Recruiting from ScratchRemote
Founding Machine Learning Engineer
United States of America
Full Time
2 hours ago
$150,000 - $300,000 USD
H1B Sponsored
Key skills
Machine learningNatural language processingInformation retrievalEntity resolutionPythonPyTorchTransformer architecturesEncoder model trainingRetrieval systemsClassification systemsEmbedding modelsRepresentation learningContrastive learningMetric learningLarge language modelsMultilingual NLPDistributed training on GPU clustersExecution biasOwnership mindsetMachine LearningMLNLPLLM
About this role
Recruiting from Scratch is a specialized talent firm dedicated to helping companies build exceptional teams. They are seeking a Founding Machine Learning Engineer to own the development of core ML systems, focusing on information retrieval, entity resolution, and classification, while integrating these systems into production APIs.
Responsibilities:
- Own the end-to-end development of core ML systems—from research and modeling to production deployment
- Design and train models for information retrieval, entity resolution, classification, and structured data extraction
- Build systems that transform messy, multilingual web-scale data into structured, queryable intelligence
- Develop embedding models, ranking systems, and retrieval pipelines for high-precision search and matching
- Apply transformer architectures and modern NLP techniques to real-world data problems
- Leverage LLMs for tasks such as extraction, classification, and data enrichment at scale
- Continuously evaluate and improve model performance using rigorous experimentation and metrics
- Work closely with engineering and product teams to integrate ML systems into production APIs
Requirements:
- 3+ years of experience building and shipping production ML systems, particularly in NLP, information retrieval, or entity resolution
- Strong hands-on experience with Python and PyTorch
- Deep understanding of transformer architectures, including training and fine-tuning encoder models
- Experience building retrieval systems, classifiers, or embedding-based systems
- Familiarity with representation learning techniques (e.g., contrastive learning, metric learning)
- Experience applying LLMs to structured data problems (e.g., extraction, classification, generation)
- Strong problem-solving skills with the ability to work on ambiguous, large-scale data challenges
- High ownership mindset with a strong bias toward execution in fast-paced environments
- Experience with entity resolution or record linkage at scale
- Background in multilingual or cross-lingual NLP
- Experience building taxonomies, ontologies, or knowledge systems
- Familiarity with distributed training on GPU clusters
- Experience scaling LLM inference pipelines in production
- Research publications or open-source contributions in NLP/IR