𝐥 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 One of the most persistent challenges in chemical engineering is the transition from lab-scale processes to full commercial production. A system that performs flawlessly in a 5-liter reactor may behave unpredictably in a 5,000-liter vessel. This shift—from controlled experiments to industrial volumes—brings significant risks, including cost overruns, quality issues, and safety concerns. 𝐊𝐞𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐒𝐜𝐚𝐥𝐞-𝐔𝐩: 🔹Heat Transfer Limitations Larger reactors exhibit a lower surface area-to-volume ratio, which can hinder efficient heat removal and increase the risk of hotspots. 🔹Non-Uniform Mixing What mixes well at lab scale may lead to distinct mixing zones at scale, adversely affecting reaction rates and outcomes. 🔹Residence Time Distribution Flow behaviour changes with scale, impacting conversion rates and product consistency. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡-𝐁𝐚𝐜𝐤𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐨 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐒𝐜𝐚𝐥𝐞-𝐔𝐩 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬: 🔹Advanced CFD Modelling Computational fluid dynamics simulations can predict flow, temperature, and mixing behaviour, allowing engineers to proactively address potential issues before they arise in production. 🔹Rethinking Scale-Up Rules Traditional guidelines (such as constant tip speed or power per volume) may not apply effectively for complex reactions and should be adapted on a case-by-case basis. 🔹Targeted Instrumentation Strategically placing sensors at CFD-identified critical points can significantly enhance real-time control and process stability. Chemical process scale-up is rarely a linear task. However, with the right combination of modeling, monitoring, and engineering judgment, it can become a manageable challenge. What obstacles have you encountered when scaling complex reactions? I’d love to hear your experiences! #ChemicalScaleUp #ProcessEngineering #PolymerManufacturing #Ingenero

“Democratized research” doesn’t work. Lots of companies out there are trying to empower non-researchers to conduct research themselves. The idea is well intentioned: more research gets done, product teams build empathy, and researchers get time back for more strategic projects. The reality? Democratized research programs usually end up creating a bunch of overhead and process that nobody wants. There’s an illusion of speed, but too often teams churn out poor quality research that doesn’t move the needle. A new model seems to be emerging. Consider these examples from Learners Research Week: ➡️ Kaleb Loosbrock (Instacart) routinely uses custom GPTs in his research workflow, cutting time spent on tasks like transcript synthesis by 80% ➡️ Kevin Newton & Marieke McCloskey (LinkedIn) shared that 94% of UX researchers at LinkedIn now use AI in their process on a weekly basis ➡️ Jane Justice Leibrock (Anthropic) expects “we will move into a future of true end-to-end workflow automation where we will be able to set in motion repeatable processes for research” These teams aren’t democratizing research. They’re scaling it. The difference? The research is driven by experts, not offloaded to non-experts. Don’t get me wrong—product teams should be talking to customers as much as they can. But they shouldn’t be expected to master research techniques that take years to develop. Scaled research doesn’t necessarily mean increasing the volume of research output. It means automating all the repeatable processes behind the scenes to consistently deliver trusted results: robust recruitment, screening, questioning techniques, quality assurance, open-ended analysis, searchable repositories, and more, orchestrated into a system that everyone benefits from. Done right, this approach leads to less time spent on process—and more time actually listening to customers. For all the researchers out there: how are you thinking about scaling research on your team? What’s working well (or not)?

Quantum Scaling Recipe: ARQUIN Provides Framework for Simulating Distributed Quantum Computing Systems Key Insights: • Researchers from 14 institutions collaborated under the Co-design Center for Quantum Advantage (C2QA) to develop ARQUIN, a framework for simulating large-scale distributed quantum computers across different layers. • The ARQUIN framework was created to address the “challenge of scale”—one of the biggest hurdles in building practical, large-scale quantum computers. • The results of this research were published in the ACM Transactions on Quantum Computing, marking a significant step forward in quantum computing scalability research. The Multi-Node Quantum System Approach: • The research, led by Michael DeMarco from Brookhaven National Laboratory and MIT, draws inspiration from classical computing strategies that combine multiple computing nodes into a single unified framework. • In theory, distributing quantum computations across multiple interconnected nodes can enable the scaling of quantum computers beyond the physical constraints of single-chip architectures. • However, superconducting quantum systems face a unique challenge: qubits must remain at extremely low temperatures, typically achieved using dilution refrigerators. The Cryogenic Scaling Challenge: • Dilution refrigerators are currently limited in size and capacity, making it difficult to scale a quantum chip beyond certain physical dimensions. • The ARQUIN framework introduces a strategy to simulate and optimize distributed quantum systems, allowing quantum processors located in separate cryogenic environments to interact effectively. • This simulation framework models how quantum information flows between nodes, ensuring coherence and minimizing errors during inter-node communication. Implications of ARQUIN: • Scalability: ARQUIN offers a roadmap for scaling quantum systems by distributing computations across multiple quantum nodes while preserving quantum coherence. • Optimized Resource Allocation: The framework helps determine the optimal allocation of qubits and operations across multiple interconnected systems. • Improved Error Management: Distributed systems modeled by ARQUIN can better manage and mitigate errors, a critical requirement for fault-tolerant quantum computing. Future Outlook: • ARQUIN provides a simulation-based foundation for designing and testing large-scale distributed quantum systems before they are physically built. • This framework lays the groundwork for next-generation modular quantum architectures, where interconnected nodes collaborate seamlessly to solve complex problems. • Future research will likely focus on enhancing inter-node quantum communication protocols and refining the ARQUIN models to handle larger and more complex quantum systems.

Flexible pressure sensors are everywhere in prototypes. But scaling them to real-world production? That’s where most projects fail. In our work designing pressure mapping systems for robotic end effectors, we faced two challenges at once: → Build a high-fidelity, flexible sensor array. → Make it manufacturable beyond the lab bench. Here’s what it took: → Material System Selection. Why? We couldn’t just pick the softest or thinnest FSR materials. We needed materials that could survive lamination, mechanical cycling, and environmental stress without losing responsiveness. → Matrixing Without Crosstalk. Why? In a grid of distributed sensors, each node needs to be individually addressable without electrical interference bleeding across rows and columns. We engineered trace geometries and insulative layers to keep signals clean — even under flex and inflation. → Layered Durability. Why? Flexibility often sacrifices lifespan. We designed stackups that maintained elasticity while protecting conductive layers from mechanical fatigue and delamination. → Manufacturing Alignment: Why? Prototyping with hand-aligned layers is easy. Scaling requires layers to be aligned mechanically or laser-cut to tight tolerances, without introducing shifts that ruin sensor performance. It’s not enough to build a working prototype anymore. If you want to move from a concept to something scalable, you have to engineer for: → Mechanical reliability → Electrical integrity → Production repeatability

Despite the value of randomized experiments, many do not scale. New work shows how to introduce “Option C thinking” into experiments to raise the odds they can scale. The “voltage drop” - thanks to innovative work by John List ("The Voltage Effect") - between small-scale efficacy and population-level implementation is now recognized as one of the most significant barriers to translating experimental results into durable policy change. The problem lies not in the experiments themselves, but in the assumptions we make about generalizability. Experimental trials are typically optimized for internal validity, e.g. tight controls, well-defined populations, and narrow treatments. But when interventions are scaled, they encounter heterogeneity in populations, institutions, and implementation fidelity. As a result, between 50% and 90% of initial effect sizes shrink. To address this, a new approach known as “Option C thinking” proposes a pivot in research design. Rather than start with proof-of-concept and only later test for scale, researchers are encouraged to build considerations of scalability into the discovery process itself. This reframes experimental research as part of a broader strategy to identify interventions that are not just effective, but scale ready. The core question becomes: what information do we need at the outset to predict large-scale success? In this paper, Fatchen, Pagnotta, and John List argue that AI can augment Option C thinking by helping researchers simulate the conditions of scale from the very beginning, giving a case study from the Chicago Heights Early Childhood Center (CHECC) - a pioneering research-preschool partnership launched in 2010. Designed both as a site of educational delivery and as a platform for studying interventions, CHECC offers a rare environment for structured experimentation with built-in considerations for generalizability. The authors show that AI tools can generate a wide range of plausible Option C ideas when prompted appropriately. These ideas include modifications to program structure, delivery mechanisms, implementation logistics, and population targeting that would be difficult to anticipate through traditional discovery processes alone. Rather than replacing researcher judgment, AI enhances it by simulating broader conditions, anticipating constraints, and identifying scalable permutations that maintain fidelity to the core intervention. When embedded early in the research pipeline, such tools can help researchers identify which interventions are worth taking to scale, and what features will need to adapt in order to preserve effectiveness at higher levels of deployment. #ExperimentalEconomics #ScalingPolicy #OptionC #AIforPolicy #EarlyEducation

The hardest part of scaling a scientific platform isn’t the tech. It’s translating scientific reasoning into engineering specs. Pipelines and cloud systems can scale - that’s what they’re built for. What doesn’t scale as easily is the implicit knowledge scientists use to make decisions: - Which samples to exclude as outliers - What cutoff to apply for significance - How to balance sensitivity vs specificity for a given study When that judgment lives only in a scientist’s head (or in a half-documented ELN), it can’t be engineered. And if it can’t be engineered, it can’t scale. The real challenge, and the real opportunity, is capturing that reasoning in a way systems can enforce and teams can share. That’s where reproducibility pays off. Not just in code, but in design: making invisible judgment calls explicit, so they can travel across teams and across time.

Most research teams build roadmaps for projects—what studies we’re running, which teams need insights, and how we prioritize incoming requests. But what often gets left out is a roadmap for research operations. And that’s a problem. Because without a plan for ops work, things like updating repositories, improving workflows, and scaling research fall to the bottom of the priority list. So how do you make space for it? One way is to dedicate a percentage of your team’s capacity to research operations. Here’s a simple breakdown: 40% → Research Projects: Intakes, performing research, analysing data etc. 40% → Research Strategy: Sharing insights, strategic workshops, influencing roadmaps 20% → ResearchOps: Maintaining the repository, updating templates, improving processes, testing AI, etc. That last 20% is crucial. It’s what keeps research scalable, efficient, and impactful in the long run. For example, in our roadmap, we plan: • Quarterly time to clean and update the insights repository • Dedicated space for improving consent forms & compliance • Experimenting with automation and AI to streamline processes • Ensuring outdated recordings and data are properly managed If you don’t intentionally plan for ops work, it won’t get done. So next time you build a research roadmap, ask yourself—are you only planning for projects, or are you also setting up your team for long-term success? UXR Study

A lot of engineers think scalability means throwing in microservices, complex abstractions, or designing for problems that don’t even exist yet. I used to think like that too. Until I realized… scalability isn’t about complexity, it’s about efficiency. Here’s what actually matters: - Will your code be easy to extend in 6 months? - Can someone new understand it without a PhD? - Does it solve the problem today without blocking growth tomorrow? Overengineering is a trap. You don’t need "future-proof" architecture on day one. You need clarity, adaptability, and smart trade-offs. Some simple but powerful rules I follow now: ✅ Start small, scale when needed. Premature optimization is a waste. ✅ Keep it readable. If another engineer (or future you) can’t understand it, it’s a problem. ✅ Performance vs. maintainability. Scaling isn’t just about speed, it’s about long-term growth. Big Tech values engineers who can scale without sinking in complexity. Be that engineer. What’s the worst overengineering horror story you’ve seen? #softwareengineer