Senior Engineering Manager, Data & AI
Alvys · United States
Posted by Alvys on their careers site · View the original posting
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Senior Engineering Manager, Data & AI
Lead the engineering team behind Foundry, Insights, Intel, and Voice: the agents, analytics, and data intelligence of the modern freight platform.
About the Role
Remote, United States. Some travel is required for team onsites, customer visits, and industry events. Relocation assistance is not provided.
The Senior Engineering Manager, Data & AI leads the engineering group behind Foundry, Insights, Intel, and Voice. You'll own the group's technical strategy, delivery, and quality, hire and develop its engineers, and stay hands-on by prototyping, writing evals, reading traces, and reviewing architecture in depth.
The team is about eight engineers today and growing. It also owns the shared foundation those products run on: a context graph that connects loads, trips, carriers, drivers, customers, documents, and conversations; event-sourced platform data streaming into the warehouse; and the evals, tracing, guardrails, and model routing every agent depends on.
Alvys facilitates more than $1 billion in funds every month across invoices, carrier payments, and driver settlements. Behind that volume is one of the richest operational datasets in freight. This team turns it into agents that do real work and intelligence customers can't get anywhere else.
Engineering leaders at Alvys are builders. Expect to spend about a third of your time building. You'll set the standard for how an AI-first team works: coding agents in the daily workflow, evals before scale, and production evidence over demos.
The platform runs on .NET (C#, and increasingly F#), TypeScript, and Python on Azure and AKS, with Cosmos DB, Postgres, Elasticsearch, Snowflake, dbt, and Fivetran underneath.
We're looking for someone who treats AI as a first-class material, both in what we build for customers and in how the engineering team works.
Industry Insight
Freight runs on phone calls, emails, PDFs, and tribal knowledge. The U.S. trucking industry is worth approximately $1 trillion today and is projected to grow by roughly 60% to nearly $1.5 trillion over the next decade.
Yet the work that moves that freight is still largely manual. Nearly 580,000 active U.S. motor carriers operate in a highly fragmented market; 91.5% run 10 or fewer trucks, and 99.3% run 100 or fewer. Few of these businesses will ever build a data team of their own. Every check call answered, rate confirmation read, and exception caught by software is time back for a small team running on thin margins.
Freight data is also structurally messy. A single load spans brokers, carriers, drivers, shippers, and documents, each with its own identifiers, and the truth lives across EDI messages, phone calls, and free text. Generic AI tooling doesn't model any of this. That's why we're building a context graph grounded in how freight actually moves.
Agents can now answer the phone, read documents, query operational data, and take actions inside a TMS. In freight, those actions mov
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t systems. You've built with tool use, function calling, MCP, orchestration, memory, and retrieval, and you understand where agents fail.
- Evaluation discipline. You've built eval systems that combine offline suites, online metrics, human review, and transcript analysis, and used them to gate releases.
- Strong data engineering judgment. You've worked with event streams, CDC, cloud warehouses, dbt, and data quality, and you know how the shape of the data limits what models can do.
- Distributed systems in production. You've designed and operated production services with real reliability requirements.
- Multi-tenant security. You've secured multi-tenant SaaS and isolated customer data, and you treat agent permissions as a security boundary.
- Language fluency. You write Python or TypeScript fluently and are comfortable reading C#, F#, and .NET. Experience with F# or another functional language is a plus.
- Startup-stage fit. You've operated at Series B/C velocity amid ambiguity, thin support, competing priorities, and a bias toward shipping and learning.
- Clear technical communication. You can explain a tradeoff to an engineer, a customer, and an executive.
Preferred Qualifications
- Knowledge graphs, context graphs, entity resolution, or GraphRAG in production.
- Real-time voice agents, including telephony, speech-to-text, text-to-speech, turn-taking, and latency budgets.
- Fine-tuning, reinforcement learning from feedback, or prompt optimization against eval sets.
- Embedded automation or workflow platforms such as Activepieces, n8n, or Temporal.
- SRE experience, or a record of running systems other teams depend on.
- Logistics or freight experience, or another vertical where data is deeply entangled with operations.
- Founder or early-stage engineering leadership experience.
- A history of hiring, growing, and retaining excellent engineers.
Built by people who've moved freight. Come build the intelligence layer with us.