data.typ

#let personal = (
    name: "Jonathan Jin",
    email: "me@jonathanj.in",
    url: "https://jonathanj.in",
    linkedin: "https://linkedin.com/in/jinnovation",
    github: "https://github.com/jinnovation",
    huggingface: "https://huggingface.co/jinnovation",
)

#let skills = (
    "AI, ML, and Data": (
        link("https://ray.io")[Ray],
        link("https://spark.apache.org")[Spark],
        link("https://www.tensorzero.com/docs")[TensorZero],
        link("https://phoenix.arize.com")[Phoenix],
        link("https://github.com/Arize-ai/openinference")[OpenInference],
        link("https://pytorch.org")[PyTorch],
        link("https://flink.apache.org/")[Flink],
        link("https://mlflow.org")[MLflow],
        link("https://airflow.apache.org")[Airflow],
        link("https://www.kubeflow.org")[Kubeflow],
        link("https://www.tensorflow.org")[TensorFlow],
    ),
    "Infrastructure and Distributed Systems": (
        "Kubernetes",
        "Terraform",
        "Helm",
        "Prometheus",
        "OpenTelemetry",
        "gRPC",
        "Docker",
    ),
    "Enterprise Platforms": (
        "Amazon Web Services (AWS)",
        "Google Cloud (GCP)",
        link("https://databricks.com")[Databricks],
        link("https://arize.com")[Arize],
    ),
    "Programming Languages": (
        "Python (fluent)",
        "Go (fluent)",
        "Rust (fledgling)",
    )
)

#let education = (
    (
        institution: "University of Chicago",
        url: "https://uchicago.edu",
        area: "Computer Science",
        startYear: 2011,
        endYear: 2015,
        location: "Chicago, IL",
        degrees: (
            (
                type: "B.S.",
                area: "Computer Science",
            ),
            (
                type: "B.A",
                area: "Economics",
            )
        )
    ),
)

#let speaking = (
    (
        title: "Trimming the Long Tail of Production Model Ownership at Hinge",
        url: "https://www.youtube.com/watch?v=AaEMUlmKO4s&list=PLAesBe-zAQmFUeS0gMFSII4m-Zw4CoOoE",
        conference: "Data Council",
        date: "2025-04-22",
    ),
    (
        title: "Accelerating Time-to-Production for ML at Hinge",
        url: "https://youtu.be/_nsTcYtfnXU",
        conference: "Ray Summit",
        date: "2024-09-30",
    ),
    (
        title: "How Spotify is Navigating an Evolving ML Landscape with Hendrix Platform",
        url: "https://twimlai.com/conf/twimlcon/2022/session/how-spotify-is-navigating-an-evolving-ml-landscape-with-hendrix-platform/",
        conference: "TWIMLcon AI Platforms",
        date: "2022-10-04",
        hide: true,
    ),

    (
        title: "Empowering Traceable and Auditable ML in Production at Spotify with Hendrix",
        url: "https://mlconf.com/sessions/empowering-traceable-and-auditable-ml-in-production-at-spotify-with-hendrix",
        conference: "MLconf",
        date: "2022-03-28",
    ),
    (
        conference: "KubeCon + CloudNativeCon",
        url: "https://www.youtube.com/watch?v=KUyEuY5ZSqI",
        title: "Scaling Kubeflow for Multi-tenancy at Spotify",
        date: "2021-10-07",
    ),
)

#let work = (
    (
        organization: "Hinge",
        url: "https://hinge.co",
        position: "Staff Machine Learning Engineer",
        startDate: "2024-04-15",
        location: "New York, NY",
        blurb: [
            Founding member of the AI Platform Core team. Technical leader across all three of:
            model serving (online; real-time/near-real-time streaming; batch); feature store
            infrastructure and engineering; and agentic system evaluation and governance.
        ],
        highlights: (
            [
                Tech lead for "User Traits," an agentic system leveraging LLMs and VLMs to power
                inferred user understanding. Defined foundational architecture (TensorZero,
                OpenTelemetry, Spark, Unity Catalog) and shared semantic layer. Collaborated with
                development partners across Backend, Data, and AI disciplines to identify use cases
                and coordinate contributions.
            ],
            [
                Laid the foundations of Hinge's centralized agentic AI infrastructure. Successfully
                led and closed out build-or-buy evaluation of vendor/OSS solutions for evaluation
                and prompt engineering/management. Implemented platform based on OpenTelemetry,
                #link("https://github.com/Arize-ai/openinference")[OpenInference], and
                #link("https://docs.arize.com/phoenix")[Phoenix] in collaboration with stakeholders
                across Backend, AI, and Data disciplines. Identified need for centralized LLM
                gateway solution to federate foundation model queries across model porviders and
                query patterns. Led partnership with
                #link("https://www.tensorzero.com/docs")[TensorZero] team on open-source gateway and
                optimization solution.
            ],
            [
                Delivered Hinge's first generally-available model serving platform, HAI Serve, based
                on KubeRay, MLflow, and Spark. Defined the year-long strategy in collaboration with
                applied AI/ML teams. Reduced time-to-production for new models by \~50%.
            ],
        )
    ),
    (
        organization: "Humane Intelligence",
        url: "https://www.humane-intelligence.org/",
        position: "Engineering Advisor",
        startDate: "2024-02-01",
        endDate: "2024-08-30",
        location: "New York, NY",
        simple: true,
        blurb: [
            "Fractional" tech lead and architect. Partner with the CEO to define
            and execute on engineering strategies. Contribute in hands-on
            capacity for technical proofs-of-concepts.
        ],
        highlights: (
            [
                Led 0-to-1 development of platform for crowd-sourced auditing of
                foundation models using Vercel, Vercel AI SDK, Next.js, and the
                Hugging Face API; enabled over 100
                "#link("https://www.humane-intelligence.org/bounty1")[bias
                bounty]" participants to contribute evaluations of open-source
                LLMs.
            ],
            [
                Hired and managed a team of engineering contractors and an
                external development agency; defined, communicated, and
                coordinated development roadmap, led platform evaluations, and
                defined technical ways-of-working for a brand-new team;
            ],
        ),
    ),
    (

        organization: "Spotify",
        position: "Senior Machine Learning Engineer",
        url: none,
        location: "New York, NY",
        startDate: "2021-03-01",
        endDate: "2024-03-04",
        blurb: [
            Senior member of the ML Platform org. Primarily focused on managed
            multi-tenant compute infrastructure for Kubeflow and Ray in
            GCP. Later shifted focus to AI/ML governance and artifact/experiment
            management solutions.
        ],
        highlights: (
            [
                Drove early-stage evaluation and implementation of centralized,
                multi-tenant Ray infrastructure based on KubeRay;
            ],
            [
                Defined general-availability roadmap for
                Kubeflow-based orchestration product, centered on SLOs and SRE
                best practices.
            ],
        ),
    ),
    (
        organization: "NVIDIA",
        position: "Senior Systems Software Engineer, AI Infrastructure",
        location: "New York, NY",
        startDate: "2019-12-01",
        endDate: "2021-01-01",
        url: none,
        blurb: [
            Worked on AI infrastructure for autonomous vehicle research and development.
        ],
),
    (
        organization: "Twitter",
        position: "Machine Learning Software Engineer",
        startDate: "2018-08-01",
        endDate: "2019-12-01",
        url: none,
        location: "New York, NY",
        blurb: [
            Member of Cortex, Twitter's central ML platform organization. Worked
            on: workflow orchestration; experiment management.
        ],
        // highlights: (
        //     [
        //         Enabled distributed training of TensorFlow models in Apache
        //         Mesos from an Airflow pipeline via Deepbird, Twitter's
        //         TensorFlow-based model training/evaluating/serving framework
        //     ],
        // ),
    ),
    (
        organization: "Uber",
        position: "Software Engineer, Observability",
        startDate: "2016-07-01",
        endDate: "2018-07-01",
        location: "New York, NY",
        hide: true,
    ),
    (
        organization: "OkCupid",
        position: "Software Engineer",
        location: "New York, NY",
        startDate: "2015-07-01",
        endDate: "2016-07-01",
        hide: true,
    )
)