Job Description – Applied Machine Learning Engineer $90K-$110K

Tracera is in search of a passionate Applied Machine Learning Engineer. The ML Engineer will work closely with our Product & Engineering team to work on ML research focused on enabling the making Sustainability (Environmental, Social, and Governance) data collection and reporting easy, efficient, and enjoyable. We help sustainability teams focus on driving meaningful impact in Environmental, Social, and Governance domains 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
• Model design, fine‑tuning, clustering, evaluation, active‑learning loop
• Pattern recognition and extraction from unstructured data, including pdf documents, tabular data, strategy decks, and images.
• Training ML models with internal policy document to tag and surface relevant data
• researching and improving vector embedding models
• Collaborate with the product and engineering teams to come up with a roadmap to implementation of solutions into the product.
• Stay updated with the latest advancements in machine learning and related fields to incorporate cutting-edge techniques into your research.

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.
• Computer Vision for Documents
• Worked on layout analysis and table detection in documents
• OpenCV / Pillow to de-skew, de-noise, augmentation, etc.
• Unsupervised Representation & Clustering
• Built layout-based representations to group documents with similar structure
• Large‑scale clustering algorithms (MiniBatch K‑Means, HDBSCAN, Agglomerative)
• Approximate‑nearest‑neighbor (FAISS, HNSWlib, etc.) for fast cluster assignment
• High‑Cardinality & Open‑Set Classification
• Building & tuning ≥1 000‑label or hierarchical classifiers
• Designed strategies to handle class imbalance
• Developed open-set recognition capabilities to detect and flag unseen or novel document types that are not present during training
• Evaluation & Experiment Discipline
• Field‑level precision/recall, macro/micro F1, Silhouette & Davies‑Bouldin for clusters
• Calibration methods, A/B testing frameworks

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)
• Hands-on experience with Retrieval-Augmented Generation (RAG) techniques.
• Experience with large scale clustering algorithms
• 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 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 Sustainability 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.