Forward Deployed Engineer

Neuroscale AI•Reston, VA

8d•$125,000 - $180,000•Hybrid

About The Position

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’re now hiring a Forward Deployed Engineer to help accelerate adoption of Arbi in both federal & commercial space to help us dominate one of the largest and fastest-expanding categories in modern software. This is not a maintenance role. This is a hands-on, high-ownership opportunity for someone who has operated at a full-stack engineer, architect, or founding/lead engineer level and knows how to deploy production-grade systems that scale. As a Forward Deployed Engineer at Neuroscale AI, you will be the technical bridge between our customers and our product. You will work directly with customers to understand their recruiting workflows, configure and deploy Arbi, integrate with their existing systems, and build production-ready solutions that deliver measurable impact. This is a hands-on engineering role with significant customer exposure. You will write code, design integrations, build prototypes, configure AI workflows, troubleshoot deployments, and work closely with product and engineering to turn customer feedback into scalable product improvements. This role is ideal for an entrepreneurial engineer who enjoys the intersection of software engineering, AI implementation, solution architecture, technical consulting, and customer success.

Requirements

5+ years of experience in software engineering, solution engineering, technical consulting, customer-facing engineering, implementation engineering, or forward deployed engineering.
Strong hands-on programming experience with Python, TypeScript, JavaScript, Node.js, or similar modern languages.
Experience building or integrating production software systems using REST APIs, webhooks, OAuth, SSO/SAML, databases, cloud services, and enterprise application integrations.
Experience working with cloud environments such as AWS, Azure, or GCP.
Strong understanding of modern AI/ML and GenAI concepts, including LLMs, RAG, embeddings, vector search, prompt engineering, evaluation, and workflow orchestration.
Ability to work directly with enterprise IT and security teams on authentication, access control, logging, data privacy, data residency, and deployment requirements.
Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
Strong ownership mindset, customer empathy, and ability to operate in ambiguous, fast-moving environments.
Willingness to travel for customer workshops, onsite deployments, executive briefings, and go-lives.

Nice To Haves

Experience deploying AI, SaaS, HR Tech, recruiting tech, workflow automation, document intelligence, or enterprise analytics platforms.
Experience integrating with systems such as Workday, Greenhouse, Lever, iCIMS, Bullhorn, Salesforce, HubSpot, ServiceNow, Microsoft 365, Google Workspace, or similar enterprise platforms.
Experience with databases and search systems such as PostgreSQL, Redis, OpenSearch, Elasticsearch, Pinecone, Weaviate, or other vector databases.
Experience with Docker, Kubernetes, CI/CD pipelines, observability tools, and production monitoring.
Experience in public sector, federal, higher education, staffing, HR, talent acquisition, or workforce development environments.
Prior experience at an early-stage startup or in a high-growth customer-facing technical role.

Responsibilities

Own customer deployments from discovery through go-live, including workflow mapping, solution design, implementation, testing, launch, and post-launch optimization.
Work directly with recruiting leaders, HR teams, talent acquisition operations, IT/security teams, and executive stakeholders to understand business goals and translate them into technical solutions.
Configure and customize Arbi workflows for use cases such as resume screening, candidate matching, recruiter copilots, automated outreach, interview workflows, compliance review, talent pipeline analytics, and enterprise reporting.
Build and maintain integrations with ATS, HRIS, CRM, LMS, identity providers, email/calendar systems, job boards, and customer data environments.
Support production rollouts for enterprise, public sector, and higher-education customers.
Design and implement AI-powered workflows using LLMs, RAG, structured evaluation rubrics, candidate scoring logic, document intelligence, and agentic automation.
Help customers define evaluation criteria, scoring rubrics, workflow automation rules, and human-in-the-loop review patterns.
Work with product and ML teams to improve prompt chains, model evaluation, retrieval quality, explainability, auditability, and customer-specific performance.
Support safe, reliable, and explainable AI deployments with clear logging, traceability, monitoring, and customer-facing outputs.
Serve as the voice of the customer to the product and engineering teams.
Identify repeatable customer patterns and turn them into reusable templates, product features, implementation accelerators, and reference architectures.
Write clear technical documentation, deployment guides, integration notes, customer playbooks, and implementation best practices.
Partner with sales, customer success, product, and engineering teams during pilots, demos, enterprise evaluations, renewals, and expansions.
Troubleshoot integration issues, API failures, data mapping problems, authentication flows, permissions, system performance, and customer environment constraints.
Rapidly prototype solutions when customer requirements are ambiguous or evolving.
Make practical engineering tradeoffs to deliver customer outcomes quickly without introducing unnecessary complexity.