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Open nowPosted 202 days ago

Founding Data Engineer • PG/Opensearch

Neuroscale AI9 open roles

Pay
$100,000 – $200,000 a year
Where
Remote
Work mode
Remote
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Your applicationOpen nowFounding Data Engineer • PG/OpensearchNeuroscale AI · Remote

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Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 202 days ago

The posting

About Neuroscale

Neuroscale is building the AI recruiting operating system — one unified platform for your entire talent organization, from first touch to hired. Arbi eliminates fragmented workflows, integrates directly with your ATS, and orchestrates AI agents for sourcing, outreach, screening, and candidate intelligence end-to-end. By combining reasoning-optimized models with deep workflow automation, Arbi helps talent teams win more top candidates with dramatically less friction.

We're backed by top operators and investors, and we're growing fast. Our momentum is further fueled by blue-chip partners and programs, including the NVIDIA Inception Program, the HPE Unleash AI Program, and our active DoD SBIR Phase II award and contract.

We are looking for a Founding Data Engineer who can build the data backbone of the platform and help us scale reliably across ingestion, transformation, indexing, storage, and retrieval workflows. This is not a maintenance role. This is a hands-on, high-ownership opportunity for someone who has operated at a staff engineer, architect, or founding/lead engineer level and knows how to build production-grade systems that scale. You will work on the backbone of our platform: APIs, orchestration systems, data workflows, search infrastructure, background jobs, cloud deployment, and backend systems that power AI-enabled products for real customers.

Role Overview

This is a hands-on founding engineering role for someone who knows how to design, build, and operate production-grade data systems. You will work closely with backend engineers, AI engineers, and product leaders to create the data infrastructure that powers search, analytics, and AI-driven product experiences.

You should be comfortable working in a fast-moving startup environment, making pragmatic technical decisions, and owning systems end to end.

What You'll Do

- Design, build, and maintain scalable batch and streaming data pipelines that support Neuroscale AI's core platform

- Build robust ingestion, transformation, enrichment, and indexing workflows across structured, semi-structured, and document-centric data

- Develop and operate production-grade data systems using PostgreSQL, OpenSearch/Elasticsearch, AWS, and Python-based tooling

- Design efficient data models, schemas, and storage patterns that support analytics, search, application workflows, and AI use cases

- Build secure cloud-native data infrastructure using AWS services such as S3, Lambda, Glue, Kinesis, and IAM

- Optimize PostgreSQL for advanced SQL workloads, replication, query performance, and data integrity

- Design and manage search and retrieval pipelines in OpenSearch/Elasticsearch for high-speed, relevant access to data

- Improve observability, lineage, testing, and reliability across the data platform

- Automate infrastructure provisioning and environment management using Terraform or CloudFormation

- Partner closely with backend, product, and AI teams to enable new data-driven capabilities and platform features

- Help define engineering standards, data platform best practices, and operational playbooks as the company scales

Requirements

- Strong experience in data engineering or backend/data platform engineering in production environments

- Strong programming skills in Python; experience with Typescript is a plus

- Deep hands-on experience with PostgreSQL, including advanced SQL, schema design, query tuning, indexes, sharding, replication, and modeling

- Strong experience with OpenSearch/Elasticsearch, including indexing strategy, search performance, relevance tuning, and distributed query operations at very large scale

- Experience building and maintaining ETL/ELT pipelines and data processing workflows for large-scale datasets

- Hands-on experience with AWS data and infrastructure services, especially S3, Lambda, Glue, Kinesis, and IAM

- Experience designing reliable cloud-native data architectures and secure data movement patterns

- Experience with Infrastructure as Code, ideally Terraform or CloudFormation

- Strong understanding of distributed systems, production operations, fault tolerance, and data reliability

- Ability to work with high ownership, move quickly, and make sound engineering decisions in a startup environment

Nice to Have

- Experience with Kafka, event-driven systems, or other streaming architectures

- Experience supporting vector search, semantic retrieval, or AI/ML data pipelines

- Experience with workflow orchestration, async processing, and integration-heavy platforms

- Experience with containerized environments, CI/CD pipelines, and production monitoring

- Experience working on document-centric or AI-enabled platforms

Compensation & Benefits

- Base Salary: $100,000 – $200,000

- Equity: ~0.1–0.75% early-stage equity with a clear range shared during the process

- Bonus: Quarterly performance bonuses tied to clear feature shipping targets

- Healthcare: Medical, dental, and vision coverage

- PTO: 14 days accrued annually

- Learning: $2,000+ per year for courses, conferences, books, and communities

- Equipment: New MacBook Pro, monitor, and a monthly tools budget

- Flexibility: Flexible hours; ideally in-person in the Northern Virginia / Washington DC region (remote considered)

- Growth: Clear, fast-track path to Engineering Lead as the team scales

Why Join Neuroscale AI

- Founding role with significant ownership over architecture, tooling, and platform decisions

- Direct exposure to company leadership with real influence over product direction

- Work on modern AI, search, and data infrastructure problems with direct customer impact

- Build systems from day one — no legacy architecture to inherit

- Zero bureaucracy, high trust, and a strong culture of ownership and technical excellence

- A seat at the table as we scale our product, team, and engineering culture

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