Level

Harvey

Engineering Manager, Model Infrastructure

Harvey is hiring an Engineering Manager, Model Infrastructure for a remote role open to applicants in United States. It pays $260k-$350k a year and Level rates it ; you can apply on Harvey.

Level's read on this job

  • Level rates this role Builds AI ●●●●: the job is building AI systems.
  • This is listed as a remote role.
  • The posting mentions OpenAI, Anthropic and Azure OpenAI among its tools and skills.
  • Level classes it as a senior role.
  • Harvey has 357 other open roles on Level.

AI in this role

openaianthropicazure-openai
ai-research
Why Harvey

At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.

This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.

Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.

At Harvey, the future of professional services is being written today — and we’re just getting started.

Role Overview

As the Engineering Manager for Model Infrastructure, you'll lead the team responsible for the platform powering every model request across Harvey. You'll partner closely with AI Research, Product Engineering, Infrastructure, and external AI providers to ensure our platform remains reliable, scalable, and cost-efficient as our business grows.

Model Infrastructure is one of Harvey's most strategic engineering organizations. Every product capability—from chat experiences and agents to document workflows and future reasoning systems—depends on this platform.

Over the next several years, the team will evolve beyond operating third-party models to building the infrastructure that enables Harvey to train, evaluate, deploy, and operate our own frontier AI models. This role offers the opportunity to shape the technical foundation of Harvey's AI platform and build an organization that will power the company's next phase of growth.

What You'll Do
  • Lead and grow a high-performing team of software engineers responsible for Harvey's Model Infrastructure platform.

  • Define the technical roadmap for model reliability, scalability, and operational excellence.

  • Build highly reliable systems for model provisioning, capacity management, failover, and incident response across multiple AI providers.

  • Own Harvey's multi-provider model platform, including provider integrations, SDK upgrades, API migrations, and onboarding new model providers.

  • Drive the evolution of our Unified Model Controller (UMC) and Model Selector platform to automatically detect degraded models and intelligently route traffic based on health, latency, quality, compliance, and cost.

  • Improve observability through health dashboards, alerting, token usage analytics, cost reporting, and end-to-end model telemetry.

  • Partner with Product Engineering to support new model launches, capacity planning, experimentation, and proactive production monitoring.

  • Lead initiatives to improve inference efficiency, reduce infrastructure costs, and increase model utilization across providers.

  • Build the infrastructure foundation for Harvey's future model training efforts, including data pipelines, model operations, training environments, and AI platform capabilities.

  • Partner with executive leadership on long-term AI infrastructure strategy and vendor relationships.

  • Recruit, mentor, and develop exceptional engineering talent while fostering a culture of technical excellence and operational ownership.

What You Have
  • 8+ years of software engineering experience, including multiple years managing high-performing engineering teams.

  • Experience leading teams responsible for large-scale distributed systems or cloud infrastructure.

  • Strong technical background that enables you to guide architectural decisions and mentor senior engineers.

  • Experience operating highly available production services with strong reliability and operational excellence.

  • Experience building platforms that require scalability, observability, automation, and cost optimization.

  • Strong cross-functional leadership skills with the ability to partner effectively across Engineering, Research, Product, and external vendors.

  • Excellent communication skills and the ability to influence technical strategy across organizations.

  • A passion for building teams and developing engineering talent.

Nice to Have

  • Experience with AI infrastructure, LLM serving, or machine learning platforms.

  • Experience working with multiple model providers such as OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, or open-source model ecosystems.

  • Experience building inference platforms, model gateways, traffic routing systems, or policy-based serving infrastructure.

  • Experience with Kubernetes, cloud infrastructure, distributed systems, and large-scale observability platforms.

  • Experience supporting GPU infrastructure, model training platforms, or ML infrastructure.

  • Familiarity with data platforms and technologies such as Spark, Kafka, Flink, Airflow, or Iceberg.

  • Experience leading organizations through periods of rapid growth and technical transformation.

Compensation

$260,000 - $350,000 USD

Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices here.

#LI-AN2

Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

How we rate this

Engineering Manager, Model Infrastructure at Harvey rates 91 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI ResearchOpenAIAnthropicAzure OpenAI

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. Walk me through how you've used OpenAI in your day-to-day work.
  3. What are the limits of Anthropic that you've run into, and how did you work around them?
  4. What's a project where you used Azure OpenAI hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: AI Research, OpenAI, Anthropic, and Azure OpenAI. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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