Junior AI/ML Engineer

Netholabs Ltd.; San Francisco (CA); Hybrid; USD 110,000 - 150,000; Full time

Job summary

Netholabs is building AI grounded in biological intelligence. We record petascale neurobehavioural data and use it to train neural foundation models for robotics and embodied AI.

We are seeking an AI/ML engineer to support the research and engineering team across model training, data pipelines, and applied ML work. This is a broad, hands-on role with opportunities to work on active projects and grow into areas that fit you best.

Qualifications

• Strong Python skills and Linux/CLI experience.
• Solid foundation in ML fundamentals from coursework, projects, or research.
• Experience with at least one deep learning framework (PyTorch preferred).
• Experience training ML models on time-series datasets.
• Curious, self-directed, and comfortable with ambiguity in fast-moving research.
• Good communication; able to document work clearly as you go.

Responsibilities

• Support training, fine-tuning, and evaluation of neural foundation models.
• Run experiments, track results, and iterate on model architectures.
• Work with biomechanical data, machine vision data, and robotics control problems.
• Help benchmark model performance and write up findings.
• Build and maintain data pipelines for petascale neurobehavioural datasets.
• Clean, preprocess, and structure multi-modal data for training.
• Help keep experiment tracking, datasets, and compute usage organized.
• Prototype small tools, scripts, and demos to test model capabilities.
• Contribute to internal documentation as work progresses.

Skills

Python

ML fundamentals

PyTorch

Time-series ML

Communication

Job description

Type: Full-time Entry Level Role

Start date: ASAP
Location: SF, USA, remote — On-site/hybrid possible

The successful candidate must be eligible to work in the US or the UK. We are not able to sponsor visas at this time.

The Role

Netholabs is building AI grounded in biological intelligence. We record petascale, high-resolution neurobehavioural data from living systems and use it to train neural foundation models — a new substrate for the next generation of AI, robotics, and personalized intelligence.

We're looking for an AI/ML engineer to support our research and engineering team across model training, data pipelines, and applied ML work. You'll get hands‑on exposure to how a neural foundation model is actually built — from raw neurobehavioural data through to training runs and downstream applications in robotics and embodied AI. This is a broad, hands‑on role: you'll work closely with our research engineers, take on real pieces of active projects, and grow into the areas that fit you best.

Responsibilities

Model Development

• Support training, fine‑tuning, and evaluation of neural foundation models
• Run experiments, track results, and help iterate on model architectures
• Work with biomechanical data, machine vision data, and robotics control problems
• Help benchmark model performance and write up findings

Data & Infrastructure

• Build and maintain data pipelines for petascale neurobehavioural datasets
• Clean, preprocess, and structure multi‑modal data (video, sensor, physiological) for training
• Help keep experiment tracking, datasets, and compute usage organized

Applied ML & Robotics

• Support applications of trained models to robotics and embodied‑agent tasks
• Prototype small tools, scripts, and demos to test model capabilities
• Contribute to internal documentation as work progresses

Requirements

Core (essential)

• Strong Python skills and comfort working in a Linux/command‑line environment
• Solid foundation in ML fundamentals (e.g., through coursework, projects, or research)
• Experience with at least one deep learning framework (PyTorch preferred)
• Experience training ML models on time‑series datasets
• Curious, self‑directed, and comfortable working with ambiguity in a fast‑moving research environment
• Good communication; able to document work clearly as you go

Valued (or willing to learn)

• Experience with machine vision or robotics problems
• Exposure to large‑scale model training or distributed compute
• Experience with data pipelines, structured storage, or large dataset handling
• Familiarity with robotics, sensorimotor learning, or embodied AI
• Background in neuroscience, behavioural science, or related fields

No prior neuroscience or robotics experience required; we will cross‑train the right person.