Senior Machine Learning Engineer

About Troveo

Troveo is building the next-generation data platform to train AI video models. Troveo offers the world’s largest library of AI video training data, featuring millions of hours of licensed video content. Our end-to-end data pipeline connects creators, rights holders, and AI research labs, enabling scalable, compliant, and innovative uses of video across for AI application and model development.

We are an early‑stage, high-growth venture backed by forward‑thinking investors, and we are seeking an innovative strategic engineer to help us scale.

Role Overview

The Senior Machine Learning Engineer will play a central role in designing, building, and optimizing large‑scale machine learning pipelines for AI video model training. You’ll work across the full ML lifecycle, from structuring massive datasets to deploying, evaluating, and training models in production.

This is a hands‑on, high‑impact role for an engineer who thrives on scale, autonomy, and cross‑functional collaboration. You will combine deep technical expertise with strong communication and business acumen, translating models into measurable costs, performance targets, and real‑world outcomes.

Key Responsibilities

• Data Curation & Indexing Pipelines – Architect and implement large‑scale pipelines for video ingestion, metadata extraction, and indexing using vector databases and embedding models to enable fast, semantic retrieval. Design annotation workflows integrating active learning, weak supervision, and human‑in‑the‑loop systems to curate high‑quality labeled datasets for video models. Contribute to optimizing data partitioning, sharding, and caching strategies to handle petabyte‑scale video corpora, ensuring low‑latency search and robust data lineage.
• Model Training & Evaluation – Develop and fine‑tune multimodal models (e.g., CLIP variants, transformer‑based encoders) for video embeddings, scene segmentation, and relevance ranking using PyTorch and Hugging Face. Build evaluation frameworks with metrics like NDCG, mAP, and annotation consistency scores to iteratively improve search accuracy and annotation efficiency. Deploy models via containerized services with A / B testing and monitoring for drift detection in production search and annotation pipelines. Collaborate with Product and Operations teams to translate ML performance into business insights and cost implications.
• Infrastructure & Optimization – Scale ML infrastructure on AWS, leveraging multi‑GPU clusters and distributed training to accelerate embedding computation and indexing jobs. Implement testing and deployment processes across large distributed systems. Fine‑tune OSS models. Working knowledge in training large models is a plus. Implement automated CI / CD for model versioning, hyperparameter tuning, and resource orchestration to minimize compute costs and maximize GPU utilization. Profile and tune systems for bottlenecks in vector similarity search, batch annotation, and real‑time querying.
• Cross‑Functional Collaboration – Partner with product, research, and data teams to align ML outputs with business KPIs, such as search latency targets and annotation throughput. Translate technical trade‑offs (e.g., recall vs. precision in embeddings) into actionable insights for stakeholders, fostering adoption in video discovery features. Work closely with data engineers, research scientists, and product teams to align model performance with strategic business goals. Communicate technical concepts clearly to both technical and non‑technical stakeholders. Take ownership of project outcomes in a fast‑paced, startup environment.

Qualifications & Experience

Nice to Have

Location & Compensation

Location : Strong preference for candidates based in the San Francisco Bay Area.

Compensation : $200,000 – $400,000 base salary + equity.

Why Join Troveo?

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