Staff Machine Learning Engineer

Fruition Group US; San Francisco (CA); On-site; USD 200,000 - 260,000; Full time

Job summary

Fruition Group US is partnering with a fast-growing autonomous vehicle company to hire a Staff Machine Learning Research Engineer. You will work on large multimodal models that interpret what the vehicle sees and decide next actions, with end-to-end involvement from dataset shaping to onboard deployment.

The role emphasizes taking models from experiments to real-world tests on vehicles, with options to lead a major research thread, implement training pipelines, and shrink models to fit onboard

Qualifications

• 5+ years building and training deep learning models in PyTorch, TensorFlow, or JAX.
• Experience taking models from training to production.
• Strong knowledge of multimodal models, transformers, diffusion, and flow matching.
• Experience with large-scale or distributed training.
• Background with camera, lidar, radar data; publications or autonomous driving experience is a plus.

Responsibilities

• Build, train, and test vision-language-action models for driving decisions.
• Lead a major research thread, from data and design to large-scale training.
• Create training/evaluation pipelines with rigorous driving-scenario metrics.
• Develop model compression to run onboard on vehicle hardware.
• Apply supervised fine-tuning and RL to improve reasoning and reliability.
• Collaborate with perception, planning, and platform teams to productionize research.

Skills

Deep learning frameworks (PyTorch, TF,

Education

Master's degree in CS/EE/Math/Stats or related field

PhD in related field preferred

Tools

ONNX

TensorRT

Job description

Staff Machine Learning Research Engineer, Multimodal Driving Models

$200,000 - $260,000 base + equity

About the Role

Fruition Group is exclusively partnering with a fast-growing autonomous vehicle company to find a Staff Machine Learning Research Engineer. Our client is building large multimodal models that look at what the vehicle sees and work out what it should do next, and they want strong researchers who can take those models from experiments to running on real vehicles.

This role suits someone who enjoys the full journey: shaping the data, designing and training the model, proving it works in realistic driving conditions, and then making it small and fast enough to run onboard. You’d be working on a problem the industry is only just starting to crack, with the backing and the vehicles to test your ideas.

What You’ll Do

• Build, train, and test vision-language-action models that propose high-level driving choices and guidance for the vehicle’s path.
• Take responsibility for a major research thread, covering the data, the model design, large training runs, and checking results on actual vehicles.
• Create training and evaluation pipelines, along with demanding measures of how well models perform in driving situations.
• Work out ways to shrink large models so they can run on the vehicle’s own computer without losing what makes them useful.
• Use supervised fine-tuning and reinforcement learning after pre-training to improve reasoning, reliability, and handling of unusual situations.
• Work closely with the perception, planning, and platform teams to carry successful research into the production system.

What Our Client Is Looking For

• A master’s degree in computer science, electrical engineering, mathematics, statistics, or a related field; a PhD is a strong advantage.
• 5+ years building and training deep learning models in PyTorch, TensorFlow, or JAX.
• A record of taking models from training and evaluation through to production use.
• A deep grasp of the current landscape: multimodal models, transformers, diffusion, and flow matching.
• Experience with large-scale or distributed training.
• Model compression and inference optimization (quantization, ONNX/TensorRT, mixed precision, custom kernels), post-training of large multimodal models, work with camera, lidar, and radar data, publications at leading ML and robotics conferences, or a background in autonomous driving or driver assistance.