Build and ship live and game features across the full stack using AI coding tools, contributing to a velocity that redefines what a small engineering team can deliver.
Compensation
$178,000 –$223,000 base + equity + benefits
Responsibilities
Build and ship live and game features across the full stack using AI coding tools
Take ownership of your features end to end, from development through deployment, using AI-facilitated QA processes and tooling to maintain high engineering quality
Write clean, reliable, and performant code with a strong sense of accountability for what you ship in production
Collaborate closely with your teammates, contributing to a culture of high standards, fast iteration, and continuous learning
A part-time contract (~5-10 hrs/week for ~12 weeks) supporting learners in an AI-augmented engineering bootcamp at a large regulated insurer. Review code, give feedback, run office hours, and keep engineers on track.
Responsibilities
Review learners' AI-assisted code, specs, and governance artifacts — give constructive, specific feedback
Catch where learners are stuck or falling behind and intervene early
Run virtual office hours for Q&A and hands-on help
Evaluate not just 'does the code work' but 'did the engineer use AI well' — judgment, not just output
Requirements
Nice to have: Mentoring
Nice to have: Code review
Nice to have: Bootcamp TA experience
Nice to have: Experience in a regulated or enterprise environment
Nice to have: Familiarity with spec-driven development
The AI Capabilities Intern – AI Architecture, Enablement & Tools will join the AI Capabilities & Governance team within Zoetis Tech & Digital (ZTD) to support the enterprise generative AI platform, evaluate and enable new AI tools, and document AI architecture and integration patterns.
Locations (3)
Kalamazoo, Michigan, United States
Parsippany, New Jersey, United States
United States
Compensation
The following hourly pay rates reflect the anticipated base pay for this position:
Associate-level degree: $16.00/hour
Undergraduate-level degree: $22.00/hour
Graduate-level degree: $32.50/hour
Doctorate-level degree: $36.00/hour
For candidates located in California, Connecticut, District of Columbia, Illinois (Chicago area), Massachusetts, New Jersey, New York, or Washington (Seattle area), the anticipated hourly base pay rates are:
Associate-level degree: $17.00/hour
Undergraduate-level degree: $24.20/hour
Graduate-level degree: $36.40/hour
Doctorate-level degree: $40.30/hour
Responsibilities
Partner with business users to identify, document, and implement AI use cases within the enterprise generative AI platform.
As a Full-Stack Developer, you'll join one of our development squads and help drive the evolution of our products using a stack including React, Next.js, Typescript, and AWS/GCP.
Locations (2)
Stockholm, Stockholm County, Sweden
Sheffield, South Yorkshire, United Kingdom
Responsibilities
supporting colleagues through code reviews
knowledge sharing sessions
coaching
evaluating new technologies to keep us efficient and scalable
writing code that raises the bar for what 'good' looks like
Build and ship features for an AI-enabled revenue cycle management platform, including full-stack development, event-driven services, integrations, and AI agent orchestration.
Locations (2)
Chicago, Illinois, United States
United States
Compensation
The target base compensation for this position is $130,000 to $160,000 USD per year in most US locations. Final offer amounts are determined by multiple factors including location, local market variances, and candidate experience and expertise, and may vary from the amounts listed above.
Responsibilities
Build and ship features across a full-stack RCM worklist platform, from the user interface through backend services, APIs, and the data model
Develop event-driven services and workflows that route, prioritize, and track work across people and AI agents
Help build the shared automation foundation: the queues, state, and handoff logic that let an AI agent pick up a work item, finish it, or hand it back to a person, along with audit history, approval gates, and access controls for agent actions
Build and maintain integrations that connect the platform with EverHealth products, our clearinghouse, and third-party systems
Project Manager, AI Model Enablement and Agentic SDLC
Lead and coordinate AI model enablement and agentic development initiatives across engineering, security, data science, legal, and IT to drive adoption and improve program management processes.
Compensation
The base salary range for this position for all U.S. candidates is $85,000 - $120,000 per year, with eligibility for bonuses, equity grants and a comprehensive benefits package
Responsibilities
Lead end-to-end execution of TechOps AI initiatives
Plan and coordinate model access, evaluation, and approval processes
Align cross-functional teams and act as bridge between technical and non-technical stakeholders
Own status reporting and communication for small to mid-size projects
Drive adoption of new AI-assisted and agentic ways of working
Lead the design, build, and maintenance of backend services and frontend components for a multi‑tenant SaaS platform that connects utilities with consumer platforms.
Responsibilities
Design, build, and maintain scalable APIs and backend services with TypeScript and Node.js/NestJS.
Contribute to frontend development with TypeScript and React.
Deliver reusable, well‑tested code.
Build integrations connecting utility systems with external consumer platforms.
Develop secure data exchange services within a multi‑tenant SaaS architecture.
Focused on building demand generation and account-based marketing (ABM) programs to help win and expand strategic accounts for SpaceXSI. This is an execution-heavy role involving copy writing, program execution, and data-driven optimization.
Compensation
Annual compensation range for this role is listed below. $144,000 - $176,000 USD
Responsibilities
Build and run programs for priority accounts and segments
Design and run ABM programs for clusters of strategic accounts grouped by vertical, tech stack, use case, or buying-group pattern
Scale what works into 1:many programmatic plays across a broader target list of prospects and customers
Map buying centers inside each account and build multithreaded plays
Turn account intelligence, intent, and usage signals into tailored campaign treatments
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