• Develop, test, fine-tune, and optimize computer vision and object detection models using video and image datasets • Support the deployment of ML models in production or near-production environments • Analyze and process large-scale visual datasets, including annotation workflows and video sample preparation • Build new models and adapt pre-trained models to support operational and safety-focused use cases • Evaluate technical feasibility and prioritize AI/ML use cases according to business timelines and delivery goals • Collaborate with cross-functional stakeholders including Infrastructure, Operations, AI Engineering, and business teams to translate operational requirements into technical solutions • Contribute to the development of real-time detection and alerting capabilities using camera-based systems • Support rapid prototyping and MVP delivery initiatives within fast-paced environments

• Work with a cross functional team of scientists and engineers building foundational models that are used, tested, and trained on real world applications of generative design to the development of therapeutic biomolecules. • Develop and implement groundbreaking machine learning and protein science solutions. • Collaborate with computational and experimental scientists to establish these tools as a foundation of the discovery pipeline. • Work towards individual ownership and a leadership role in determining model architectures and research program needs. • Engage with open source and academic collaborations in the machine learning field.

• Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support. • Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business. • Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling. • Improve the ML development experience by building better operational patterns and advancing production-ready ML practices. • Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production. • Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems. • Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations.

Role Description We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines. Key Responsibilities - Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering - Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks - Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets - Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines - Work with structured and unstructured data at scale — including text, tabular, and time-series data - Monitor model performance in production and implement retraining and drift-detection strategies - Collaborate with engineering and product teams to translate data insights into actionable AI features - Document data schemas, model architectures, and pipeline logic clearly and thoroughly Qualifications - Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar) - Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets - Practical experience with model training, evaluation, hyperparameter tuning, and deployment - Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts - Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar) - Strong grasp of statistical concepts, data quality principles, and model performance metrics - Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered. Preferred Qualifications - Experience with distributed data processing frameworks (Spark, Dask, or similar) - Familiarity with vector databases and embedding-based retrieval systems - Background working with real-time or streaming data pipelines (Kafka, Flink, or similar) - Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) - Experience with data governance, lineage tracking, or compliance-aware data workflows Tools & Technology - Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy) - MLOps: MLflow, Weights & Biases, DVC, or equivalent - Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar - LLM platforms: Hugging Face, OpenAI, Anthropic, or similar - Cloud infrastructure: AWS, GCP, or Azure - Google Workspace, Slack, Zoom, and remote collaboration tools Application Note Please note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.