Machine Learning Engineer

Netholabs Ltd.; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Netholabs Ltd. is seeking a Senior Machine Learning Engineer to design, train, and iterate on foundation models of brain and behavior across species, using large-scale multimodal neural and behavioral data.

You will own core modeling work, including architecture, distributed training, and representation learning, and you’ll collaborate with the data and research teams to turn findings into production-grade code.

Qualifications

• Trained large deep learning models end-to-end in production or research settings.
• Experience training transformer or other large sequence models with distributed training.
• Strong Python and PyTorch (or JAX) fundamentals with reproducible code.
• Experience working with large, multimodal or time-series data.
• Pragmatic in early-stage environments owning end-to-end work.

Responsibilities

• Design, train, and iterate on large generative models over multimodal data.
• Own training at scale: data loading, distributed training, hyperparameters, evaluation.
• Develop representations that cross species and modalities.
• Turn research findings into production-ready, reproducible code.
• Collaborate with data engineering and research teams on roadmap.

Skills

Deep learning

Transformer models

Python

PyTorch

Distributed training

Time-series data handling

Reproducible model code

JAX

Tools

JAX

Job description

Senior Machine Learning Engineer (Foundation Models)

Type: Full-time

Location: US or UK

At this time we are only able to hire candidates who are eligible to work in the US or the UK.

The Role

We're building a foundation model of the brain and behavior across species, trained on large-scale multimodal neural and behavioral data, and this role is central to designing and training it. You'll work on the core generative model: architecture, training at scale, and representation learning across neural signals and behavior, along with the research questions that come with modeling biological data as sequences. You'll join a small team and work alongside our existing ML engineer, with room to shape the modeling direction as we grow. This is early-stage scope, so you'll train greenfield models, own parts of the stack, and see your work define the company's core asset.

Responsibilities

Model development and training

• Design, train, and iterate on large generative (recurrent or transformer-based) models over multimodal neural and behavioral data

• Own training at scale: data loading, distributed training, hyperparameter optimization, and evaluation

• Develop representations that capture structure across species and modalities

• Train models on animal and human behavioral data as well as direct neural data

Research and evaluation

• Define and run experiments to test modeling choices, and build the evaluation that tells us whether the model is learning what we need

• Draw on the neuroscience and sequence-modeling literature to inform architecture and training

• Turn research findings into reproducible, production-quality model code

Collaboration

• Partner with the data engineering team on data readiness and with the research team on what the model needs to capture

• Contribute to the shared modeling roadmap alongside our existing ML engineer

Requirements

Core (essential)

• You’ve trained large deep learning models end to end, in production or research settings

• Hands-on experience training transformer or other large sequence models, including distributed training and scaling

• Solid software fundamentals: Python and PyTorch (or JAX), and the discipline to write reproducible model code

• Comfort working with large, messy, multimodal or time-series data

• Pragmatism for an early-stage environment where you own work from end to end

Valued

• Enthusiasm for the science of modeling biological data and the intersection of the brain and AI

• Familiarity with representation learning and self-supervised or generative modeling

• Background or strong interest in neuroscience, biosignals, or computational cognitive science

• Experience with hyperparameter optimization, training infrastructure, or evaluation frameworks

• Publications or open-source contributions in relevant areas