Careers at Native AI Lab X

Build Intelligent Systems. Work With People Who Care.

We are a team of engineers, data scientists, and builders who believe AI should work for everyone. If you are driven by hard problems, honest collaboration, and work that actually ships — you will feel at home here.

"We don't hire for CVs. We hire for curiosity, ownership, and the genuine desire to figure things out — even when nobody has done it before."

Founding Team, Native AI Lab X

WHY JOIN NATIVE AI LAB X

Why People Choose to Work Here

We know you have options. Good engineers, data scientists, and technology professionals always do. So we will not waste your time with generic promises about “exciting challenges” and “dynamic environments.”

Here is what working at Native AI Lab X actually looks like — and why the people who join us tend to stay.

Work That Ships Into the Real World

At Native AI Lab X, you are not building proof-of-concepts that sit in a repository. You are building production systems — ML models that make real decisions, data platforms that real teams depend on, cloud infrastructure that handles real traffic, and software products that real users open every day.

Remote-First, Async-Friendly, Genuinely Flexible

We are a remote-first company. That means:
You work from wherever you do your best work — home, a co-working space, a different city, or a different country. Flexibility is not a benefit we grant — it is how we are designed to operate.

Continuous Learning Is Structural, Not Aspirational

We do not just say we value learning. We build time for it into how we work:
The AI and data landscape moves faster than almost any other field in technology. Staying current is not optional here — and we make sure you have the time and resources to do it.

Your Voice Is Heard and Your Ideas Get Shipped

We have a flat hierarchy. There are no layers of management between a junior engineer and the founding team. When you have an idea — about a client project, about our internal tooling, about how we could do something better — you say it, and it gets discussed on its merits.

A Team You Will Actually Want to Work With

We are honest, direct, and low on ego. We disagree constructively and do not make it personal. We celebrate each other’s work and share credit generously. We hold each other to a high standard because we respect each other enough to do so.

OUR CULTURE

What Our Culture Actually Looks Like Day to Day

How we communicate

Primarily async, through written documentation and structured project channels. We use Slack for quick communication but do not expect instant responses. Decisions are documented in writing so context is not lost to tribal knowledge.

How we make decisions

We default to the person closest to the problem — with full context, autonomy, and accountability. Big decisions are discussed openly before being made, and the reasoning is always documented. We do not make decisions by committee but we do make them transparently.

How we handle mistakes

We have a no-blame culture for honest mistakes made in good faith. We do post-mortems on significant issues — focused entirely on system and process improvements, never on individual fault. The only mistake we take seriously is the one where someone knew something was wrong and did not say so.

How we give feedback

Directly, specifically, and promptly. We do not wait for quarterly reviews to tell someone they are doing exceptional work or that something needs to change. Feedback flows in all directions — junior to senior as freely as senior to junior.

How we celebrate

Loudly and specifically. When a client project delivers exceptional results, when someone clears a certification, when a complex technical problem gets solved elegantly — we name it, share it, and celebrate it as a team.

OPEN POSITIONS

Current Openings

We hire for the following roles on an ongoing basis. Even if a specific role is not currently listed as open, we welcome applications from exceptional candidates — we will always find a way to work with the right person.

AI & Machine Learning

Senior ML Engineer

Nice to have: LLM experience (fine-tuning, RAG, prompt engineering), AWS SageMaker, Hugging Face

ML Engineer (Mid-level)

You should have: 2+ years of ML engineering experience, Solid Python and familiarity with major ML frameworks

Data Engineering & Analytics

Senior Data Engineer

You will architect and build data pipelines, warehouses, and transformation layers for our clients

Data Analyst / BI Developer

You will design and build analytics systems and dashboards that help our clients make better decisions.

Cloud & DevOps

Cloud Solutions Architect (AWS)

You will lead cloud architecture and migration engagements for our clients — designing AWS environments that are secure, cost-efficient, and built for scale.

DevOps / Platform Engineer

You will design and implement CI/CD pipelines, container platforms, and infrastructure automation for our clients

Software Development

Senior Full Stack Engineer

You will lead software development engagements — architecting and building web applications, APIs, and AI-powered product features for our clients.

Backend Engineer (Python / Node.js)

You will build the backend systems, APIs, and integrations that power our clients’ products and internal tools.

DON'T SEE YOUR ROLE?

We Hire Exceptional People, Not Just Open Roles

If you are genuinely excellent at what you do and believe you would thrive at Native AI Lab X — we want to hear from you even if there is no matching role listed above.

We have hired some of our best team members through speculative applications. Tell us what you do, what you are looking for, and why you think Native AI Lab X is the right place for you.

OUR HIRING PROCESS

What to Expect When You Apply

We have designed our hiring process to be respectful of your time, transparent about what we are evaluating, and genuinely two-directional — we want you to be assessing us as much as we are assessing you.

Stage 1 — Application Review (3–5 business days)

We read every application personally. We look at your background, your written answers, and any work you share. If there is a fit worth exploring, we will reach out within 5 business days. If there is not, we will tell you — we do not leave applications in silence.

Stage 2 — Introductory Call (30 minutes)

A relaxed conversation with a member of our team — typically the hiring lead for the role. We will tell you more about the role, the team, and how we work. You will tell us about your background and what you are looking for. We want to understand how you think and whether there is mutual interest — not run you through a scripted interrogation.

Stage 3 — Technical Assessment (2–4 hours depending on role)

A take-home or live technical exercise relevant to the role. We design these to reflect the actual work you would be doing — not abstract puzzles or trick questions. We respect your time: no exercise we give takes longer than it says it will, and we compensate candidates for significant assessment work.

We review and give written feedback on every submission — whether or not we move forward.

Stage 4 — Deep Dive Interview (90 minutes)

A structured conversation with two or three members of the team — covering your technical background, how you approach problems, how you have handled specific situations, and what you are looking for in your next role. There will also be time for your questions — we expect and welcome detailed questions about the role, the team, and the company.

Stage 5 — Offer & References (3–5 business days)

If we want to move forward, we make a verbal offer before a written one — so there are no surprises in the paperwork. We check two references, focused on working style and collaboration rather than generic performance questions.

Services