The timeline above illustrates the typical progression from application to offer. Use this to pace your preparation; ensure your coding skills are sharp for the early screens, then shift your focus to system design and optimization theory for the final rounds.

Deep Dive into Evaluation Areas

The following areas represent the core technical pillars for this role. Based on the job description and the nature of Agero's business, you must be prepared to discuss these topics in depth.

Mathematical Optimization & Operations Research

This is a distinguishing feature of the Agero interview. Unlike general ML roles, you are expected to understand how to make decisions under constraints. You need to show that you can take the output of a probability model and use it to drive a logistical action.

Be ready to go over:

• Constrained Optimization – Formulating problems using Mixed Integer Programming (MIP) or Linear Programming (LP).
• Tools & Libraries – Experience with OR-Tools, Gurobi, or CPLEX.
• Stochastic Optimization – How to handle uncertainty in your objective functions (e.g., uncertain traffic or provider availability).
• Advanced concepts – Monte-Carlo tree search, multi-agent simulation, or hierarchical Reinforcement Learning (RL).

Example questions or scenarios:

• "How would you assign 1,000 roadside requests to a fleet of 500 tow trucks to minimize total mileage while meeting ETA SLAs?"
• "Explain the difference between a heuristic approach and an exact solver for a Vehicle Routing Problem (VRP)."
• "How do you incorporate a probability of failure (predicted by an ML model) into a linear optimization constraint?"

Applied Machine Learning & Modeling

While optimization drives the decision, ML drives the intelligence. You must demonstrate deep knowledge of predictive modeling, specifically regarding time-series or tabular data relevant to logistics.

Be ready to go over:

• Modern ML Algorithms – Gradient-boosting machines (XGBoost, LightGBM) and Deep Learning (PyTorch, Transformers).
• Feature Engineering – Handling geospatial data, timestamps, and categorical variables in a dispatch context.
• Evaluation Metrics – Beyond accuracy/AUC; discuss business metrics like cost-per-dispatch or ETA deviation.

Example questions or scenarios:

• "How would you build a model to predict the arrival time of a tow truck provider?"
• "Describe how you would handle high-cardinality categorical features in a gradient boosting model."
• "How do you prevent data leakage when training a model on historical dispatch data?"

System Design & MLOps

Agero requires "ownership of production systems." You will be tested on your ability to build the infrastructure that supports your models.

Be ready to go over:

• Cloud-Native Architecture – Designing pipelines on AWS using services like SageMaker and Airflow.
• Data Engineering – Strong SQL skills, feature store design, and data quality checks.
• Deployment Patterns – Batch vs. streaming inference, A/B rollout strategies, and monitoring for model drift.

Example questions or scenarios:

• "Design an end-to-end architecture for a real-time dispatch system that handles 12 million events annually."
• "How would you orchestrate a pipeline that retrains a model weekly and promotes it to production automatically?"
• "What telemetry would you instrument to monitor the health of a dispatch optimization service?"

Key Responsibilities

As a Machine Learning Engineer at Agero, your day-to-day work is centered on the Swoop dispatch management platform. You are responsible for architecting and shipping Python services that ingest model outputs and run constrained optimization logic to surface real-time decisions. This is a hands-on coding role where you will design batch and streaming pipelines to ensure data flows reliably from the source to the model and back to the product.

You will also spend significant time modeling and simulating. This involves building and extending ML models using frameworks like XGBoost or PyTorch and running time-horizon simulations to quantify the trade-offs between cost and service levels. You aren't just predicting outcomes; you are simulating the entire marketplace dynamics to understand how a change in the dispatch algorithm affects the ecosystem.

Operational excellence is a major part of the role. You will automate training, validation, and A/B rollouts using tools like SageMaker and Airflow. Beyond the code, you will lead and collaborate, partnering with Product and Operations teams to present findings to executives and mentoring a squad of data scientists and engineers. You will continuously improve the system by instrumenting NPS and cost telemetry to identify failure modes and iterate on solutions.

Role Requirements & Qualifications

Agero seeks a candidate who acts as a bridge between a Data Scientist and a Backend Engineer, with a specialized focus on Optimization.

Must-Have Skills

• Expert-level Python: You must write clean, production-ready code, not just research scripts.
• Optimization Experience: Hands-on work with Mixed Integer Programming (MIP), Linear Optimization, or Stochastic Optimization is critical. Familiarity with Google OR-Tools is highly valued.
• Modern ML Stack: Proficiency with XGBoost and PyTorch.
• Cloud Architecture: A proven record of designing pipelines on AWS (preferred), GCP, or Azure. Experience with SageMaker and Airflow.
• Experience: Generally 6+ years combined experience in Data Science and ML Engineering with ownership of production systems.

Nice-to-Have Skills

• Domain Knowledge: Experience in dispatch, logistics, supply-demand marketplaces, or gig-economy platforms.
• Advanced Simulation: Familiarity with Monte-Carlo tree search, multi-agent simulation, or hierarchical Reinforcement Learning.
• Strategic Balancing: Prior work balancing short-term incentives (e.g., immediate cost) against long-term KPIs (e.g., Customer Lifetime Value, NPS).

Common Interview Questions

The following questions reflect the specific requirements of the role and the "hybrid" nature of the position at Agero. They are designed to test your ability to apply theory to the messy reality of roadside assistance.

Optimization & Algorithmic Logic

These questions assess your ability to solve the "Dispatch" problem.

• "How do you formulate a vehicle routing problem with time windows as a Mixed Integer Program?"
• "If our optimization solver takes too long to run in real-time, what heuristics or approximations would you use to speed it up?"
• "How would you model the trade-off between assigning a closer (more expensive) provider versus a farther (cheaper) one?"
• "Explain how you would use OR-Tools to solve a constraint satisfaction problem."

Machine Learning System Design

These questions test your ability to build the "Swoop" platform components.

• "Design a system that predicts the probability of a provider accepting a job. How do you integrate this probability into the dispatch decision?"
• "How would you architect a feature store for real-time vehicle location data?"
• "We want to A/B test a new dispatch algorithm. How do you design the experiment to ensure we don't negatively impact stranded drivers in the control group?"
• "How do you handle retraining models when the underlying distribution of data (e.g., traffic patterns) changes seasonally?"

Behavioral & Leadership

Agero values collaboration and mission alignment.

• "Tell me about a time you had to explain a complex mathematical trade-off to a non-technical product manager."
• "Describe a situation where you identified a failure mode in a production model. How did you fix it and prevent it from recurring?"
• "How do you balance the need for scientific rigor with the pressure to ship features quickly?"
• "Give an example of how you have mentored junior engineers or data scientists in a previous role."