Data Scientist (Autonomous Driving AI)

Location: Hybrid role based in the London office, with two days per week onsite; the posting header also lists Sunnyvale, California, USA. The role involves asynchronous collaboration across time zones.

Salary: $209.7K–$240K

Company

hirify.global develops an AI platform for autonomous driving, using end-to-end, mapless, and hardware-agnostic technology to help vehicles learn from real-world experience.

What you will do

• Develop and refine performance metrics that guide engineering priorities and commercial progress.
• Design experiments and targeted off-road measurements to validate customer requirements, safety, and performance.
• Investigate model training and inference factors that create functionality and performance bottlenecks.
• Identify, test, and validate hypotheses that can improve the hirify.global AI Driver.
• Summarize, visualize, and communicate findings to support prioritization and strategy.
• Partner with engineering and cross-functional teams to turn data into actionable direction.

Requirements

• 3+ years of experience in a Data Science role.
• Production-level SQL skills and experience building large datasets and data-transformation pipelines.
• Experience designing robust real-world experiments, including A/B tests, and evaluating test statistics.
• Strong statistical foundations, including distribution selection and frequentist-statistics assumptions.
• Proficiency with Python or R and data science or machine-learning packages.
• Ability to communicate findings clearly and influence prioritization and strategy.

Nice to have

• Practical machine-learning experience with PyTorch and experience taking research ideas to production.
• Experience with causal inference, econometrics, or Bayesian hypothesis testing.
• Experience with large datasets and distributed computing tools such as Spark or Hadoop.
• Experience in a fast-moving technology company or startup.

Culture & Benefits

• Hybrid working with core hours and opportunities to work in vehicle workshops and labs.
• Relocation support and visa sponsorship where applicable.
• Equity participation and annually benchmarked salaries.
• Learning and development budgets for training, conferences, and growth.
• Health, dental, parental leave, retirement or pension benefits, therapy access, wellbeing partnerships, and team socials.

Hiring process

• Initial recruiter call followed by competency interviews, including a hiring manager discussion and SQL or Python interview.
• Technical interviews covering quantitative methods and a causal inference case study.
• Final leadership interview.