Founding Machine Learning Engineer
Read straight from Orbit Neuro Co.'s careers page, verified 3d ago.Seekless can tailor your résumé to it.
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Before you apply
SalaryNo pay range posted
LocationSan Francisco
Work modeon-site
Typefull-time
SenioritySenior
Experience3+ yrs
DepartmentEngineering
Company size1+ people
PostedAug 13, 2025 · 1y ago
Verified live3d ago
May no longer be actively hiring
Posted by the company on Aug 13, 2025No pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
An BS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or a related STEM field (exceptional self-taught researchers also considered)
3+ years of applied ML research or development experience, or equivalent depth through publications, projects, or startup work
Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX
Built and iterated quickly on ML models and pipelines
Experience with data preprocessing, labeling, and exploratory analysis
Agility working with multimodal data (e.g., imaging + time series, text + audio)
Proven ability to thrive in small, fast-moving teams
Publications in top ML or domain-specific journals/conferences
Experience with biomedical, neuroimaging, or other high-dimensional sensor data
A background in signal processing for time-series or imaging data
Experience with distributed or large-scale training (e.g., mixed precision, very large datasets)
Knowledge of semi-supervised or self-supervised approaches
Excitement to learn neuroimaging and neuroscience context
Skills
Python
PyTorch
TensorFlow
JAX
About the company
We’re a team of engineers, neuroscientists, and designers solving the most difficult and meaningful challenge: understanding the human brain. Our translational brain computer interface and pioneering models decode emotion, putting experience and wellbeing at the center of every interaction.
Our wearable BCI achieves fMRI-comparable resolution untethered to the lab. It’s this advancement that enables us to build foundation models of emotion.
We’re looking for people to help us build and scale. If you want to work on deep technology with real impact, and help define the future of brain-computer interfaces and AI, join us.
We’re backed by the founders and execs of the leading companies in AI, neurotech, consumer hardware and pharmaceuticals - including Google, Hugging Face, Apple, Stability, Microsoft and Dropbox. We’re venture funded.
About the team we are building
We’re building a generational founding team which is truly full-stack - from neural sensors to complex models. If you want to work on deep technological problems and help pioneer the future of NeuroAI, this is the place for you. Projects have opportunities for a high degree of autonomy and demand intense, fast-paced learning.
You will:
•
Critically evaluate and implement the best machine learning approaches for our unique design problems in neural data
•
Work with real-time, multi-dimensional, multimodal datasets
•
Collaborate closely with neuroscience, hardware, and software teams to co-design end-to-end systems
•
Explore new model architectures and perform detailed experimentation and analysis
•
Learn neuroimaging and neuroscience context (we will support you in getting up to speed)
You have:
•
An BS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or a related STEM field (exceptional self-taught researchers also considered)
•
3+ years of applied ML research or development experience, or equivalent depth through publications, projects, or startup work
•
Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX
•
Built and iterated quickly on ML models and pipelines
•
Experience with data preprocessing, labeling, and exploratory analysis
•
Agility working with multimodal data (e.g., imaging + time series, text + audio)
•
Proven ability to thrive in small, fast-moving teams
You might also have:
•
Publications in top ML or domain-specific journals/conferences
•
Experience with biomedical, neuroimaging, or other high-dimensional sensor data
•
A background in signal processing for time-series or imaging data
•
Experience with distributed or large-scale training (e.g., mixed precision, very large datasets)
•
Knowledge of semi-supervised or self-supervised approaches
•
Excitement to learn neuroimaging and neuroscience context