Founding Machine Learning Engineer

orbit; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

orbit in San Francisco is seeking an experienced ML Research Engineer to contribute to neuroscience-informed AI models and neuroimaging data pipelines. You will design, implement, and evaluate advanced ML approaches that handle multimodal data in real-time settings.

Collaborating with neuroscience, hardware, and software teams, you will drive end-to-end experimentation, iterate quickly on models, and help translate research into scalable systems that power our brain-computer interface platform.

Qualifications

• BS or higher in CS, EE, applied math or related STEM field.
• 3+ years of applied ML research or development.
• Strong Python and ML framework experience (PyTorch, TensorFlow, or JAX).
• Experience with multimodal data and fast-paced teams.

Responsibilities

• Critically evaluate and implement ML approaches for neural data.
• Work with real-time, multimodal datasets.
• Collaborate with neuroscience, hardware, and software teams to co-design end-to-end systems.
• Explore new model architectures and perform experiments and analysis.
• Learn neuroimaging and neuroscience context with internal support.

Skills

ML research

Python programming

Model iteration

Team collaboration

Education

BS or higher in CS/EE/Applied Math

Tools

PyTorch

TensorFlow

JAX

Job description

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