Staff Machine Learning Engineer
Engineering & AI Full-time
Build and evaluate machine-learning systems for Orgni and Olyxee's research into organizational systems, with a focus on careful evaluation and responsible deployment.
Compensation
Top-of-band salary, significant equity, performance bonus, compute and learning budget.
A clear, multi-step process. The written application is first; details are below.
Required fields are marked *. You can review what to prepare before applying.
The role
What you'll work on
- 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
Experience
What you'll need
- 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)
Hiring process
What happens next
01 6
Written application
Submit your application. We review it and contact you by email about next steps.
02 6
Founder screen
A 30 minute conversation with the founder. We talk about your trajectory, your work, and how you think.
03 6
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 6
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 6
Final interviews
Three to four conversations with people you would work with most closely. We make sure both sides have everything they need.
06 6
References and offer
We contact your references, then move quickly to a written offer with compensation, equity, and start details spelled out.
Application
Let's get to know your work.
A few focused answers help us understand your experience and what you'd like to do here.
Before you start
Have your CV, LinkedIn, work links and two references ready.