Run Fast, Reproducible Experiments:

Design and run experiment searches while keeping configs, seeds, and artifacts tidy so that any run can be reproduced with a single click.

Diagnose Bad Runs:

Trace failures across data, augmentations, labels, and training dynamics, implement quick fixes, and verify them with targeted evaluations.

Curate & Improve Data:

Define datasets, create focused error sets, manage sampling and class imbalance, and partner on labeling and QA to close the loop.

Own Evaluation:

Build and maintain a rigorous evaluation suite covering accuracy, robustness, latency, and cost, along with dashboards for trend tracking and failure clustering.

Prototype to Production:

Partner with backend engineers to convert promising ideas into production‑ready models, including tests, packaging, canary releases, and monitoring.

Optimise for Cost & Latency:

Profile training and inference, choose efficient architectures, and keep GPU hours and serving budgets in check.

Communicate Clearly:

Keep the experimental environment clean and well‑documented so that experimental knowledge is preserved and easy to share across the team.

Build the Function:

Establish research hygiene — coding standards, reviews, and documentation — and help shape hiring as the team grows.

Applied ML & Computer Vision Experience:

At least 2‑5 years of hands‑on experience working with real‑world image or video models, with a track record of delivering high‑quality solutions.

Educational Background:

Master’s or PhD in Computer Science or a related technical field with a strong foundation in computer vision and machine learning.

Proficient Programming Skills:

Exceptional programming abilities in Python and PyTorch, comfortable writing clean training and evaluation loops.

Experiment Operations:

Hands‑on experience with modern ML experiment platforms such as ClearML, Weights & Biases, or equivalents.

Data‑Centric Mindset:

Practical know‑how in augmentations, class imbalance, sampling strategies, label quality, and active‑learning basics.

Evaluation Rigor:

Fluency with metrics such as mAP, F1, IoU, and ROC‑PR, plus calibration, robustness testing, and dataset and version management for reproducible comparisons.

Software Engineering Best Practices:

Familiarity with version control (Git), testing frameworks, code reviews, and CI/CD pipelines.

Communication & Collaboration:

Strong English communication skills and the ability to turn findings into clear recommendations while working closely with Engineering, Product, and GTM.

Video & Tracking:

Experience with temporal models such as 2D/3D CNNs and transformers, multi‑object tracking, and motion and occlusion handling.

Foundation & Multimodal Models:

Familiarity with CLIP‑style pretraining, VLM adapters and LoRA, or lightweight finetuning of foundation models.

Scale & Search:

Experience with distributed training (DDP/FSDP), HPO frameworks such as Optuna or Ray Tune, and thoughtful compute budgeting.

Model Optimisation:

Experience with distillation, pruning, and quantisation, plus deployment with ONNX Runtime or TensorRT; edge and real‑time constraints a plus.

Data Tooling:

Experience with labeling operations, weak supervision, and synthetic data generation and augmentation pipelines.

Domain Context:

Prior work in video analytics for industrial or safety‑critical environments.

Kaggle Competition Participation:

Previous involvement in Kaggle competitions, demonstrating practical experience in tackling real‑world machine learning problems.

Ownership & impact:

Work on hard, real‑world vision problems that directly improve manufacturing for some of the world’s largest producers. Your models will run in factories across 15 countries.

Growth path:

This role is designed to evolve as our research capabilities grow — you’ll shape the technical direction and help build the team.

Culture:

We operate with flat hierarchies and a get‑things‑done mentality. You’ll work with ambitious colleagues from all over the world in a collaborative environment where the best idea wins.

Compensation & equity:

Competitive salary, meaningful stock option package and comprehensive benefits.

Location:

Modern office in the heart of Zurich; enjoy the quality of life of Switzerland and the dynamism of a high‑growth startup.