Location: Switzerland Remote: Fine going to office. Remote works aswell Technologies: [Vision] OpenCV, torchvision, mediapipe, imgaug [Deep Learning] PyTorch, TensorFlow, ONNX, TensorRT; [Languages] Python, C++, Java; [Infrastructure] Docker, MongoDB, SQL, Triton. Résumé/CV: https://www.icloud.com/iclouddrive/0e1wsmfeW4XrCNBvU4BESGktg... Email: matteoriva [at] me [dot] com

Location: Tampa, Florida

Remote: Yes

Willing to Relocate: Yes

Technologies: Python, Numpy, Pandas, SciKit, PyTorch, Matplotlib, Nim, Datamancer, C, Linux, GCC cross-compilation w/ musl-libc (ARM, RISC-V), gnuplot, LaTeX, Typst, Clojure, JavaScript/TypeScript, HTML/CSS, PostgreSQL

Resume/CV: https://mega.nz/file/NOBCkDgT#zjEmVYv9WuoYti7xPxUJkrOPC2v-dQ...

Email: nervecenter7@gmail.com

I'm a Master's in CS, and a primarily backend software engineer focusing on mathematical & data analysis, currently specializing in vibration, signals, and sensor analysis, as well as data visualizations. I recently reverse-engineered and from-scratch rewrote an obfuscated backend analysis codebase, resulting in a maintainable, readable, concise, well-documented system.

I have some past experience building backend web services with Clojure, Postgres, and Datomic, and more recent experience making local frontend GUI tools with TypeScript.

I'm a strong advocate of FOSS tools, powerful pragmatic languages, modular technology stacks, and simple solutions. All directly informed by experience with how proprietary languages & libraries, monolithic stacks, and clever programming wizardry result in systems that are brittle and crash-prone, expensive or impossible to maintain, and difficult to defend to clients and courts.

Remote: Optional Willing to relocate: Yes, worldwide Technologies: Python, Matlab Email: m.faylias [at] gmail [dot] com

My Contribution (Short Profile) • Applied Al research with focus on real-time signal processing, decision logic & anomaly detection • Modular signal & system architecture — complements classical ML stacks • Physically informed analogy modules (e.g., superposition in Python) • Hand-crafted, explainable features & operators • Guiding principle: ML/RL cleanly embedded into a predefined architecture Example Result Phase-aware XGBoost trained on "Melodic House" harmonies → showed genre generality beyond training data Working Principle Reproducible research: clear pipelines, paper-style notebooks, deterministic exports Mainstream Deep Learning (TensorFlow) • Purpose: ML, Deep Learning, neural networks • Focus: Classification, regression, prediction • Architecture: Computation graph & tensors • Inputs: n-dim tensors • Outputs: Probabilities, models, vectors • Computational Principle: Gradient descent • Components: Layers, losses, optimizers • Tooling: GPU-first workflows, tf.keras, deployment stacks • Data Strategy: Big Data, automatic feature learning • Explainability: Often low (requires additional tooling) • Style: GPU-heavy, often overdimensioned • Versioning: Model checkpoints, weights

Location: Boston Remote: Yes Willing to relocate: Yes Technologies: Python, Pandas/Numpy, Jupyter, JS/TS and something many devs skip: actually talking to users. Résumé/CV: https://www.linkedin.com/in/paddymullen/ Email: paddy@paddymullen.com I'm a developer who believes code isn't valuable unless it's used. That's why I start with conversations, not commits. I work to understand real problems before reaching for shiny new packages. I build tools that are simple, effective, and easy to adopt. My goal is always to solve the problem and make sure people know there’s a solution. In my next role, I'm looking to work with a team that values clarity and impact. I'm especially interested in data heavy environments where thoughtful tooling can make workflows better. Most recently, I built [Buckaroo](https://github.com/paddymul/buckaroo) an open source data table for Jupyter using Pandas/Polars. It combines fast rendering, summary stats, and a low-code UI. It scratches an itch I've had for over a decade and has already streamlined my own analysis workflow.

SEEKING WORK - Data scientist, data/software consulting & fractional leadership, Canada/remote worldwide, email in profile.

I'm a data scientist in a world of data technicians and I look for hard problems to solve. Hair on fire "This is causing lemon-law recalls and we can't solve it." type problems - I've done a few of those. I have 15+ years experience helping companies plan, prototype, and productionize sane data science solutions.

I love avant-garde problems, and have a pretty unique skillset. I've worked with names you know in automotive, aviation, tech, and other spaces. Recently I've been working with Oil & Gas - Production estimates, pricing, failure analysis, and other interesting things.

I've worked on projects including LLMs, knowledge extraction, automotive part failure prediction, vehicle route planning in constrained environments, translation, maintenance optimization, automated sports highlights, maritime piracy, and more.

Two things I would love to work on, feel free to ping me on these:

    - Offensive & defensive security. I've been working through some ideas for a while now  to apply AI (Not LLMs) to security. I think they could harden a network environment even in the presence of usable exploits against an active adversary. 
 
    - Executive memory and longer range functioning using LLM's. Steve Yegge has been working a little bit on this recently (and blogging about it), I’ve been working on formalizing this.

Location: New Zealand, Manawatu Remote: Yes Willing to relocate: No Technologies: Go, Java, Git, Erlang, Postgres, Linux Resume: https://www.linkedin.com/in/francis-stephens/ Email: francisstephens@gmail.com I work primarily on backend systems, with a strong focus on performance and system stability/resilience. I worked as a performance engineer at the mobile add-attribution company Adjust. Some interesting open-source projects include

https://github.com/fmstephe/memorymanager An exploratory manual memory allocator for building large in-memory data structures with near zero GC cost.

https://github.com/fmstephe/matching_engine A financial trading matching engine with a somewhat novel red+black tree implementation.

https://github.com/fmstephe/flib A set of packages primarily in support of a lock-free single-producer single-consumer queue.

My ideal position would be working on backend systems primarily in Go.

  Location: Rosamond, CA
  Remote: Preferred.
  Willing to relocate: For the right role and compensation.
  Technologies: Python, Fortran, C, Matlab
                PyTest, coverage, PyLint, ruff, Snyk, mypy, Sonar Cloud
                Apache Airflow, Google Cloud Scheduler
                Json, YAML, Parquet, Avro, csv, xls(x)
                BigQuery, GCS, SQL (MySQL, PostgreSQL), NoSQL 
                NumPy, Pandas, Polars, PyFlask
                SodaCore, Pydantic, jsonschema, schema (python package)
                Tecplot, Paraview, gnuplot, matplotlib, Plotly
                OpenMP, MPI
                SciKit-Learn, TensorFlow, Keras
                AWS, Google Cloud (GCP)
                Docker, Kubernetes
                GitHub, BitBucket, GitLab, svn, git
  Résumé/CV: https://www.linkedin.com/in/nathan-mundis-3bb7101b9/
  Email: nlmundis.work@gmail.com

I am a mostly a backend engineer with lots of experience with data science, engineering, data integrity, QA, numerical methods, and high-performance computing.

Meticulous, yet rapid coder Knowledgeable, yet humble contributor Fastidious, yet broadly-explorative technical designer Curious, thorough, yet nimble learner Bar-raising, yet empathetic leader

Location: Europe

Remote: Yes

Willing to relocate: No

Technologies: Node.js, PostgreSQL, MongoDB, AWS, Heroku, Datadog, React.js, JavaScript, Next.js, Apollo, GraphQL, data visualizations (D3.js, Mapbox, Leaflet), headless CMS (Contentful, Prismic, Strapi), MobX, Redux, Wordpress, Tailwind, Bootstrap, Material UI, Webpack, TypeScript, web3 (ethers.js), chrome extensions and more.

Résumé/CV: https://www.linkedin.com/in/andrejgajdos

Email: mail [at] andrejgajdos.com

I am a full-stack dev and tech lead with focus on PERN/MERN stack and over 10 years of experience delivering software. I have a strong background as a software architect and have successfully led teams as a fractional CTO. I have worked with clients from diverse industries worldwide. I have successfully delivered over 20 projects for a range of clients, including solo founders, early stage startups, growing startups, digital agencies, and notable companies like Apple and Binance.

Portfolio: https://andrejgajdos.com/projects/

Personal Website: https://andrejgajdos.com

Github: https://github.com/AndrejGajdos

  Location: Philadelphia, PA
  Remote: preferably remote, open to on-site/hybrid
  Willing to relocate: no
  Technologies: JavaScript, TypeScript, React, HTML, HTML5, CSS, CSS3, Ruby on Rails, Next.js, GraphQL, Tailwind CSS, Node.js, FHIR, AWS, SQL, MySQL
  Résumé/CV: https://docs.google.com/document/d/1wM8WgWAht1_NE5M2Xir6EDaijJZzIkLV/edit?usp=sharing&ouid=108591034527128282853&rtpof=true&sd=true
  Email: amandawitecki(at)gmail(dot)com
  LinkedIn: https://www.linkedin.com/in/amanda-witecki-586334135/

About me: I'm a senior software engineer with over 10 years of strong engineering experience in the healthcare IT space. I have a proven track record of delivering impactful web applications across full-stack and front-end development, and I bring to the table not just technical skills but engineering leadership skills. I've advocated for best practices, helped shape my team's direction, and contributed to leadership discussions in addition to leading technical investigations and leading/facilitating aspects of agile sprints such as planning, pointing, and demos.

I'm not limiting myself to opportunities using the technologies I already know; I would love to continue growing my technical skills.