Machine Learning Engineer (Autonomous Driving)

Location: Sunnyvale, California, USA; hybrid working model combining in-person collaboration with remote work.

Salary: Senior $311,850–$389,400 annually; Staff $370,040–$419,760 annually.

Company

hirify.global develops an end-to-end AI platform for autonomous driving, combining real-world driving data, robotics, simulation, and foundation models to enable vehicles to adapt and improve at scale.

What you will do

• Build scalable methods to discover rare, high-value, and safety-critical scenarios in fleet-scale driving data.
• Develop pipelines for automated enrichment, labelling, deduplication, and data-quality monitoring.
• Use embeddings, active learning, similarity search, model-assisted mining, and smart sampling to improve dataset coverage.
• Run experiments on data mixtures and curation strategies to improve model performance.
• Build evaluation benchmarks and slice analyses that expose model blind spots and guide data and modelling iterations.
• Collaborate with foundation-model, evaluation, simulation, and infrastructure teams to move research ideas into production.

Requirements

• Strong machine-learning and software-engineering fundamentals with the ability to take applied research into production.
• Proficiency in Python and a modern deep-learning framework, plus familiarity with SQL or large-scale data tools.
• Experience with data curation, foundation models, large-scale data wrangling, computer vision, or model evaluation.
• Ability to reason about sampling, class imbalance, label quality, dataset coverage, and experimental design.
• Clear communication of technical trade-offs and comfort with ambiguous, cross-functional problems.

Nice to have

• Experience with autonomous driving, robotics, vision-language models, active learning, similarity search, Spark, or distributed training.

Culture & Benefits

• Hybrid work with access to vehicle workshops and labs.
• Meaningful equity and annually benchmarked salaries.
• Relocation support and visa sponsorship where applicable.
• Learning and development support.
• Location-dependent health, family, retirement, and wellbeing benefits.
• Ownership in an evolving environment where processes and ways of working are still being developed.

Hiring process

• Initial 25-minute recruiter call.
• 60-minute competency interview.
• Deep-dive interviews covering programming, system design, ML breadth, and PyTorch debugging, lasting 3–4 hours.
• Final interview focused on mission, values, and level alignment.