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 6+ years in ML engineering, with a focus on information retrieval, embedding systems, or data annotation pipelines. Proven track record building scalable indexing and search infrastructure, including vector stores and similarity search algorithms. Expertise in Python and PyTorch for core model development; hands‑on experience with Hugging Face Transformers for multimodal embeddings and fine‑tuning. Working experience with video, computer vision, and multi‑modal LLMs. Hands‑on experience deploying models in production environments and measuring model accuracy. Proficiency in ML ops tools (e.g., MLflow, Weights & Biases) for experimentation, versioning, and deployment. Hands‑on experience with production ML deployment, evaluation metrics for retrieval / annotation tasks, and cost‑optimized scaling on cloud platforms like AWS. Strong analytical skills for dissecting performance in large distributed systems; familiarity with multi‑GPU training and vector databases preferred. Excellent communication to bridge technical depth with strategic priorities in collaborative settings. Nice to Have Prior experience training video models or working with video‑based datasets. Demonstrated expertise in GPU optimization and large‑scale compute performance tuning. A blend of startup agility and big tech rigor. Contributions to open source development and projects. Experience working with search ranking algorithms. 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? Work at the cutting edge of AI, video, and large‑scale data infrastructure. Build systems that directly power the next generation of AI video models. Collaborate with a world‑class team of engineers, researchers, and industry experts. High autonomy, high impact, your work will shape the foundation of our platform. Competitive compensation with meaningful equity upside. #J-18808-Ljbffr