Senior Machine Learning Engineer
Senior Machine Learning Engineer
Posted 17 days ago
SENIORITY
Lead
About the role
- Build, train, evaluate, and deploy computer vision and multimodal models, taking them from early prototype through to production
- Design systems that infer structured attributes and spatial context from imagery, combining learned models with geometric and heuristic reasoning
- Train and fine-tune models on large, diverse real-world image datasets, and build the pipelines to curate and label that data at scale
- Work with vision-language models (VLMs) and build rigorous evaluation frameworks to measure their accuracy on our tasks
- Develop and benchmark high-performance image retrieval capabilities with embedding models and vector indexing strategies
- Optimize models for inference latency and throughput using techniques like distillation, quantization, and GPU acceleration
- Read current research, prototype novel algorithms from academic literature, and turn promising ideas into reliable production code
- Implement efficient, scalable data pipelines and inference infrastructure
- Develop high-performance tooling in ML and data engineering
- Experience building, training, evaluating, and deploying ML models in production
- Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models
- Strong software engineering ability to build and maintain complex systems and work with large-scale datasets
- Ability to solve open-ended problems and quickly learn new domains
- Comfort operating with significant ownership and autonomy, making pragmatic trade-offs between model sophistication, velocity, inference and business constraintsBS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience
- Experience inferring structured, real-world attributes from images
- Experience training models on large-scale, real-world image datasets
- Familiarity with vision-language models (VLMs)
- Ability to digest academic literature, prototype novel algorithms, and bridge the gap between research and production code
- Experience building LLM or VLM pipelines and the evaluation frameworks to measure their performance
- Experience in an ML role at a growth-stage startup
- Publications in major ML or computer vision conferences (e.g., CVPR, ICML, ICCV, WACV)
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
