Applied Scientist / Machine Learning Engineer

Wayve; Sacramento (CA); Hybrid; USD 312,000 - 370,000; Full time

Job summary

Wayve is the leading developer of Embodied AI technology focused on autonomous driving and foundation models. This full-time role is based in Sunnyvale with a hybrid policy, and involves building and evaluating large-scale pretrained models, data curation, and advancing the GAIA/LINGO ecosystems to drive safer, smarter driving systems.

The role emphasizes data wrangling at scale, training foundation models, and rigorous offline/closed-loop evaluation, collaborating across teams to push

Qualifications

• Masters with 6+ years of relevant experience, or a PhD with 2+ years, in computer science, ML, robotics, mathematics, or related field.
• Strong ML and software fundamentals with a track record of production ML at scale.
• Hands-on strength in data curation, foundation-model training, large-scale data wrangling, and evaluation.
• Experience with large-scale data and large neural networks.
• Fluency in Python and a modern deep-learning framework (PyTorch or similar).

Responsibilities

• Mine world-scale fleet data for rare, safety-critical events using active learning and embedding-based retrieval.
• Figure out good training data mix and curate repeatable processes across cities and sensor rigs.
• Build high-quality enrichments and labeling pipelines at scale.
• Build and fine-tune large-scale pretrained models and run experiments to de-risk ideas.
• Help build best embodied VLM/VLA for driving (LINGO line).
• Design rigorous offline and closed-loop evaluation with coverage of rare scenarios.
• Use GAIA for counterfactual evaluation safely and at scale.
• Contribute across the foundation-model stack (GAIA, policy learning, RL, reward modeling).

Skills

Python

PyTorch

Data wrangling

Foundation models

Evaluation

Large datasets

Education

Master's degree or PhD

Tools

Milvus

Ray Data

Daft

Spark

Job description

About UsFounded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! Wayve is building embodied AI for the physical world, starting with autonomous driving. Instead of the hand-engineered, modular stacks that defined the first era of self-driving, we pioneered AV2.0: a single, end-to-end neural network that learns to drive from raw sensor data and generalises to new cities, vehicles, and conditions. Our foundation models, the GAIA family of generative world models and the LINGO family of vision-language-action models, let vehicles perceive, reason, and act in the open world. We have driven zero-shot across hundreds of cities on three continents, and we are now scaling from proving the science to deploying it with leading automakers and mobility partners, including Nissan, Stellantis, and Uber.

Day to day, the role also spans the broader foundation-model stack, including vision-language-action and vision-language models for embodied AI, world modeling, policy learning, reinforcement learning, and reward modeling.

Key Responsibilities

• Mine world-scale fleet data for rare, long-tail, and safety-critical events using active learning, smart sampling, and embedding-based retrieval and dedup.
• Figure out what makes a good training dataset: which data, mix, and balance actually move the model, and turn that into repeatable curation across cities, sensor rigs, and embodiments.
• Build high-quality enrichments that teams across the company depend on, through (semi-)automated enrichment and labeling pipelines and data quality at scale.
• Build and fine-tune large-scale pretrained models, and run smaller-scale experiments to test and derisk ideas before committing serious compute.
• Help build the best embodied VLM / VLA in the world for driving (the LINGO line): push multimodal perception, reasoning, language, and action.
• Design rigorous offline and closed-loop evaluation: metrics and benchmarks that correlate with real on-road behaviour and safety, with deliberate coverage of rare and safety-critical scenarios.
• Use world-model-based evaluation (GAIA) to probe counterfactual “what if” scenarios safely, repeatably, and at scale.
• Contribute across the wider foundation-model stack as the work demands: generative world models (GAIA), policy learning, reinforcement learning, and reward modeling.

Essential

• A Masters with around 6 or more years of relevant experience, or a PhD with 2 or more years, in computer science, machine learning, robotics, mathematics, or a related field (required).
• Strong ML and software fundamentals, and a track record of taking ML from research into production systems that run at scale.
• Hands-on strength in one or more of: data curation, foundation model training, large-scale data wrangling, and foundation-model evaluation (for example, evaluation of LLMs or similar large models).
• Experience with large-scale data and/or large neural networks, and the judgment to know which experiments and which data actually matter.
• Fluency in Python and a modern deep-learning framework (PyTorch or similar), and comfort working with large, messy, real-world datasets.

Desirable

• Autonomous driving, robotics, or other embodied-AI domains.
• Foundation models, VLMs, world models, diffusion or autoregressive generative models, or reinforcement learning and reward modeling.
• Large-scale data infrastructure: embedding and vector search (e.g. turbopuffer, Milvus), distributed data processing (Ray Data, Daft, Spark), lakehouse formats (Lance, Iceberg), or annotation tooling.
• Closed-loop or simulation-based evaluation, and safety-critical ML.
• Publications at top ML, CV, or robotics venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS).

This is a full-time role based in our office in Sunnyvale. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. The reasonably estimated salary for this role ranges from $311,850 to $370,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above.

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

For more information visit Careers at Wayve.

To learn more about what drives us, visit Values at Wayve.

For US candidates only, please visit E-Verify Notice and Participation and Right to Work.

DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.