Engineering & AI · Paid role

Staff Machine Learning Engineer

Own the models behind Orgni and its operational workflows, financial operations, and document integrity capabilities end to end: training, evaluation, and reliable serving at production scale.

Engineering & AIRemoteStaff · 8+ years building ML systems in production

Compensation

Top-of-band salary, significant equity, performance bonus, compute and learning budget.

What you'll own

Own the full lifecycle of production models: data, training, evaluation, and serving
Push the reliability, latency, and cost of model inference at scale
Set the standard for how the team evaluates and monitors model behavior
Partner with research to move new methods into shipped product
Mentor engineers and raise the ML engineering bar across the company

Who we're looking for

8+ years building and operating ML systems in production, with 3+ at a senior or staff level
First-author publications or a public Google Scholar / arXiv profile we can review
Expert Python and deep experience with PyTorch or JAX on real training and serving workloads
Proven track record deploying large models with strict latency, cost, and reliability targets
Relevant certifications are a plus (AWS/GCP ML specialty, NVIDIA DLI, or equivalent)
Strong systems fundamentals; you can own the path from notebook to production service
Exceptional written communication and a body of public work (open-source, papers, or talks)

How we'll evaluate you

01
Written application
Submit the full application below. We read every word. Expect a response within 14 days, even if it is a no.
02
Founder screen
A 30 minute conversation with the founder. We talk about your trajectory, your work, and how you think.
03
Take-home exercise
A paid, role-specific exercise that takes 6 to 10 hours over a week. We pay market rate for your time on this.
04
Technical deep dive
Two hours with two people from the team. We go deep on your exercise, your past work, and a live problem in your domain.
05
Final interviews
Three to four conversations with people you would work with most closely. We make sure both sides have everything they need.
06
References and offer
We contact your references, then move quickly to a written offer with compensation, equity, and start details spelled out.

Apply

The bar is high. Take your time.

Plan for 30 to 45 minutes. Strong applications are specific, written in your own voice, and show real proof of work. Vague answers are the most common reason we say no.

First name *

Surname *

Email *

Phone number *

Where are you based? *

LinkedIn profile *

GitHub, portfolio, or other relevant work *

Google Scholar, arXiv, or publications profile

Required for research, ML, and data science roles. Leave blank if it does not apply.

Relevant certifications or credentials

List any professional certifications relevant to this role.

Link to your CV *

Make sure the link is publicly viewable.

Current role and company *

Years of relevant experience *

Describe a project you led end to end that you are most proud of *

Be specific. Vague answers are the most common reason we say no.

Why this role and why Olyxee? *

Describe the hardest technical or strategic problem you have solved in the last 12 months *

Two professional references (name, relationship, email) *

Salary expectation (annual, in your local currency) *

Earliest start date *

Work authorization *

I confirm that everything in this application is accurate and written by me. I understand Olyxee will verify references and may conduct background checks.

Your application is sent directly to our hiring team. No third parties.