Machine Learning Engineer

Summary

Join EvenUp's Machine Learning team and contribute to building scalable, innovative Document AI solutions using cutting-edge techniques. You will pioneer cutting-edge Document AI systems, implement and advance technologies in information extraction, retrieval, and management, and collaborate with domain experts. This role requires 3+ years of experience in machine learning and strong software engineering fundamentals. Expertise in classical ML techniques, deep learning frameworks, NLP, LLMs, and RAG systems is essential. EvenUp offers a competitive benefits package including health insurance, flexible paid time off, 401(k), paid parental leave, and a home office stipend.

Requirements

• 3+ years of experience in machine learning, data science, or similar technical role
• Strong software engineering fundamentals and proficiency in Python
• Demonstrated expertise in: Classical ML techniques (regression, classification, clustering)
• Demonstrated expertise in: Deep learning frameworks (PyTorch, TensorFlow)
• Demonstrated expertise in: Natural language processing and information extraction
• Demonstrated expertise in: Large language models and prompt engineering
• Demonstrated expertise in: RAG system design and implementation
• Demonstrated expertise in: Advanced RAG techniques (multi-hop reasoning, self-consistency)
• Demonstrated expertise in: Vector databases and embedding techniques
• Demonstrated expertise in: Model evaluation and validation methods
• Experience with: LLM fine-tuning (LoRA, QLoRA, instruction tuning)
• Experience with: Knowledge graph construction and reasoning
• Experience with: Production ML system deployment
• Experience with: Distributed computing and data processing
• Excellent communication skills with ability to: Explain complex technical concepts clearly
• Excellent communication skills with ability to: Collaborate effectively with cross-functional teams
• Excellent communication skills with ability to: Write clear technical documentation
• Sound judgment in balancing technical tradeoffs with business needs

Responsibilities

• Pioneer cutting-edge Document AI systems at the forefront of generative AI innovation, building next-generation models that go beyond traditional document processing to achieve human-level understanding of complex legal and medical documents, intelligently extract key entities and relationships, perform sophisticated multi-document reasoning, and generate contextually-aware documents that transform business workflows
• Implement and advance technologies in: Information Extraction (using traditional ML, LLMs, and multi-modal LLMs for entity recognition, relationship extraction, and document structure understanding)
• Implement and advance technologies in: Information Retrieval (query understanding, semantic search, hybrid retrieval architectures, and learning-to-rank models)
• Implement and advance technologies in: Data Management (schema design, knowledge graphs, distributed data pipelines, and petabyte-scale processing)
• Implement and advance technologies in: RAG (Retrieval-Augmented Generation) with advanced techniques like multi-hop reasoning, chain-of-thought prompting, and self-consistency checks
• Implement and advance technologies in: Prompt Engineering (few-shot learning, instruction tuning, and context window optimization)
• Implement and advance technologies in: LLM fine-tuning (parameter-efficient techniques like LoRA/QLoRA, instruction fine-tuning, and domain adaptation)
• Collaborate with domain experts (legal, healthcare, etc), product managers, and engineers to translate insights into robust machine learning systems
• Create tools that empower internal teams and clients to make data-driven decisions
• Mentor junior team members, promoting a culture of excellence and collaboration

Benefits

• Choice of medical, dental, and vision insurance plans for you and your family
• Flexible paid time off
• 10 US observed holidays, and Canadian statutory holidays by province
• A home office stipend
• 401(k) for US-based employees
• Paid parental leave
• Sabbatical program
• A meet-up program to get together in person with colleagues in your area
• Offices in San Francisco and Toronto