Machine Learning Engineer - 1

Parspec•San Mateo, CA

1d•Hybrid

About The Position

Founded in 2021, Parspec is revolutionizing material procurement for the $13 trillion USD construction industry by digitizing and organizing the industry's product data. Our proprietary AI technology maintains a current and comprehensive catalogue of millions of products, enabling our customers to identify products that best meet their needs - instantly. Trusted by top designers, builders, distributors and sales agents and backed by leading venture investors, Parspec is paving the way for a more innovative, connected, and sustainable future in construction. Join us in building transformative technology that reshapes one of the world’s oldest and largest industries. We're looking for a Machine Learning Engineer 1 to join our AI team in San Mateo. This is an early-career role for someone with strong ML fundamentals, real curiosity, and a drive to ship production systems. You'll work alongside senior ML engineers on the AI behind Parspec's product catalog: search, ranking, recommendations, NLP, and document extraction. You'll own real components from day one, with scope that grows as you do.

Requirements

Bachelor's or Master's in Computer Science, Engineering, or a related field
0-2 years in ML/AI (internships, academic projects, or early-career roles count)
Solid grasp of supervised and unsupervised learning, bias-variance tradeoff, regularization, and cross-validation
Working knowledge of CNNs, transformers, training loops, and loss functions
Exposure to text processing, embeddings, retrieval, or information extraction
Proficient in Python beyond notebooks. Can write clean, testable code with NumPy, Pandas, and scikit-learn
Experience with PyTorch or TensorFlow. You've trained models, not just called APIs
Familiarity with Git, collaborative development workflows, and writing code others can maintain
Strong problem-solving, curiosity, and a bias toward shipping

Nice To Haves

Experience with search or recommendation systems (academic or personal projects count)
Familiarity with vector databases (Pinecone, Qdrant, Milvus, FAISS)
Exposure to LLMs. Has used APIs, built simple RAG pipelines, or experimented with fine-tuning
Experience with OCR, PDF parsing, or document processing
Understanding of search evaluation metrics (nDCG, MRR, precision@k)
Cloud platform basics (AWS, GCP). Can deploy a model or run training on cloud infrastructure
Kaggle competitions, open-source contributions, or published research
Experience with messy, real-world data, not just clean benchmark datasets

Responsibilities

Contribute to hybrid search systems that combine keyword retrieval with dense vector embeddings
Help develop and evaluate recommendation models for product discovery and personalization
Implement and experiment with retrieval, ranking, and re-ranking approaches under guidance from senior engineers
Work with vector databases and embedding pipelines to improve search relevance across millions of products
Build and improve NLP pipelines for construction domain tasks like entity extraction, classification, and text understanding
Contribute to document extraction systems that turn PDFs, spec sheets, and product catalogs into structured data
Experiment with LLM-based approaches (prompting, RAG pipelines) for domain-specific information retrieval
Debug model behavior. Understand why a model is wrong, instead of retraining and hoping
Build and maintain data pipelines for model training and evaluation
Create, clean, and curate labeled datasets. Data quality is a first-class problem here
Build evaluation frameworks for search relevance and extraction accuracy
Instrument models with metrics and monitoring to catch regressions early
Work with product managers and senior engineers to turn business problems into ML solutions
Participate in the full lifecycle, from prototyping through deployment
Own specific components or features end-to-end, with increasing scope over time
Use AI tools (Claude Code, Copilot) as part of your daily workflow. We're an AI-native team