Applied Scientist / Machine Learning Engineer

Wayve; Sunnyvale (CA); Hybrid; USD 311,850 - 370,000; Full time

Benefits offered by this job

Competitive equity package

Hybrid working policy

Inclusive culture

Job summary

Wayve is seeking an experienced professional for a dual-track role as an Applied Scientist or Machine Learning Engineer based in Sunnyvale, California. This position focuses on enhancing foundation models through effective data curation and enrichment processes.

The role involves working on critical data events, building datasets for training, and developing rigorous evaluation metrics to ensure safety and performance in autonomous driving. A hybrid work model supports innovation and collaboration.

Qualifications

• A Masters with around 6+ years of relevant experience, or a PhD with 2+ years.
• Strong ML and software fundamentals with a track record in production systems.
• Experience with large-scale data and neural networks.

Responsibilities

• Mine world-scale fleet data for critical events.
• Build high-quality enrichments for data quality at scale.
• Design metrics and benchmarks for evaluation.

Skills

Data curation

Foundation-model training

Large-scale data wrangling

Fluency in Python

Experience with deep-learning frameworks

Education

Masters in Computer Science or related field

PhD in Computer Science or related field

Tools

PyTorch

TensorFlow

Job description

About Us

Founded 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.

Role

This role sits in the AI Platform organisation, on the data flywheel that powers every model we ship. The thesis is simple and compounding: the more intelligently we curate, enrich, and evaluate the real‑world driving experience our fleet generates, the faster our foundation models improve, and the further they generalise across geographies, embodiments, and OEM platforms. As deployment scales, the bottleneck is shifting from raw model capacity to the quality and intelligence of the data engine and the rigour of how we measure progress. That is the problem you will own.

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

The role is a dual‑track position: Applied Scientist or Machine Learning Engineer (TC3 Senior or TC4 Staff/Tech Lead), calibrated to your background. We are open to specialisation. There are three areas you can go deep into:

• Data curation: mine world‑scale fleet data for the rare, long‑tail, and safety‑critical moments that move the model.
• Data enrichment: turn raw driving experience into high‑signal training data through (semi‑)automated enrichment, labeling, and data quality at scale.
• Foundation model evaluation: define how we know a driving foundation model is genuinely getting better, offline and in closed loop.

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 deduplication.
• 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 modelling.

Essential Requirements

• 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 modelling.
• 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.

Inclusive Interview 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.

EEO Statement

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.

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.