Machine Learning Intern

Job Description — Applied Machine Learning Engineer Intern – immediate hire, 40hours, 3-days in NYC office, expected $4,500/month

Tracera is in search of a passionate Applied Machine Learning Engineer Intern. The Intern will work closely with our Product & Engineering team to work on ML research focused on enabling the making Supply Chain Due Diligence data collection and reporting easy, efficient, and enjoyable. We help sourcing, procurement, and sustainability teams focus on driving meaningful insights and change in their domains with data rather than getting bogged down with reporting tasks.

Tracera emerged from Bain & Company’s Venture Incubator, where advisors consistently observed their clients struggling with sustainability compliance and data collection. Our software optimizes data extraction, validation, and transformation to make sustainability reporting seamless. We were founded in 2022 and are headquartered in New York City. We are a Series-A company with funding from top VCs and Bain & Company.

• Utilize your major in computer science specifically in machine learning to work on ongoing experimental initiatives, these include
• RAG systems to surface relevant insights
• Build Agents or bots to crawl public or API-driven sources to collect supplier data. This could involve extraction from unstructured data, including pdf documents, tabular data, and images.
• Model design, fine‑tuning, clustering, evaluation, active‑learning loop
• Build high quality eval datasets
• Identify, prototype, deploy, and monitor machine learning solutions—selecting the appropriate approach (off-the-shelf, fine-tuning, or custom models)—to automate data ingestion (e.g., open- source databases, GenAI models, AI agents, etc.) and deliver intelligent assistance across the full AI development lifecycle.
• Collaborate with Product, Customer Success, and Engineering teams to assess technical feasibility of AI opportunities, articulating considerations specific to AI-powered solutions (e.g., models, prompting, compute/GPU needs, nondeterminism).
• Identify and source relevant, high-quality data for machine learning solutions, and proactively recognize and address potential bias in datasets and product development.
• Design, develop, leverage, and maintain scalable infrastructure, including APIs and data pipelines, to support AI experimentation, data processing, and model evaluation, ensuring integration with existing systems.
• Build tools and integrations that streamline customer workflows and provide seamless, high-quality experience.
• Work directly with the customer(s) alongside product in understanding the pain points and desired insights.
• Participate in the architectural design and implementation of Customer Success products, ensuring scalability, maintainability, and integration with existing systems.

• Deep Learning & NLP
• Hands‑on with transformer architectures
• Experience fine‑tuning Hugging Face models.
• Strong grounding in self‑supervised pre‑training, transfer learning, multilingual embeddings.
• Experience with Building Agents or Agentic AI
• Proven experience with building and deploying Agents (preferred)
• Experience deploying observability and alerting using SLIs (service level indicators) on the data sets
• Experience with building RAG Systems
• Hands-on experience with RAG pipelines: document chunking, grounding, retrieval, ranking, response synthesis, and guardrails for quality, safety, and tone.
• Knowledge of search and retrieval systems.
• Experience with Data Pipelines
• Ability to build and maintain data pipelines for ingesting, cleaning, transforming, and indexing structured and unstructured data to support RAG systems.
• Evaluation & Experiment Discipline
• Identifying appropriate evaluation metrics for the problem statement
• Building high quality eval datasets
• Hands on with ML experimentation tool like MLflow

• Research or relevant coursework in Machine Learning (required)
• Stro…