ML Software Engineer

Remote; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Remote in San Francisco is seeking an ML/AI systems engineer to build the software backbone for autonomous-model data pipelines and evaluation workflows. You will own end-to-end model code paths, from input tokenization to deployment, and craft tooling for metrics, observability, and low-latency serving.

The role requires solid Python, production-grade coding, and experience with ML frameworks (PyTorch/TensorFlow/JAX) plus MLOps and CI/CD.

Qualifications

• MS in CS/ML/Robotics or BS with ≥2 years building ML/data/evaluation systems
• Strong Python fundamentals and production-quality code experience
• Experience building ML data and evaluation pipelines
• Experience with PyTorch, TensorFlow, or JAX
• Experience with large multimodal datasets and data integrity checks
• Familiarity with MLOps, CI/CD, and observability
• Excellent communication and ownership in a small team

Responsibilities

• Design and ship multimodal data pipelines and reproducible training/evaluation workflows
• Implement and iterate on LLM, VLM, and VLA architectures; own model code paths, input/tokenization, inference
• Integrate simulators for closed-loop evaluation; build tooling for metrics, visualization, and experiment management
• Deliver production-grade serving and inference tooling for deterministic, low-latency operation
• Own systems from scratch: architecture - implementation - testing - documentation - iteration

Skills

Python

Production-grade code

ML pipelines

Training & serving

MLOps

Communication

Education

MS in CS/ML/Robotics or BS + ≥2 years

Tools

PyTorch

TensorFlow

JAX

CI/CD

Job description

About us

We're building the next generation of ground transportation with advanced physical AI to simplify the toughest challenges in modern freight. Our stealth team, founded by the engineers who scaled autonomous driving, is developing an entirely new vehicle platform. We move fast, stay tightly aligned between engineering and product, and focus on creating reliable, real-world autonomous systems.

What You'll Do

• Build the software backbone for autonomy-focused foundation models: design and ship multimodal data pipelines (ingest, validate, shard, package) and reproducible training/evaluation workflows (manifests, checkpoints, failure handling).

• Implement and iterate on LLM, VLM, and VLA architectures; own model code paths, input/tokenization, inference runners, and output heads for downstream consumers.

• Integrate and operate simulators for closed-loop evaluation; build tooling for metrics, visualization, and experiment management.

• Deliver production-grade serving and inference tooling for deterministic, low-latency operation on bench/mule and eventual vehicle deployments.

• Own systems from scratch: architecture - implementation - testing - documentation - iteration; raise the bar on code quality, reliability, and observability.

What We're Looking For

• Education & Experience: MS in CS/ML/Robotics or BS + ≥2 years building ML/data/evaluation systems

• Software engineering excellence: Strong Python fundamentals (data structures, testing, debugging, modular design) and a track record of shipping production-quality code/APIs and reliable automation.

• Pipelines - Training - Serving: Demonstrated experience building data/ML pipelines and evaluation tooling, and integrating training and inference using PyTorch, TensorFlow, or JAX.

• Datasets at scale: Dataset packaging, sharding, manifest formats, and integrity checks for large multimodal datasets.

• Performance & optimization: Practical work improving training/inference throughput and latency (e.g., mixed precision, efficient batching, model parallelism).

• MLOps & infrastructure: Cloud storage and training workflows, containerization, CI/CD, and experiment observability (tracking, logging, metrics).

• Team fit: Strong communication, collaborative with research and engineering partners, and a bias for ownership/independence in a small, fast-moving team.

• Nice to have: Prior work on perception, detection, or multimodal models.