SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Encord is the universal data layer for AI, helping 300+ AI teams train and run models on the right data. The platform indexes, curates, annotates, and evaluates data across the full AI lifecycle from development through production. Trusted by Woven by Toyota, AXA, UiPath, Zipline, and others. The company has raised $60M in Series C funding and operates a team of 100+ at the frontier of AI.
In this role, you will own how state-of-the-art computer vision and multimodal models are brought into a production platform used by hundreds of AI teams worldwide. You'll take ambiguous, often unsolved problems, decide how to approach them, and see them through from research to reliable, scalable features in production.
Key responsibilities include:
- Own ML initiatives end-to-end, from scoping and model selection through deployment, monitoring and iteration in production
- Evaluate and adapt the latest models (foundation models, VLMs, segmentation and tracking models) and decide what's worth bringing into the platform
- Tackle open-ended statistical, geometric and algorithmic problems where there's no established playbook
- Design ML systems and pipelines that scale across large, multimodal datasets and hundreds of customers
- Partner with product and engineering leads to shape the ML roadmap and turn customer needs into technical direction
- Set engineering standards, lead design reviews and mentor other engineers
- Play an active role in hiring and building out the ML team
- Work with Python, PyTorch, OpenCV, CUDA, Kubernetes and GCP/AWS
You'll work closely with product, engineering and the human data team, with real influence over the ML roadmap, technical direction and team growth.
Requirements:
- 5+ years in ML engineering (or 5+ years with a relevant PhD), including shipping and maintaining models in production at scale
- Deep experience in computer vision and PyTorch, with hands-on work on modern architectures (transformers, foundation models)
- Track record of leading technically complex projects from idea to production
- Strong grounding in maths, algorithms and applied ML
- Experience with production ML infrastructure, such as model serving, GPU optimisation and distributed training
- Bonus: multimodal data (video, 3D/LiDAR, medical imaging), active learning or data-centric AI
The ideal candidate is an owner who takes ambiguous problems, defines the approach and drives them to production; a pragmatist who knows when to reach for latest research and when a simpler solution will do; a multiplier who raises the bar through mentoring and clear communication; and a customer champion who cares about how work lands with real users.
