Hire the Best Algorithms Engineers

I’m a software engineer with a strong focus on problem solving and algorithms. I’ve solved 7000+ algorithmic problems across platforms like LeetCode and Codeforces, ranking in the top 0.16% on LeetCode and achieving Candidate Master on Codeforces. My background is deeply rooted in data structures, algorithmic thinking, and writing efficient, scalable solutions under constraints. Beyond solving problems myself, I mentor students and engineers to master problem-solving skills for coding interviews and technical assessments. I focus on building strong fundamentals and recognizing patterns.

I help research teams and technical founders build reliable, testable scientific and quantitative software — from research prototypes and backtesting frameworks to production-grade execution systems, simulation engines, and performance optimization. I also conduct independent technical review of quantitative models, frameworks, and research. If you're dealing with slow code, messy data, a model you can't trust, or a research prototype that needs to become production, I can turn it into a robust, reproducible system. Credibility: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase (derivative pricing, 1M+ LOC C++ library). Max Planck Institute postdoc (general relativistic hydrodynamics simulations on HPC clusters, 1000+ cores). 13 peer-reviewed publications, 1,300+ citations, h-index 12. WHAT I DELIVER ▸ Quant & Options Engineering Backtesting frameworks (event-driven or vectorized), walk-forward, leakage checks Options analytics: Greeks, IV surfaces, Black-Scholes / numerical methods Research → production pipelines (clean architecture, tests, logging, monitoring) Execution integrations (e.g., IBKR) and robust order/risk handling Market data ingestion, cleaning, corporate actions handling, quality control checks ML and statistical models for time-series signals (proper time-series CV, no leakage) ▸ Scientific Computing & Research Tooling Custom software and research tooling for research-driven teams across physics, chemistry, biology, engineering and beyond — where the underlying problem is mathematical or computational. - Custom numerical solvers (finite volume / finite difference, spectral methods, particle methods), with stability and convergence analysis - Optimization engines (Bayesian optimization, gradient-based, evolutionary) for experimental design, formulation, and parameter search - Simulation frameworks for physical, chemical, and biological systems — from prototype to production-grade - Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows - Verification and validation: benchmarks, unit tests, regression tests, convergence studies - Analysis tools, dashboards, and reporting infrastructure for research workflows - Air-gapped and reproducible deployments where IP sensitivity or regulatory context requires it ▸ Quantitative & Mathematical Review (NDA-protected) Independent technical review of quantitative models, frameworks, and research Verification of internal consistency, identifiability, hidden assumptions, and mathematical correctness Assessment of whether the formal structure supports the conclusions drawn from it Review of implementation against specification: numerical stability, edge cases, and code-to-specification fidelity ▸ HPC & Performance Engineering Performance engineering for large-scale scientific and ML workloads Distributed computing (MPI/OpenMP/CUDA), GPU optimization, memory and I/O tuning, parallel architectures, inference and training infrastructure at scale. Profiling, refactors, and speedups for codebases that need to run reliably under production load. WHY CLIENTS WORK WITH ME - Trustworthy engineering: I turn research prototypes into tested, reproducible production code - De-risking: I find failure modes early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability: clean architecture, docs, and handover-ready delivery your team can extend - Communication: clear milestones, concise updates, realistic timelines — no surprises - Math ↔ engineering bridge: strong intuition for both theory and implementation details IDEAL PROJECTS - Quant strategy development, backtesting and research infrastructure - Options analytics and derivatives tooling - Mathematical review of quantitative manuscripts, white papers, or research frameworks - Independent validation of production models or implementations against specification - Market data pipeline and reproducibility upgrades - Performance optimization of slow Python/C++ codebases - Distributed training, GPU optimization, and inference serving for AI/ML workloads - Custom scientific or industrialized simulation or numerical software - Custom internal R&D tools for research labs and technical teams If this sounds like a fit, message me with a detailed brief with your current stack and success criteria — I'll let you know how I can help.

- CTO at Singularis Programming - Extensive experience in Computer Vision and Machine Learning projects. - Keen on cutting-edge technologies and helping businesses achieve their objectives. 🏅About company - 13+ years of continuous experience in programming - Our engineers got significant achievements and results in the Programming World Cup ACM ICPC - 200+ successfully completed projects 🤝 Trusted by Intel, NEC, Samsung, DHL, VIATechnik, Amberg Technologies - Working with partners around the world, in all time zones: in Europe, North America, MENA, the Asia-Pacific region. - Completed projects in the UK, Switzerland, the USA, Spain, Germany, Belgium and other of EU. 💡 OUR SERVICES include, but are not limited to: – Definition and Planning, Business Analysis, Project Planning – Back-end development + DevOps Application Architecture, REST API, Application development, CI/CD, .NET Stack, C#, Entity Framework, NHibernate, Microservices – Front-end development Mark-upping, Client-side development, Responsiveness, React, Typescript, JavaScript, Redux, MobX – AI / Machine Learning / LLM Deep Learning and convolutional networks, GAN, TensorFlow, Keras, Torch, Stable Diffusion, Transformer architectures, fine-tuning and optimization, Hugging Face, LangChain, RAG, prompt engineering, Unsloth, llama.cpp, vLLM, model quantization and deployment – Computer Vision OpenCV, C++/Python, MMCV, YOLO, dlib, developing applications for Kinect and Intel RealSense, Microsoft HoloLens, video processing with FFmpeg – Mobile development iOS, Android, Cross-platform – Testing/Quality Assurance Manual (TestLink), Unit Tests, Automation, Stress, checklists, test-cases, Cypress – Technical Support and Maintenance DB Back-ups, Cloud/Data migration, Bug Fixing, 24/7 support 💼 Main principles - Maximal quality of the provided services and the developed software. - Focus on permanent improvement of our team. - Stay tuned at the front edge of modern computer technologies - Apply the most advanced approaches to software development. 👋🏼 I’m open to collaborate. Feel free to get in touch with me and my team to discuss any project implementation — we’ll be happy to share valuable ideas and suggestions!

I am a mathematician that wants to help businesses of any size to stay competitive and improve using modern advances in applied and industrial mathematics. I have experience with Analysis, Measure theory, Geometry, Modelling, Optimization and Machine learning. I am interested in working closely with technical staff of the client business and designing new or improving existing models for the better production and performance of either physical or digital products and processes. My usual project involves understanding the real-life problem encountered in the industry, creating suitable mathematical models, formulating the corresponding mathematical problems, and solving them.

I have Expert-Vetted Talent (EVT) badge - it's Upwork's top 1% freelancers — pre-screened by Upwork Talent Managers and experts in their field. Out of ~30 million programmers worldwide, only a few thousand know Algorithms & Data Structures better than I do, which is proven by programming competitions. Please, contact me if you need that skill level (top 0.01%). I can do algorithmic/performance work in C/C++, Python, SQL, Java, MQL4, MQL5, C#, Assembly, JavaScript, Julia, Rust, and probably other languages - learning them rapidly. I also work with AI, mostly in NLP and NLU: large language models including OpenAI GPT-3, Bloom, BloomZ, GPT-J 6B, LLaMA, Alpaca, etc.; HuggingFace Transformers, Accelerate; Petals, Deepspeed, zfp/zfpy; CUDA, CPU, and MPS (AArch 64 M2 MacOS Metal GPU) backends. I have some work experience with Apple Neural Engine (ANE). In AI, I also worked with XGBoost for predictions (including trading), LibSVM, TensorFlow, PyTorch, Scikit-learn, etc. I have the hardware in my home office for training and inference with large language models and other AI. English: C1 (Grammarly plugin says I use more unique words than 95% of other users, native speakers included). Polish: B1 (86%). Russian, Belarusian: Native. - With unique skills in Algorithms & Data Structures, I improve programs asymptotically (often 100 or more times on large input data). - 29 years of programming (started Basic and assembler on ZX Spectrum), 24 years of C/C++, 16 years of commercial work experience + 3 years of research projects. - Contributed to widely used Open Source projects: LLVM/Clang (my contribution is XRay profiler on ARM32 and AArch64 systems), Katana Graph (multiple small contributions mostly driven by the proprietary part where I do GPU/CUDA), CBMC "C Bounds Model Checking" (I contributed parallelized output of DIMACS formatted Boolean Satisfiability formula), oatpp (C++ web framework, I contributed bug-fixes), OWL (OptiX Wrapper Library, I contributed build fixes for Ubuntu), Galois (research project for distributed computations on graphs, I contributed GPU improvements) - Actively participated in bug reporting and reproduction (for NVIDIA CUDA, Cadical&kissat boolean satisfiability solver, JBOSS, MariaDB, Tensorflow, Linux, etc) - Led several open-source projects of my own: ProbQA (a video game recommendation system based on a high-performance Bayesian inference engine with CUDA, SIMD, and multi-threading); InSoAr (automatic reconstruction of software architecture from source code ), a multi-threaded Boolean Satisfiability solver, etc. Working for hire, implemented: - efficient multi-threading, scaling real-world workloads almost linearly with the number of CPU cores (128x for AMD Ryzen Threadripper 3990X) - SIMD vectorization (SSE/AVX), up to 8x improvement in computing thread or even copying (see my "Faster alternatives to memcpy" answer on Stackoverflow, URL upon request). - cache-aware algorithms: up to 50x improvement on some workloads - up to 20 trillion operations/second in CUDA (thousands of times faster than CPU) - up to the theoretical limit (6.8 Gigarays/second on RTX 2080 laptop GPU) in ray-tracing with OWL and OptiX - AVX512 and RTM (Restricted Transactional Memory) based acceleration, 16x improvement for float numbers -up to 20x improvements to cryptocurrency miners on CPU using AVX512 and cache-friendly algorithms - crypto-miners for Ethereum, Bittensor, Qubic 15K reputation on Stackoverflow: (1915854/serge-rogatch) Topcoder SRM score: 1480 - among top 5K programmers in the world - top 0.02% (rSerge) I developed all kinds of networking applications, from Linux Kernel modules up to Web applications. The majority of work was, of course, done at TCP/IP level with socket calls like send/recv/select. Programming languages: C++, C++11/14/17/20, C, Python, x86/x64/ARM/AArch64 assembly, SQL, C# .NET, JavaScript, HTML, CSS, Java, MQL4, MQL5, XML, Cypher, Rust. Libraries/Frameworks: PyTorch, Tensorflow, HuggingFace Transformers/Accelerate/Safetensors, Hivemind/Petals, OpenAI, tiktoken, Django, Flask, STL, LibSVM, XGBoost, libcurl, Selenium, PyTorch, Transformers. Technologies: OpenMP, CUDA, SIMD (AVX&SSE, RTM), Linux Kernel Modules, OptiX/OWL, RTX, raytracing. Theory/Principles/Know-how/Methodologies: Algorithms & Data Structures, Performance Optimization, Artificial Intelligence, Multithreading, Vectorization, OOP, Low-latency, High-frequency, Blockchain. Open source code: Clang, LLVM, LLVM's compiler-rt library, Linux Kernel, a few of my own repositories, contributions to AI and Algorithm open-source projects such as Petals and CBMC. Tools/APIs/Architectures/Platforms: PostgreSQL, MSSQL, MySQL, Neo4j, MATLAB, CMake, GIT, MT4, MetaTrader 5, Conda, PyCharm. Virtual Machines / Containers: Docker, VMWare, VirtualBox, QEmu, Hyper-V. OSes: Windows, Linux, Android, macOS

I’m an AWS Certified Solutions Architect – Professional with multiple AWS specialty certifications and hands-on experience designing scalable, secure cloud architectures. I help businesses migrate applications to AWS, improve infrastructure reliability, and implement DevOps best practices. My AWS Certifications: • AWS Certified Solutions Architect – Professional • AWS Certified Security – Specialty • AWS Certified Advanced Networking – Specialty • AWS Certified Solutions Architect – Associate • AWS Certified Cloud Practitioner I can help with: • Cloud migration to AWS • High-availability architecture design • DevOps automation and CI/CD pipelines • Infrastructure security and networking • Application deployment and monitoring I also teach AWS concepts on my YouTube channel(Learn Tech), helping engineers understand real-world AWS architecture and best practices.