Machine Learning Engineer / Data Scientist

Madfish; Remote; GBP 125,000 - 150,000; Full time

Job summary

A forward-thinking AI company is seeking a talented Machine Learning Engineer to develop and optimize models that extract insights from multimodal data. This remote role requires strong expertise in LLMs and computer vision as well as proficiency in Python. You will conduct experiments and collaborate with cross-functional teams to enhance model performance and drive real-world AI applications. Join us to impact the future of AI technology in an innovative environment.

Benefits

Work on cutting-edge AI projects

Collaborate with a talented team

Opportunity to impact real-world applications

Qualifications

• Strong understanding of LLMs and computer vision.
• Proficiency in Python for data manipulation and modeling.
• Ability to design and rigorously evaluate ML experiments.

Responsibilities

• Develop and optimize models for data extraction and insights.
• Conduct experiments to benchmark models and improve accuracy.
• Collaborate with cross-functional teams on modeling tasks.

Skills

Understanding of LLMs

Computer vision expertise

Data manipulation in Python

Strong communication skills

Tools

PyTorch

TensorFlow

scikit-learn

AWS

Docker

Kubernetes

Job description

Location

Location: Remote (must overlap with EST business hours)

Contract Type: B2B

English Level: C1 (Advanced)

Compensation: Gross, TBD

Holidays: 10 public holidays (vacation & sick days unpaid)

About the Role

We are seeking a talented Machine Learning Engineer with expertise in LLMs, computer vision, and multimodal data processing. You will develop and optimize models to extract insights, generate embeddings, and model relationships across text, images, and structured metadata. Your work will power similarity search, recommendation systems, and other AI-driven applications.

Core Responsibilities

• Develop, fine-tune, and evaluate LLM and computer vision models for information extraction, embeddings, and relationship modeling across multimodal data.
• Build and refine vector embeddings for text, metadata, and visual representations to support similarity search and recommendation systems.
• Conduct systematic experiments to benchmark models, compare approaches, and improve accuracy, robustness, and retrieval quality.
• Define evaluation strategies for model performance, embedding quality, and search relevance.
• Prepare, clean, and analyze multimodal datasets from drawings, OCR outputs, and structured metadata.
• Perform feature engineering for visual, textual, and structured data used across ML models.
• Validate data consistency, identify issues, and propose improvements to labeling, preprocessing, and dataset structure.
• Collaborate on evaluating similarity search pipelines using Milvus, OpenSearch, or Elasticsearch.
• Analyze retrieval performance, investigate mismatches, and iterate on embeddings and preprocessing logic.
• Prepare trained models for deployment with clear documentation, evaluation reports, and defined inputs/outputs.
• Collaborate with engineering teams on inference pipelines and deployment strategies.

Monitoring & Continuous Improvement

• Track model performance metrics (accuracy, recall, drift) and investigate error patterns.
• Recommend strategies for new data collection, labeling, or retraining to improve model quality.
• Support continuous improvement cycles driven by feedback and new data.

Collaboration & Documentation

• Work with AI engineers, data engineers, QA, and business analysts to translate requirements into modeling tasks.
• Document datasets, experiments, and modeling decisions clearly.
• Communicate insights and results effectively to both technical and non-technical stakeholders.

Required Skills & Experience

Machine Learning

• Strong understanding of LLMs, computer vision, embedding models, and experimentation workflows.
• Ability to design and rigorously evaluate ML experiments.

Frameworks & Tools

• PyTorch, TensorFlow, scikit-learn
• Hugging Face Transformers
• OpenCV, TorchVision

Programming & Data

• Proficiency in Python for data manipulation, experimentation, and modeling
• Experience preparing and analyzing large-scale datasets
• Expertise in feature engineering and data quality assessment
• Familiarity with Milvus, OpenSearch, or Elasticsearch for similarity search
• Experience with AWS, S3, DynamoDB, EKS, Docker, Kubernetes
• Understanding of ML monitoring using OpenTelemetry, Prometheus, and Grafana

Collaboration & Communication

• Strong ability to communicate modeling insights and data findings clearly
• Comfortable working cross-functionally with engineering, product, and business teams

Why Join Us

• Work on cutting-edge AI and multimodal ML projects
• Collaborate with a talented, cross-functional team
• Opportunity to impact real-world applications with AI