# Allston Fojas Systems Software Engineer, Core OS Power and Performance at Apple Contact: allstonfojas@gmail.com I build decision systems that measure their own outcomes. ## Skills - **Programming Languages:** Python, Objective-C, C++, SQL, Swift, JavaScript, HTML, CSS, Java - **Development Tools:** Git, Jupyter, Spark, Streamlit, Tableau, Jenkins, Kubernetes, Convex, Vercel, Bash - **AI Agent Tools:** Claude Code, Codex, LangGraph, Claude API, OpenAI API, Gemini, MCP, OpenCode, Pi ## Experience ### Apple - Systems Software Engineer, Core OS Power and Performance 2022-07 to Present - Cupertino, CA - Led end-to-end design and implementation of telemetry for a next-generation WiFi and Bluetooth chipset, enabling quantitative battery performance comparisons against prior chipsets across large device fleets. - Drove cross-functional alignment with the WiFi, Bluetooth, Privacy, logging infrastructure, and analytics teams. - Implemented on-device telemetry pipelines in Objective-C and C++ for iOS, macOS, and watchOS. - Designed and executed large-scale data analysis workflows using Python, Jupyter, and Spark. - Built agentic AI workflows to make investigations of key metrics more efficient. ### Cisco - Software Engineer 2020-08 to 2022-06 - San Jose, CA - Developed performance test scripts in Python and ran them against the team's Kubernetes cluster. - Implemented unit tests for the team's services in Go, with CI/CD through Jenkins. - Built data visualization dashboards in Grafana and analyzed test logs in Kibana. ### VIZIO - Data Engineer Intern 2020-06 to 2020-08 - San Jose, CA - Created a location weather API driving product recommendations surfaced on VIZIO TVs. - Built the API and recommender with Python, Pandas, scikit-learn, and Scrapy. ### American Express - Software Engineer Intern 2019-06 to 2019-08 - New York, NY - Replaced a $1M vendor relationship with an Excel file normalizer that standardized incoming formatting. - Worked with Product Managers and the Business team to meet the business use case. ## Projects ### Waypoint - agentic trip planner *Apr 2026 - Jun 2026* An orchestrator that dispatches four parallel Claude research agents to produce a costed, sequenced trip itinerary. Outcome: 248 tests. Built across a five-hackathon cascade and presented at the Andreessen Horowitz office in San Francisco. - Four parallel Sonnet research agents - points of interest, hotels and flights and restaurants, photography, transit - each a multi-turn web-search loop terminating in a structured submit tool call. - A provider-abstraction layer that allows swapping the generative-media model without touching the pipeline. - A prompt-injection input sanitizer, per-session cost telemetry, and graceful partial failure when one agent returns nothing. - A second documented research path builds a prompt for Perplexity Computer and parses its output back - copy-paste through the UI, not an API integration. - Presented at the a16z, Overshoot, Fal and Mux Video hackathon, May 2026. Stack: Python 3.12, Anthropic SDK, FastAPI, Jinja2, fal, Mux, FFmpeg, SQLite, pytest, Playwright Links: - [Product page](https://waypoint-manifest.butterbase.dev/) ### Automated housing-search decision system *2026* Scored 531 candidate listings across 26 discovery runs against a deterministic, versioned admission gate. Signed a lease on a property from the board. Outcome: Signed a lease on a property the system surfaced. - Deterministic, versioned admission gate: hard constraints reject before any scoring runs, so a rejection is always explainable. - Dual-anchor commute engine on Nominatim and OSRM, with no paid routing API. - Shipped two surfaces: a static GitHub Pages board and a Next.js, TypeScript, and Convex application. - 131 tests across the Python pipeline and the TypeScript app. Stack: Python, TypeScript, Next.js, Convex, Leaflet, OSRM, Nominatim Links: - [Live board](https://allstoncodes.github.io/south-bay-housing-board/) - [Source](https://github.com/allstoncodes/south-bay-housing-board) ### Execute-and-learn decision loop *2026* A reusable execute-then-learn loop applied across two unrelated domains: housing search and content production. - The scoring rubric is the single mutable surface and is read at call time, so behaviour changes are data changes rather than code changes. - An append-only heuristic change log makes every past scoring decision reconstructible. - A two-layer prediction-versus-outcome model separates what was predicted from what was observed. - Re-scoring is a deterministic tested function; every rubric mutation requires human approval. - Four consecutive retrospectives declined to change a weight because the outcome sample was too thin to learn from. Stack: Python, Markdown-as-data, LanceDB ### Concurrency-safe git layer for multi-agent workspaces *2026* 704 lines of Python that let many agents commit to one repository without clobbering each other. - Advisory flock with explicit handling for deleted-lockfile and split-inode races. - An index-health gate that blocks commits during a filesystem-eviction state which would otherwise commit mass phantom deletions. - Manifest-scoped staging, so parallel agents cannot stage each other's in-flight work. - Bounded rebase-retry that aborts loudly instead of force-pushing. Stack: Python, Git ### Headless content-production pipeline *2026* Three Python packages, roughly 2,900 lines and 211 tests, that turn a semantic search over raw footage into a rendered cut. Outcome: Search-driven edit-decision lists reached 9 of 9 top-1 retrieval accuracy on the evaluation set. - Semantic footage retrieval over TwelveLabs Marengo 3.0 and Pegasus 1.2, across both visual and audio signal. - An FFmpeg render engine driven by a typed beat contract rather than imperative edit steps. - A Remotion transparent ProRes 4444 alpha overlay track composited over the base render. Stack: Python, FFmpeg, Remotion, React, TypeScript, TwelveLabs ### Local-first inference lane with a fail-closed privacy guard *2026* An on-device model lane on 24 GB Apple silicon, with a three-layer guard that fails closed so private corpora do not leave the machine. - Ollama and MLX serving both OpenAI-compatible and Anthropic-compatible routes behind one interface. - A three-layer allowlist guard, not a denylist, that fails closed before reading any user-supplied parameter, which makes it injection-resistant. - The huggingface route is deliberately excluded from the allowlist because the router silently forwards it to cloud. - Root-caused a tool-calling failure to a model dropping sentinel tags, proven a model fault rather than a harness fault by A/B against a second model, then shipped a parity regression gate. Stack: Python, Ollama, MLX, Apple silicon, Qwen3.5, Gemma 3, Claude Code, pi ### Agent skills library (private) *2026* A private library of composable agent skills: the operating manual for the systems above. - Each skill encodes one workflow as an explicit procedure with its own verification steps, rather than relying on a prompt. - Skills compose: planning, test-first implementation, and independent review are separate skills chained by a controller. Stack: Markdown, Python, Bash ### Research vault showcase *April 2026* A public walkthrough of the retrieval architecture behind a personal research corpus. - Hybrid retrieval: vector search with HNSW cosine, BM25 lexical search, and reciprocal-rank fusion over the merged result set. Stack: Python, LanceDB, Tantivy Links: - [Live](https://allstoncodes.github.io/research-vault-showcase/) ### Predicting Disease Risk *Apr 2020 - Jun 2020* Supervised prediction of individual risk for three diseases: coronary artery disease, Alzheimer's and diabetes. Outcome: Accuracies of 0.91, 0.89 and 0.88 across the three disease models. - Predicted risk for three diseases from one body of modelling work: coronary artery disease, Alzheimer's and diabetes. - Built as the UC San Diego Data Science capstone in the genetics domain. - Delivered as reproducible notebooks. Stack: Python, pandas, NumPy, scikit-learn, Matplotlib, Seaborn Links: - [Source](https://github.com/allstonf/predicting_disease_ml) ### Weather Forecast IoT Device Map *Apr 2020 - Jun 2020* A web app mapping IoT weather sensors and testing whether forecast accuracy varies by city. Outcome: Determined that forecasts for Sacramento are about 7% more accurate than for San Diego. - A web app spanning an ESP32 sensor device, a MySQL store and a browser front end. - Compared forecast accuracy between two cities and quantified the gap. Stack: HTML, CSS, JavaScript, Python, MySQL, C, Arduino, ESP32 Links: - [Source](https://github.com/allstonf/weather_forecast) ### Music Master *Apr 2019 - Jun 2019* A playlist tool for DJs that recommends the next song by BPM or by title. - Recommends the next track by BPM or by song title. - A web app on Node.js and Firebase, integrating the Spotify API. Stack: HTML, CSS, JavaScript, Node.js, Firebase, Spotify API Links: - [Source](https://github.com/allstonf/musicmaster.github.io) ### Facebook Data Challenge 2019 *May 2019* A geospatial analysis recommending where in San Francisco a retail business should open. Outcome: Identified Hayes Valley and Haight-Ashbury as the best-suited areas. - Heat maps of San Francisco population density, income and business demographics, built with Folium. - Entered as a competition submission. Stack: Python, Folium Links: - [Source](https://github.com/allstonf/facebook_data) ## Education B.S. Data Science, University of California San Diego (Inaugural cohort, Halicioglu Data Science Institute)