Machine Learning Intern

Job Description – Applied Machine Learning Engineer Intern – Supply Chain 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. How did Tracera come about? 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. What You’ll Do:
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. Key ML Skills needed for you to succeed in this role:
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 Ideal Candidate Profile:
Research or relevant coursework in Machine Learning (required)
Strong academic background with a degree (ideally working towards your master's or Ph.D.) in a related field, such as computer science, machine learning, artificial intelligence, or a related discipline (required)
Proficiency in Python (required) and associated machine learning tools and libraries (required).
Practical, hands-on experience with transformer models (e.g., BERT, GPT, Claude, etc.) and an understanding of attention mechanisms, transfer learning, and fine-tuning (required).
Experience fine-tuning Hugging Face models (required)
Experience with large scale data analysis, experimentation and production ML system deployment
Bonus: Experience deploying Machine Learning tools and services on AWS.
Strong understanding of mathematical foundations, information theory, statistical analysis, machine learning fundamentals, and relevant algorithms.
Demonstrated ability to work autonomously and in a fast-paced environment.
Excellent problem-solving skills and ability to think analytically.
Strong communication skills (direct customer interactions expected) to effectively convey complex concepts to technical and non-technical stakeholders. As an ML Engineer at Tracera, you will have the opportunity to work on a platform that addresses critical sustainability challenges faced by companies worldwide. Your contributions will directly impact the efficiency and effectiveness of Risk and Compliance data collection and reporting, allowing businesses to make more informed and sustainable decisions. The role offers the potential to become a full-time ML Engineer, making it an exciting opportunity for those passionate about environmental sustainability and cutting-edge platform development.