Staff Engineer, Optimization & Decision Systems · PhD, Operations Research
PhD in Operations Research and eight years building the Python backends the models actually run in — a vehicle-routing engine across 40,000+ locations, from whiteboard to production in 16 months with a team of six, and a robust optimization method published in Expert Systems with Applications.
Open to conversations now. US work authorization in progress; contract and advisory engagements available today.
Real solvers, computing while you watch — most in your browser, the heaviest on a container that scales to zero. Where a page plots published results, it says so.
Robust optimization
A sampling-based method that hands you a Pareto frontier of robust solutions and lets you choose the trade-off after seeing the options, instead of committing to an uncertainty budget before you know what it costs.
Bandwidth Packing cases matched or beaten: 92.5% (as published, ESWA 2022)
Robust optimization
A continuous, normalized robustness measure for optimization under interval uncertainty — one number in [0,1] that ranks feasible solutions by how well they resist the uncertainty around them, with an exact closed form and a Monte-Carlo estimator that agree.
Measure range: η ∈ [0, 1], continuous
Metaheuristics
A public repository of mine contains 201 committed simulated-annealing runs, every one of them infeasible. This takes the failure apart, isolates its three causes, and reaches a few percent of the proven optimum in milliseconds.
Committed runs: 201
Mixed-integer programming
Fit the most people into a room subject to a minimum-separation rule — maximum independent set on a conflict graph, solved to proven optimality in the browser, on a floor plan you can rearrange.
Capacity at 1 m separation: 36 of 36 seats
Outcomes, not job descriptions. Every number below is one I owned or led.
Nitryx → Progress Rail · A Caterpillar Company
Designed and led a Python optimization engine for fleet routing across 40k+ locations — started at Nitryx, carried through Progress Rail's acquisition, and taken from concept to production-ready in 16 months with a team of 6.
−20% fleet size · $60K/month cloud savings via in-house Distance Matrix APIs
Optibus · Public Transit SaaS
Led a 5-developer team owning report generation and integrations for clients across North America, Latin America, and Western Europe — including real-time transportation map feeds.
−50% new bugs · 2× team development velocity · key contract secured
PhD Research · UNIFESP + Polytechnique Montréal
Created a sampling-based multi-objective iterative method for optimization under uncertainty, plus "Hardness", a new robustness measure. Published in Expert Systems with Applications.
Outperformed or matched literature methods in 92.5% of Bandwidth Packing cases
Banco Safra · Banking
Built a Markov-chain model of app user behavior to retarget communication, and executive-level analyses that pivoted credit policy and marketing. Led fraud-pattern analysis on PIX instant payments.
+15% app activation · +45% high-quality credit card leads
Porto Seguro · Insurance
Started the company's movement toward API-based models and AWS cloud computing, and shipped a freight-size estimation heuristic used in logistics planning.
Deployment time: 5 days → 5 minutes · 20% logistics optimization
Public Health · COVID-19
Co-built an application forecasting intensive-care bed demand for Brazilian health services during COVID-19, published in a national epidemiology journal. Also open-sourced a classroom-occupancy optimizer for safe school distancing.
Published in Epidemiologia e Serviços de Saúde (2020)
From maintaining flight simulators to leading engineering teams — the same question the whole way.
Built and shipped LLM systems for legal research, contract review and document analysis at SG LAW LLP — retrieval over confidential case material, evaluation of model output against counsel review, and the anonymisation and redaction pipeline that made the corpus usable at all. Owns IT strategy, security across the AI systems, and firm-wide adoption.
End-to-end backend engineering for AI-driven product development: distributed cloud architectures (AWS, Azure), high-throughput data pipelines, LLM-powered ML models. Mentoring a fully remote team of 4+ engineers.
Drove the institute's technical strategy and R&D execution; project prospecting, technical hiring, and mentoring of engineers and researchers.
Founded and led a consulting firm in Campinas, Brazil, crafting bespoke AI strategies and optimization solutions for clients; operations closed in January 2026.
Designed and implemented custom data-integration solutions on hotglue's embedded ETL platform.
Led the customer-oriented R&D team for specialized exports; pair-programming culture, knowledge-sharing initiatives ("Optifridays"), technical interviewing during rapid company growth.
Started the vehicle-routing optimization engine at Nitryx and carried it into Progress Rail after the acquisition — leading a team of 6 from conception to production-ready in 16 months, with Distance Matrix APIs and six real-time integrations.
Mixed Media Models, Markov-chain behavior models, PIX fraud-pattern detection, executive-level analytics.
API-based model serving on AWS; optimization heuristics for logistics; ML with XGBoost, scikit-learn, Gurobi/CPLEX.
Column-generation scheduling with CPLEX for railroad workforce planning; real-time Big Data (Kafka, PySpark) NLP fraud detection.
Optimization under uncertainty, in the research group at CIRRELT.
Thesis: "Optimization under uncertainty: a new computational method, a new robustness measure, and applications."
Peer-reviewed work at the intersection of optimization and uncertainty.
Renan Brito Cano Butkeraites, Luiz Leduino de Salles Neto, Michel Gendreau — Expert Systems with Applications, vol. 203, 2022 · doi:10.1016/j.eswa.2022.117337
Forecast UTI: an application for forecasting intensive care unit beds during the COVID-19 pandemic
Luiz Leduino de Salles Neto, Renan Brito Cano Butkeraites, Martins, Chaves, Horacio Hideki Yanasse — Epidemiologia e Serviços de Saúde, 2020
Efficient frontier of credit risk using Monte Carlo simulation
Renan Brito Cano Butkeraites, José Luiz Chela, Luiz Leduino de Salles Neto — International Journal of Business Intelligence and Systems Engineering, 2019
Renan Brito Cano Butkeraites — UNIFESP, 2021
The short version of a long path.
I started as a mechatronics technician maintaining flight simulators, and ended up with a PhD in Operations Research — because I kept asking how decisions could be made better under uncertainty. That took me through a research stay at CIRRELT in Montréal, the SIROM method, and "Hardness", a new way to measure how robust a solution really is.
Since then I've applied that lens in insurance, banking, rail logistics, public transit, and data platforms: always the same job, turning a messy real-world decision into a model, and the model into production software. I've led teams through that whole arc — hiring, mentoring, pair programming, and shipping.
Today I manage technology for SG Global Group in Kennesaw, Georgia — leading legal AI and LLM initiatives for SG LAW LLP — while directing technology at Fresh Codes, an AI-driven product studio, with remote teams and global clients. Off the clock: mathematical puzzles, good food, and music — sometimes all three while putting my daughter to sleep.