Senior Engineering Manager, Data & AI
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 move trucks and money. They must be correct, auditable, and safe.
About Alvys
Alvys is building the operating system for modern freight. Our AI-powered, cloud-native platform brings dispatch, driver mobile, safety and compliance, billing and settlements, native EDI, reporting, integrations, and automation into one product.
We were founded in 2020 by freight industry insiders who were tired of software built by people who had never moved a load. That combination of deep customer empathy and ambitious technology still defines us. We visit customers, learn how the work actually happens, prototype quickly, debate ideas openly, and turn the best ones into products people rely on.
More than 3,000 trucking and brokerage companies use Alvys today, including larger fleets with increasingly sophisticated operations. We've raised $77M to date, including a $40M Series B. That gives us real momentum and room to make bold bets, while remaining early enough that a great engineering leader can materially shape the product, team, and company.
We are building more than a modern interface for a traditional TMS. Foundry, our agent platform within Alvys, helps freight teams make decisions and complete work, and this team builds it. You'll work with curious, direct, low-ego people who solve hard problems, move quickly from prototype to production, and measure customer impact.
Our Principles
AI makes building cheap. It does not make judgment cheap.
The constraint has moved from writing code to deciding what to build and proving it is right. Agents belong in the middle of the pipeline. People remain accountable at both ends. These principles turn that into how we work every day.
Own the outcome
- You own what you ship. "AI wrote it" is not an answer.
- Judgment stays human. People decide what matters and whether the result is correct.
Verify independently
- Generation and verification are separate. Tests, review, and approval run independently of the agent that did the work.
- Autonomy is earned per workflow. Agents advance only on measured precision against human-verified outcomes. No number, no promotion.
Design for control
- AI-first means security-first. Customer data stays on approved infrastructure under managed identity.
- Cost is an engineering constraint. Every shared workflow has termination conditions and bounded fan-out.
Build durable systems
- Knowledge is tool-agnostic. Reviewed intent lives once; vendor-specific machinery is generated from it.
- Multiple vendors are a design assumption. Durable workflows don't couple to one model or harness.
- Twice is a pattern. Automate repeated work and share it within 48 hours.
The Data & AI Portfolio
The Data & AI portfolio covers Foundry, our embedded automation and agent platform, including a unified agent chat that answers questions and takes actions; Insights, conversational analytics grounded in each customer's own operating data; Intel, proprietary data intelligence drawn from third-party, licensed, and proprietary data sources; and Voice, AI voice and chat agents for load status, driver check calls, and the follow-ups in between. It also includes the shared foundation: the context graph, the data platform that feeds it, and the evals, tracing, guardrails, and model routing behind every agent.
Why This Role
Few engineering leadership roles combine frontier AI work, a proprietary dataset, and an industry this underserved. This one does.
- You build at the frontier, in production. Context graphs, self-learning agents, voice agents, and agentic automation are still being figured out across the industry. Here you ship them to real customers, on live freight operations, not benchmarks.
- You work with data no one else has. More than 3,000 trucking and brokerage companies run on Alvys, and more than $1 billion in funds moves through the platform every month. Combined with third-party and licensed sources, that data is the raw material for intelligence no single fleet or brokerage could build alone.
- Your work changes how an industry operates. With 91.5% of carriers running 10 or fewer trucks, most of our customers will never hire a data team. The agents you build give a small business the leverage of a much larger one.
- You own a portfolio, not a feature. Four products share one platform. You set its architecture, choose its models and vendors, and decide where the team invests as it grows.
- You shape the team from the start. The group is about eight engineers today. You'll hire the next wave and set the standard for how an AI-first engineering team works at Alvys.
- You stay close to the decisions. You report to the Head of Engineering, work directly with customers and our Forward Deployed Engineering team, and have a direct line into company planning, backed by a $40M Series B.
What You'll Be Responsible For
Leading and Growing the Team
- Manage, coach, and develop a team of about eight engineers, including senior engineers and tech leads, and grow it as the portfolio expands.
- Hire senior and staff engineers, and keep the quality bar high as the team scales.
- Run the team AI-first, with coding agents in the daily workflow and clear standards for how AI-written code is reviewed, tested, and shipped.
Owning Technical Strategy
- Set technical direction for agents, data, and the context graph, and make the build, buy, and model calls with cost, quality, and speed in view.
- Make tradeoffs across four products that share one platform, including what gets built, what gets cut, and where the team invests.
Building Agents That Learn
- Own the agent development lifecycle from prototype to production to continuous improvement.
- Build self-learning loops so agents improve from outcomes, user corrections, and transcripts, and make that improvement measurable.
- Make evals a release gate. No agent ships without tests for quality, safety, latency, and cost.
Data and the Context Graph
- Own the context graph that links loads, trips, carriers, drivers, customers, documents, and conversations, and the entity resolution that keeps it trustworthy.
- Partner on the data platform that feeds it, including event streams, CDC into Snowflake, data quality, and lineage, so agents and analytics share one source of truth.
- Capture decision traces (the inputs gathered, the rule applied, any exception, who approved it, and why) so precedent becomes searchable for agents and people, while rules stay explicit.
- Combine third-party, licensed, and proprietary data sources into intelligence customers can act on, with the provenance, licensing terms, and tenant boundaries of each source respected.
Safety, Isolation, and Reliability
- Design agents that change data with scoped permissions, human approvals, and audit trails, and defend against prompt injection and tool misuse.
- Enforce tenant isolation and least privilege across every agent, tool, and data path.
- Own reliability and unit economics. Track cost per task, per call, and per...