Matthew Marshall

• Architect and principal author of aion (~6,200 of 6,450 commits), a declarative, multi-tenant application platform where AI agents are first-class participants under machine-checked contracts, together with the build and deploy substrate it runs on.6,200 commits
• Mechanized the normative specification in Lean 4: a 57-RFC suite reduced to a seven-proposition kernel, with 1,400+ machine-checked theorems across 570 modules covering permission algebra, bitemporal CRDT convergence, safe schema migration, and typed-expression soundness. Proofs run as Bazel test targets, so a spec regression fails CI like any other test.1,400+ proofs
• Built a Kubernetes-native coding-agent fleet: a Go/kubebuilder operator reconciling Agent, AgentTask, AgentChannel, and HumanPrompt custom resources into resumable, S3-checkpointed runner Jobs, coordinated over NATS JetStream with an MCP tool surface, human-in-the-loop resume, and durable spawn/handoff/merge lineage.
• Designed agora, proof-carrying capability auctions layered above MCP: tools publish typed SPARQL BGP capabilities and bid per intent; a second-price Vickrey allocation dispatches the Chandra–Merlin containment witness as typed arguments, closing the NL-to-arguments hallucination gap. Truthfulness, KG consistency, and ledger solvency are machine-checked in Lean.
• Authored rules_lora, Bazel-native LoRA fine-tuning: typed SFT datasets validated at build time, declarative torchtune/peft recipes, a RunPod H100 backend, and an ExpertManifest routing contract, plus a Rust/candle adapter merge validated byte-exact against peft for two-stage fluency-rebase training. Trained a Qwen2.5-1.5B NL-to-BGP parser and an 8-dimensional box embedding for schema.org grounding.mAP 0.982
• Run 10+ kubebuilder operators and 30+ CRDs — instance lifecycle, Aurora provisioning, remote build, and agent coordination — behind a ~60-app ArgoCD app-of-apps on EKS with Karpenter, KEDA, and external-secrets. Merge triggers a hermetic build, OCI image and chart, targetRevision write-back, and self-healing sync with status reported to GitHub Checks.
• Stood up a self-hosted Buildbarn remote-execution and cache plane: cold clone to built in 175 s (vs. ~48-min GitHub Actions baseline), two bzlmod registries serving 100+ rules_* modules behind machine-checked conformance gates, blast-radius-scoped test selection as the merge gate, and SOC 2 evidence emitted as a control-plane byproduct.175 s
• Led SAVVI's first external customer APIs — employer setup and Data Service Interface endpoints — extending OAuth2 to M2M clients and building JSON-Schema tooling to migrate models to Pydantic, with generated schemas backing contract and snapshot test suites.
• Started Savvi Studio (internal onboarding console) and its forge code-generation tooling; added GitLab CI test-result visibility, a container-registry proxy eliminating Docker Hub rate limits, and an end-to-end load test that isolated environment timeout failures.

• Led the development of a microservices-based portfolio benchmarking engine using gRPC and Protobuf, supporting arbitrarily complex composite index calculations, real-time FX conversion, and flexible fund performance comparisons.
• Developed real-time and scheduled exception controls for money market funds using Kafka, enabling users to configure custom compliance alerts pertaining to cash transactions and expense yield impacts across funds and share classes.
• Designed and implemented the NAV Impact Report, giving users visibility into the effect of accounting events on fund yield and NAV over time with a versioned history.
• Authored technical thought leadership detailing code improvements, system architecture, and developer best practices.

• Directed NSF-funded R&D as PI for two grants (1722276 and 1927042) focused on using symbolic AI and optimization to address climate-based financial risk management. Managed personnel, budgets, timelines, and reporting requirements.2 grants
• Designed internal knowledge base of financial and hydrologic data using Apache Jena, crafted ontologies in OWL-DL and demonstrated advanced SPARQL query evaluation capabilities.
• Automated feature engineering using PyData tools to derive descriptive models of customer financial sensitivity to climate.
• Developed cloud-native risk simulation application using Python, Java, and Next.js for configuring and executing stochastic financial forecasts through an intuitive web interface.
• Optimized geospatial clustering algorithm for reinsurance customer, significantly reducing O(n2) runtime complexity for distance matrix calculations by using H3 as a discrete global grid system and parallelizing with TensorFlow.O(n²)

• Advised data engineering team for 2 years at unicorn alternative protein company on data pipeline design in GCP.
• Developed serverless ETL in AWS Lambda for forensic accounting system, analyzing 30M transactions in PostgreSQL.
• Led design and implementation of iOS mHealth app and web portal for nutritionist telehealth platform.

• Engineered performance and query enhancements for portfolio compliance alerts with 3x speed improvement.
• Fixed numerous bugs and stability/performance issues in the IMS C# and Java codebases.
• Greatly expanded unit and integration tests and improved code coverage, quality, and documentation.

• Designed integration testing and quality metrics for critical parts of the fund subscription and redemption processes.
• Deployed automated remote code coverage and instrumentation using WMI on Windows Server.
• Coordinated and led university programming competition; designed and implemented recruitment portal.

Undergraduate research culminated with the development of a mobile app for creating labeled reinforcement learning datasets for wearable sensor data. The app let the user adjust the position of a stylized stick figure so that the activity sensor data could be properly classified. This project preceded the mobile era and was written for Windows CE using several custom graphics libraries and toolkits. Activities: MIT Football, Lambda Chi Alpha.