Careers
At ASP, you won't just build AI systems for clients — you'll use AI to build them. Every engineer here works with frontier AI tools as standard practice. If you want to work at the cutting edge of AI engineering, not just talk about it, this is the place.
See open positionsWhy Engineers Choose ASP
Not enterprise IT maintenance. Not CRUD apps. Not "digital transformation" projects that rename old software. Challenging AI problems that require actual engineering thinking — and a methodology that lets you solve them at speed.
Custom AI models trained on your client's specific data and use case — not fine-tuning prompts in a chat interface
Production ML pipelines handling millions of predictions per day with proper monitoring and retraining
Cloud-native AI infrastructure that scales elastically and costs what it should
Novel solutions to problems that don't have established answers — architecture that's never been built before
Morning sync with your team (collaboration hours across regions)
Pull from sprint board — AI-assisted coding, automated tests running in background
Senior review of yesterday's work — quality gate before anything ships
Deploy to staging, automated integration tests, documentation updates
Hand off to the European team if ending your day — your work continues without you waiting
Not tutorials. Not coursework. Real systems deployed to real users at scale.
You know how to deploy and monitor models, not just train them in notebooks.
Deep learning, NLP, computer vision, or MLOps — you're not a generalist.
You can explain architectural decisions to non-technical stakeholders.
When there's no off-the-shelf answer, you design one.
Comfortable with async communication and distributed collaboration.
We're currently hiring for senior AI engineering roles across all three teams. All positions are remote with flexible hours.
Design and deploy custom ML models for enterprise clients. Focus on NLP, computer vision, or recommendation systems depending on project fit.
Build and maintain ML infrastructure, CI/CD pipelines for models, monitoring systems, and deployment automation.
Lead technical architecture for client engagements. Design novel AI solutions when standard approaches don't fit.
Don't see a fit above? Reach out anyway — we grow the team based on project demand.
If you're a senior engineer tired of enterprise bureaucracy, endless meetings, and working on systems that don't matter — let's talk. We'll tell you exactly what working at ASP looks like, including the parts that aren't for everyone.
You use Claude Code, Cursor, and custom AI tooling on every project. This isn't a perk — it's the operating model. Your productivity is AI-augmented from your first day. You'll ship more, learn faster, and work on higher-value problems because the repetitive work is handled.
Every engagement is a real AI challenge for an enterprise client. No legacy maintenance. No staff augmentation contracts. No building CRUD apps with an AI label. You work on custom ML systems, production NLP pipelines, generative AI applications, and infrastructure that makes AI work at enterprise scale.
Everyone on the engineering team has 10+ years of experience. There's no explaining basic concepts, no mentoring juniors on your project timeline, no carrying less experienced teammates. Your peers push you technically. Code reviews are substantive discussions between experts, not rubber stamps.
You check the handoff summary from the previous timezone team. AI has generated a progress summary from overnight commits and task movements. You review what was done, check any code from the overnight shift, and plan your day.
You work in a human-AI loop. You define requirements, AI generates scaffolding, you review and adjust logic, AI generates tests. The endpoint that would take a full day in a traditional workflow takes a few hours — with tests and documentation already in place.
When you submit a PR, AI tools run automated review first — security scanning, style checking, test coverage. Then a senior engineer examines the logic and architecture. Every piece of code in production has been reviewed by someone who didn't write it.
You write a handoff summary — AI auto-generates most of this from your commit history. You add context and judgment notes. Then you log off, and the next timezone team picks up. Your work doesn't sit idle for 16 hours — it continues.
We hire experienced engineers who want to work differently. Specifically:
You've built and deployed production AI systems, not just trained models in notebooks. You understand the full lifecycle — data, training, deployment, monitoring, iteration.
You already use AI coding assistants daily and have opinions about how to use them effectively. You see AI as a multiplier for your expertise, not a threat to it or a toy to play with.
You can make architecture decisions without waiting for approval, evaluate tradeoffs without a committee, and push back on approaches you think are wrong — including ours.
You write clear handoff summaries, document your decisions, and communicate asynchronously as effectively as synchronously. In a follow-the-sun model, written communication is as important as code.
Build production machine learning systems for enterprise clients
Requirements:
Build production NLP systems for enterprise document intelligence
Requirements:
Build MLOps infrastructure and deployment pipelines
Requirements:
A GitHub repo, a deployed project, a technical blog post, an open-source contribution. We care about your work, not your CV formatting. No cover letters required.
A 60-minute call with a senior engineer. We discuss a real problem — either yours or ours. No whiteboard algorithms, no trick questions, no homework assignments that take a weekend.
Before either of us commits, you work on a real (small) project for 1-2 weeks, paid at your full rate. You experience our workflow. We experience your work. Both sides decide with real information.
No open roles that fit? We're always interested in exceptional AI engineers.
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