How serious Data Scientist candidates prepare for Waymo interviews

Waymo structures its hiring process into 4 stages spanning approximately 4-7 weeks. Candidates who interviewed recently told Prepfully the timeline can stretch to 8 weeks if feedback is mixed or leveling discussions take longer.

Prepfully candidates that interviewed with Waymo reported on certain questions they were asked. These are in line with the focus of Waymo on SQL, statistics, and autonomous vehicle domain knowledge.

Prepfully candidates reported SQL questions that often reflect Waymo's autonomous vehicle context:

• Calculate average perception latency per sensor type across all rides in a given city
• Use CTE to join ride data with sensor logs, compute AVG latency grouped by sensor_type, filter by city using WHERE clause.
• Find all rides where LiDAR detection confidence dropped below threshold within 5 seconds of a lane change
• JOIN ride_events with sensor_readings on ride_id, use LAG/LEAD window functions to identify lane change events, filter confidence < threshold within 5-second window using timestamp arithmetic.
• Identify vehicles with the highest disengagement frequency per 1000 miles
• Calculate total miles and disengagement count per vehicle, compute rate using division, ORDER BY rate DESC, use LIMIT or RANK() for top results.
• Determine which intersection types have the highest rate of pedestrian near-misses
• JOIN intersection_data with incident_logs, filter for pedestrian near-miss events, GROUP BY intersection_type, compute rate as COUNT/total_passes.
• Find routes where safety intervention rate exceeds the fleet average
• Use CTE to calculate fleet-wide average intervention rate, then identify routes with rates above this baseline using subquery or window function comparison.

Prepfully candidates reported being asked questions such as:

• Can an unbalanced sample size in an A/B test result in bias towards the smaller group?
• What analysis would you run on non-normal A/B test data?
• How would you test if survey responses were filled at random versus truthfully?
• How would you design an experiment to validate a new perception model performs better than the current one?
• What metrics would you use to compare Waymo Driver performance across different cities?

To practice questions like these with detailed feedback, book time with a Data Scientist coach on Prepfully.

How would you evaluate a perception model for safety-critical systems?

Address precision/recall separately for vehicles, pedestrians, cyclists. Discuss false positive rate (phantom objects cause unnecessary braking) versus false negative rate (missing objects causes crashes, far more costly). Use simulation testing with billions of miles, closed-course validation, and real-world fleet testing with gradual rollout.

Design a system to predict vehicle trajectories 8 seconds into the future

Encode agent's past trajectory (1 second history), encode scene context (road graph, other agents), use self-attention to capture interactions, generate multiple mode predictions (typically 6 modes) since future is multimodal. Evaluate using Average Displacement Error (ADE) and Final Displacement Error (FDE).

How would you improve perception in adverse weather conditions like fog or heavy rain?

Discuss multi-modal sensor fusion (LiDAR penetrates rain better than cameras, radar penetrates fog), domain adaptation techniques, synthetic data generation with weather augmentation, uncertainty estimation to flag low-confidence predictions, and system-level strategies like reducing speed in degraded conditions.

Explain bias-variance tradeoff in the context of autonomous driving

Autonomous vehicles need low bias (must detect obstacles accurately) AND low variance (must be stable across diverse conditions). Safety-critical systems accept slightly higher bias for significantly reduced variance. Regularization techniques (L1/L2, dropout), data augmentation, and ensemble methods help balance this tradeoff.

Practice Waymo data scientist interview questions with Prepfully's question bank, including SQL and ML problems reported by recent Waymo candidates.

Candidates who received offers consistently highlighted four preparation areas.

Master SQL and Statistics (Not LeetCode)

The Waymo data scientist interview focuses heavily on SQL, statistics, and real-world data intuition rather than algorithmic problem-solving.

Start here (highest priority):

• A/B test design from scratch: hypothesis formulation, sample size calculation, randomization, statistical test selection
• SQL window functions (ROW_NUMBER, RANK, LAG/LEAD, PERCENTILE_CONT)
• Common Table Expressions (CTEs) and complex multi-table joins

Also important:

• Query optimization for large-scale data
• Hypothesis testing, confidence intervals, causal inference
• Handling non-normal distributions (Mann-Whitney U-Test, bootstrapping)

StrataScratch and DataLemur offer SQL problems closer to Waymo's focus than LeetCode.

Learn Autonomous Vehicle Fundamentals

Waymo expects domain knowledge. You don't need autonomous driving work experience, but understanding the technology helps significantly.

Read Waymo research papers:

• EMMA (multimodal foundation model for perception and planning)
• Wayformer (transformer-based trajectory prediction)
• ChauffeurNet (imitation learning for motion planning)

Watch Drago Anguelov's MIT lecture, VP of AI Foundations at Waymo, on building scalable autonomous driving stacks, he covers AutoML infrastructure and handling rare events that appear in system design interviews.

Understand the perception-prediction-planning pipeline:

• Perception (< 100ms latency): Detect vehicles, pedestrians, cyclists, traffic signals, lanes from LiDAR/camera/radar fusion
• Prediction (< 50ms latency): Predict future trajectories of detected objects, accounting for multiple possible futures
• Planning (< 30ms latency): Compute optimal collision-free trajectory considering safety constraints and traffic rules

Know sensor tradeoffs:

• LiDAR: Accurate depth, weather-robust, sparse point clouds
• Camera: Rich semantic information, struggles in bad lighting
• Radar: Penetrates fog/rain, lower resolution

Familiarize yourself with safety metrics: disengagements per mile, collision avoidance testing, false positive/negative rates.

Waymo's ML questions prioritize practical application over theory.

Key topics:

• Model evaluation for imbalanced datasets (rare edge cases in autonomous driving)
• Addressing class imbalance: SMOTE, class weights, focal loss
• Feature engineering for sensor data (temporal features, spatial relationships)
• Uncertainty quantification and confidence calibration
• Robustness to distribution shift (new cities, weather conditions)

A Waymo data scientist candidate who interviewed in late 2024 told Prepfully that ML coding focused on debugging existing implementations rather than building models from scratch. Practice identifying issues in transformer code, attention mechanisms, and understanding model behavior. Benjamin Sapp's MultiPath++ research, Waymo Research Scientist, shows how Waymo models multimodal trajectory predictions, relevant for ML coding rounds on uncertainty quantification.

Behavioral rounds are used by Waymo to test collaboration and communication. Write 5-8 stories that illustrate:

• Decision-making in case of ambiguity.
• Sharing in-depth knowledge with non-technical stakeholders.
• Navigating disagreements and finding constructive paths forward with teammates.
• Lessons on project failure.
• Dealing with dirty or unclean data.

For Why Waymo questions, reference Waymo's published safety achievements, demonstrate genuine interest in autonomous vehicle technology, and connect the mission to your professional objectives.

Avoid generic tech company enthusiasm. Prepfully coaches told us Waymo interviewers respond well to candidates who show they understand the safety mission and can articulate why it matters to them personally.

Candidates who completed mock interviews with Waymo data scientists or ML engineers on Prepfully felt better prepared for SQL depth questions and domain-specific challenges. Practice verbalizing your thought process, handling follow-up questions, and pivoting when your initial approach doesn't work.

Waymo follows Google's leveling framework (L3-L6+), with compensation competitive among Alphabet companies. Based on levels.fyi data and Prepfully candidate reports (note: compensation data changes frequently, so treat these as approximate ranges):

Salary Ranges

L4 Data Scientist:

• Total compensation: $240K-$270K
• Base: $160K-$180K
• RSUs/WMUs: $55K-$75K annual
• Bonus: $15K-$25K

L5 Senior Data Scientist:

• Total compensation: $320K-$360K
• Base: $190K-$220K
• RSUs/WMUs: $100K-$120K annual
• Bonus: $20K-$30K

L6 Staff Data Scientist:

• Total compensation: $400K-$460K
• Base: $230K-$270K
• RSUs/WMUs: $110K-$140K annual
• Bonus: $45K-$65K

According to Prepfully data, total compensation for L4-L5 roles typically ranges from $287K to $379K depending on experience and negotiation.

Waymo uses Waymo Market Units (WMUs) rather than direct Alphabet stock. These vest over 4 years at 25% annually with no cliff. The critical consideration: WMUs are illiquid until a potential Waymo IPO or acquisition.

Industry analysts have speculated about various IPO timelines and valuations, but these are highly uncertain and should not be factored into compensation decisions. Until a liquidity event occurs, your equity has no secondary market. Candidates coming from public companies with liquid RSUs should factor this illiquidity into compensation comparisons.

Recent candidates have reported that Waymo's compensation has become more competitive in recent years, especially when candidates have competing offers from other autonomous vehicle companies or top tech firms. Present competing offers in writing from legitimate companies, and Waymo typically responds with adjustments.

Location Differences

Compensation is highest in Mountain View and San Francisco. Phoenix-based roles pay approximately 10-15% lower. Remote positions (where available) align with Phoenix-level compensation.

Negotiation Strategy

Waymo expects negotiation. Focus on total compensation rather than splitting base versus equity. If you have competing offers from other autonomous vehicle companies (Aurora, Zoox) or top-tier tech companies, mention them. Waymo rarely pulls offers due to negotiation attempts.

Sign-on bonuses range from $15K to $50K depending on level and background. These can be negotiated if equity is at ceiling.

SQL and statistics dominate the Waymo data scientist interview, not LeetCode algorithms. Prepfully candidates consistently told us about SQL-heavy technical screens testing window functions, CTEs, A/B test design, and statistical inference. The coding bar is lower than typical tech companies, but the statistics and domain knowledge bar is significantly higher.

Domain knowledge is what distinguishes strong candidates. Learn sensor tradeoffs, the perception-prediction-planning pipeline, and read Waymo research papers (EMMA, Wayformer, ChauffeurNet). Prior autonomous vehicle experience is not required, but you need to demonstrate genuine interest in the technology. Generic tech company enthusiasm does not resonate at Waymo; connect with the safety mission and reference Waymo's published safety data to show you understand what the company is working toward.

L4-L5 compensation typically ranges from $287K to $379K based on Prepfully data, but equity comes as illiquid Waymo Market Units. WMUs cannot be sold until a potential IPO or acquisition (timing is uncertain). Factor this illiquidity into comparisons with public company offers that include liquid RSUs.

The 4-7 week timeline can compress to 3-4 weeks for referred candidates or those with strong performance. Extended timelines (6-8 weeks) occur when feedback is mixed or leveling requires additional discussion. Multiple rounds evaluate your ability to communicate complex technical concepts to non-technical stakeholders, prepare to explain A/B testing to product managers or discuss ML model performance with operations teams.