Prepare your taste buds... Backed by Lightspeed.
About the role
We're hiring various roles from Senior level to Senior Staff level Machine Learning Engineers. You will own model architecture across several connected technical areas: search ranking, homepage and topic recommendations, retrieval, and query understanding. You will be accountable for how those pieces fit together, not only for any single model. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- MS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience
- 4-8+ years building and shipping machine learning systems. For staff level above, includes 3+ years operating at senior-level scope: setting technical direction across multiple teams, model families, or systems
- Deep experience in recommendation systems, ranking, or information retrieval at scale, in production and under real latency and cost constraints
- Proven track record with production deep learning in TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUs
- Strong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug as well as build
- Fluency with experimentation: designing A/B tests, choosing the right guardrails, and recognizing when an offline metric is misleading you
More about this role
About The Opportunity
We're hiring various roles from Senior level to Senior Staff level Machine Learning Engineers. You will own model architecture across several connected technical areas: search ranking, homepage and topic recommendations, retrieval, and query understanding. You will be accountable for how those pieces fit together, not only for any single model.
You will set technical direction alongside other engineers, product managers, and platform partners. You will raise the bar on how the organization builds, evaluates, and operates models, and mentor the engineers around you through design reviews, code reviews, and direct feedback. We expect you to challenge technical decisions across teams when the engineering case is clear, and to bring evidence when you do.
Our team practices end to end project ownership, and our work focuses heavily on personalized recommendation, retrieval, and classification from catalog content and clickstream. Deep neural networks, learned embeddings and approximate nearest neighbor retrieval, transfer learning from pre-trained large scale models, calibration, classic regressions, fine tuning, and large language models all have a place in our...
Browse similar: AI jobs · AI startup jobs · Startup jobs · New York