Preparing for the Machine Learning Engineer loop at Plangrid requires a structured approach that balances deep theoretical knowledge with practical engineering execution. You should focus your preparation on four core pillars that reflect the team's engineering standards.

Role-Related Knowledge – You must demonstrate a deep, first-principles understanding of machine learning. This includes deep learning architectures (such as Transformers and Diffusion models), optimization techniques, loss functions, and evaluation metrics. Be ready to explain why you would choose a specific model architecture over another for a given business problem.

Problem-Solving & System Design – Interviewers want to see how you approach open-ended, ambiguous system challenges. You should practice structuring your thoughts out loud, starting with clear requirement gathering, followed by data ingestion, model training, evaluation, deployment, and real-time monitoring strategies.

Engineering Rigor – Excellent Python skills are non-negotiable. You will be evaluated on your ability to write clean, modular, and performance-profiled code. Familiarity with modern frameworks like PyTorch and distributed training libraries is highly valued, as is your understanding of MLOps best practices.

Collaboration & Ownership – At Plangrid, engineers own outcomes, not just individual code components. You should prepare to discuss your experience working with cross-functional partners like product managers, data engineers, and research scientists, highlighting your ability to translate academic concepts into real-world product features.

The interview process for the Machine Learning Engineer position at Plangrid is designed to evaluate both your immediate technical capabilities and your long-term growth potential. Candidates typically navigate a multi-stage loop that tests coding, system design, and behavioral alignment.

The process begins with an initial technical recruiter screen to discuss your background, followed by a deeper technical discussion with the hiring manager. If you pass these initial stages, you will move into the core technical assessment, which may include a coding round, a dedicated ML system design interview, and a behavioral panel. Some teams or seniority levels may also include a take-home technical assignment focused on a practical machine learning problem.

While the technical rounds are highly interactive and engaging, candidates should remain proactive throughout the process. The complexity of hiring across a global organization like Autodesk can occasionally lead to longer timelines between rounds.

The timeline above illustrates the typical progression from your initial application to the final decision. Candidates should expect the technical screening and hiring manager rounds to set the stage for the more intense system design and panel interviews. Use this timeline to pace your preparation, ensuring you allocate ample time to practice both hands-on coding and high-level system architecture.

Machine Learning System Design & Scalability

This evaluation area focuses on your ability to build production-grade systems that scale to handle petabytes of data. You must show that you can move beyond local Jupyter notebooks and design architectures capable of continuous training, automated evaluation, and low-latency serving.

Be ready to go over:

• Distributed Training Frameworks – Understanding how to scale model training across massive compute clusters using tools like PyTorch Distributed, DeepSpeed, Megatron-LM, or FSDP.
• Data Pipeline Architecture – Designing scalable pipelines for multimodal datasets (such as 3D BIM models, images, and text) using Spark or modern lakehouse formats.
• Model Monitoring & MLOps – Setting up robust systems for tracking model drift, data quality, latency, and throughput in a high-stakes production environment.
• Advanced concepts (less common) – CUDA optimization, custom kernel development, and building internal developer platforms using Kubernetes or Slurm.

Example scenarios:

• "Design a real-time semantic segmentation pipeline for processing high-resolution construction site aerial imagery."
• "How would you optimize the inference latency of a large multimodal model deployed on resource-constrained mobile devices in the field?"

Coding & Algorithmic Execution

The coding assessment is designed to test your core computer science fundamentals and your ability to translate complex logic into clean, bug-free Python code.

Be ready to go over:

• Data Structures & Algorithms – Mastery of graphs, trees, dynamic programming, and search algorithms, which are highly relevant to navigating 2D floor plans and 3D spatial data.
• Performance Profiling – Writing memory-efficient code, identifying bottlenecks, and utilizing Python's multiprocessing or async capabilities where appropriate.
• PyTorch Fluency – Demonstrating hands-on experience with PyTorch, including custom dataset loaders, training loops, and custom layer implementations.

Example scenarios:

• "Write a spatial indexing algorithm (like an R-Tree or Octree) to efficiently query 3D points in a construction site point cloud."
• "Implement a custom loss function in PyTorch that penalizes false positives more heavily than false negatives for safety critical detections."

Generative AI & Foundation Models

Given Plangrid's focus on embedding generative AI into its cloud platforms, you will be evaluated on your understanding of modern foundation models and their practical applications.

Be ready to go over:

• Transformer Architectures – Deep understanding of attention mechanisms, positional encodings, and the trade-offs between encoder-only, decoder-only, and encoder-decoder models.
• Fine-Tuning Techniques – Practical knowledge of Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA, QLoRA, and prefix tuning.
• Retrieval-Augmented Generation (RAG) – Designing robust RAG pipelines that can accurately retrieve and synthesize information from complex, domain-specific technical documents.

Example scenarios:

• "How would you fine-tune a foundation model on proprietary AEC data while preventing catastrophic forgetting of general knowledge?"
• "Design a evaluation framework to measure the factual accuracy and safety of an LLM-powered construction assistant."

As a Machine Learning Engineer at Plangrid, your daily work will span the entire machine learning lifecycle. You will not be siloed; instead, you will collaborate closely with product, data engineering, and core software platform teams to deliver end-to-end features.

• Technical Leadership & Strategy – You will help define the long-term technical vision for AI and foundation models within the construction ecosystem, advising leadership on architectural directions and technology choices.
• Model Development & Training – You will design, train, and optimize deep learning models, translating experimental ideas from academic research into production-grade code.
• Infrastructure & MLOps – You will architect and maintain the pipelines required for distributed training, model versioning, automated testing, and scalable deployment.
• Multimodal Data Integration – You will build data ingestion pipelines capable of processing, cleaning, and structuring massive, complex AEC datasets, including 3D meshes, CAD files, and text specifications.
• Mentorship & Culture – You will mentor senior and mid-level engineers, fostering a culture of rigorous experimentation, technical ownership, and engineering excellence.