Staff Software Engineer

Expert Institute•New York, NY

About The Position

We're looking for a Staff Software Engineer to serve as a technical leader and architect for the products powering our two-sided marketplace. You'll set the technical direction for critical systems, drive architectural decisions that shape our platform for years to come, and mentor engineers across the organization. From designing AI-powered document processing pipelines to architecting the data infrastructure behind our case-matching intelligence, you'll operate at the intersection of deep technical craft and high-leverage business impact. This role is for a seasoned engineer who has already done the "senior engineer" job well and is ready to expand their scope—influencing systems, teams, and the technical strategy of the company. You'll partner directly with engineering leadership, Product, and Design to translate ambitious business goals into resilient, scalable technical realities.

Requirements

Seasoned technical leaders who thrive in ambiguity and can turn vague goals into concrete architectures
Deeply curious about AI/ML and actively shipping production systems using modern techniques (LLMs, embeddings, vector DBs, agentic workflows)
Strong generalists with depth in distributed systems, cloud infrastructure, and data engineering
Expert-level in Node.js, TypeScript, and PostgreSQL, with working fluency across additional languages and datastores
8+ years of professional engineering experience, with at least 2+ years operating at a Staff or equivalent level of scope and influence
Deep AWS expertise—you've designed, built, and operated production workloads across compute, storage, networking, and managed services; you understand the trade-offs between ECS vs. EKS vs. Lambda, when to reach for Aurora vs. DynamoDB, and how to keep a cloud bill sane as you scale
Infrastructure-as-code fluency with Terraform, CDK, or Pulumi, and a strong grasp of CI/CD, containerization (Docker), and modern deployment practices
Data engineering chops: you've designed production data models, built reliable pipelines (Airflow, dbt, Kafka, or equivalent), and shipped warehouse-backed analytics or ML features
Architectural range: you can zoom from individual service design up to multi-system architecture, and you have strong opinions—loosely held—about microservices vs. monoliths, event-driven design, API design, and data consistency models
AI/ML production experience: you've shipped features backed by LLMs, embedding models, or traditional ML, and you understand the infrastructure, evaluation, and cost considerations that come with them
Outstanding communication skills—you can write a crisp RFC, run a productive design review, explain a complex system to a non-technical stakeholder, and give feedback that actually changes behavior
A track record of mentorship and elevating the engineers around you
Strong product intuition—you push back on bad specs, you understand why we're building what we're building, and you use that context to make better technical decisions
A bias to action balanced with the judgment to know when to slow down and design carefully
Deep interest in our product, our customers, and the legal industry's transformation through technology

Responsibilities

Architect and lead the design of complex, distributed systems that power our marketplace at scale, including our AI/ML infrastructure, search and matching services, and data platform
Drive technical strategy across multiple teams and services—setting standards for reliability, performance, security, and developer experience
Own our AWS cloud infrastructure end-to-end: VPC design, compute (ECS/EKS/Lambda), data stores (RDS, DynamoDB, S3), networking, IAM, observability, and cost optimization
Design and evolve our data platform—including data modeling, ETL/ELT pipelines, warehousing (e.g., Redshift/Snowflake), streaming architectures, and the data foundations that power analytics and ML
Lead the technical design of AI-powered features, including LLM-backed document processing, embeddings/vector search for expert matching, RAG pipelines, and evaluation frameworks
Establish engineering excellence: define coding standards, CI/CD practices, testing strategies, incident response processes, and operational maturity across the org
Mentor engineers at all levels—through code review, design review, pairing, and career coaching—raising the bar for the entire team
Partner with leadership on technical roadmap, hiring, org design, and build-vs-buy decisions
Own critical production systems including on-call rotation leadership, SLO definition, and driving postmortems that translate incidents into lasting improvements
Identify and resolve systemic performance, scalability, and reliability bottlenecks before they become customer-facing problems