Senior/Staff Research Engineer — Vision-Language-Action Models (Autonomous Driving)

Plus 2
Santa Clara, CA
Hybrid

Who this role is best for

Best suited to candidates with advanced degrees in CS/EE and hands-on experience in training vision-language-action models, working in autonomous driving and collaborating across cross-functional teams.

Best fit for

  • Candidates with advanced degrees in CS/EE and VLA model development experience
    — “M.S. minimum, Ph.D. preferred in CS, EE, Mathematics, Statistics, or a related field.”
  • Individuals with a track record in deploying models in production environments
    — “Hands-on experience with model training, evaluation, and deployment in production.”
  • Professionals skilled in large-scale distributed training and model compression techniques
    — “Experience with large-scale / distributed model training.”

Things to consider

  • There is a strong emphasis on transitioning research into production systems
    — “Collaborate with perception, planning, and platform teams to bring models from research to production.”

How to stand out

  • Highlight end-to-end project ownership and production deployment experience in your resume
    — “Own a VLA workstream end to end — data, architecture, large-scale training, and on-vehicle validation.”
  • Emphasize experience with model compression and on-board deployment techniques
    — “Develop distillation and compression recipes to deploy large reasoning models on on-board compute.”
  • Showcase knowledge of state-of-the-art VLA models and training frameworks
    — “Thorough understanding of state-of-the-art vision-language / VLA models, diffusion, flow matching, and transformers.”
  • Demonstrate expertise in training models using SFT and RL post-training methods
    — “Apply SFT and RL post-training to improve reasoning, robustness, and long-tail behavior.”
  • Demonstrate experience with multi-modal sensor data in your application materials
    — “Hands-on experience with multi-modal sensor data (camera, LiDAR, radar).”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • model training and evaluation
  • on-vehicle validation
  • collaboration with engineering teams
Typical background
AI researchautonomous drivingcomputer vision

Skills & requirements

Required

Vision-language-action ModelsLarge-scale TrainingModel DeploymentKnowledge Distillation

Preferred

Model CompressionReinforcement LearningMulti-modal Sensor Data

Stack & domain

Deep LearningVision-language-action ModelsModel TrainingEvaluationDeploymentDistillationCompressionSFTRLMulti-modal Sensor DataCollaborationProblem-solvingInnovationAutonomous DrivingAIMachine Learning

About the role

Original posting from Plus 2 via Lever

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

You will join our core AI team at the frontier of autonomous decision-making, building the Vision-Language-Action (VLA) models that form SuperDrive's reasoning layer. You'll train VLA models that generate high-level driving decisions and trajectory guidance for on-board strategic decision-making, and design the knowledge distillation and compression techniques that transition large models onto on-board compute.

Responsibilities:

  • Design, train, and evaluate Vision-Language-Action models that generate high-level driving decisions and trajectory guidance in support of Plus's reasoning layer.
  • Own a VLA workstream end to end — data, architecture, large-scale training, and on-vehicle validation.
  • Build training and evaluation pipelines and rigorous metrics for VLA performance in driving contexts.
  • Develop distillation and compression recipes to deploy large reasoning models on on-board compute.
  • Apply SFT and RL post-training to improve reasoning, robustness, and long-tail behavior.
  • Collaborate with perception, planning, and platform teams to bring models from research to production

Required qualifications:

  • M.S. minimum, Ph.D. preferred in CS, EE, Mathematics, Statistics, or a related field.
  • 3+ years implementing and training models in a deep learning framework (PyTorch, TensorFlow, or JAX).
  • Direct, hands-on experience training vision-language / vision-language-action models.
  • Hands-on experience with model training, evaluation, and deployment in production.
  • Thorough understanding of state-of-the-art vision-language / VLA models, diffusion, flow matching, and transformers.
  • Experience with large-scale / distributed model training.

Preferred Qualifications:

  • Model distillation, quantization, and inference optimization (ONNX/TensorRT, mixed precision, custom kernels).
  • SFT and RL post-training of large multimodal models.
  • Hands-on experience with multi-modal sensor data (camera, LiDAR, radar).
  • Publications at top venues (CVPR, NeurIPS, ICML, ICLR, CoRL, RSS, ICRA).
  • Autonomous driving / ADAS experience.

Salary range:

$170,000-$260,00k

Exact compensation may vary based on skills and experience.

Compensation:

$170,000-$260,00k

Exact compensation may vary based on skills and experience.

.

Your opportunities joining PlusAI

Work, learn and grow in a highly future-oriented, innovative and dynamic field.

Wide range of opportunities for personal and professional development.

Catered free lunch, unlimited snacks and beverages.

Highly competitive salary and benefits package, including 401(k) plan.

Source: Plus 2 careers (Lever)

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