Machine Learning Research Engineer
SS&C Technologies
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇬🇧 United Kingdom
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
AWSAzureCloudGoogle Cloud PlatformHadoopNumpyPandasPythonPyTorchScikit-LearnSparkTensorflow
About the role
- Conduct research into novel machine learning algorithms, deep learning architectures, and statistical models relevant to intelligent automation, natural language processing, computer vision, and predictive analytics.
- Design, develop, and implement robust and scalable machine learning models and pipelines, from data ingestion and feature engineering to model training, evaluation, and deployment.
- Collaborate with product managers and other engineering teams to translate research findings into production-ready features and components for Blue Prism's platform.
- Evaluate the performance of machine learning models, analyze results, and iteratively refine models for improved accuracy, efficiency, and real-world applicability.
- Stay abreast of the latest advancements in machine learning research and industry best practices, actively contributing to the company's knowledge base and innovation strategy.
- Participate in code reviews, contribute to technical documentation, and mentor junior engineers on machine learning principles and techniques.
- Experiment with and develop solutions for challenging problems in areas such as anomaly detection, process mining, intelligent document processing, and human-in-the-loop AI.
Requirements
- PhD or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a closely related quantitative field.
- Proven experience (3+ years) in a machine learning research or engineering role, with a strong portfolio of projects demonstrating practical application of ML techniques.
- Expertise in programming languages such as Python, with proficiency in relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Pandas, NumPy).
- Solid understanding of machine learning fundamentals, including supervised/unsupervised learning, reinforcement learning, deep learning, and statistical modeling.
- Experience with cloud platforms (e.g., Azure, AWS, GCP) and MLOps practices for deploying and managing ML models in production.
- Familiarity with data processing and big data technologies (e.g., Spark, Hadoop) is a plus.
- Strong problem-solving skills, analytical thinking, and the ability to conduct independent research.
- Excellent communication skills, both written and verbal, with the ability to articulate complex technical concepts to diverse audiences.
- Ability to work effectively in a collaborative, fast-paced, and agile environment.
Benefits
- Health insurance
- Flexible working arrangements
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningdeep learningstatistical modelingfeature engineeringmodel trainingmodel evaluationanomaly detectionprocess miningintelligent document processingreinforcement learning
Soft skills
problem-solvinganalytical thinkingindependent researchcommunicationcollaborationmentoring
Certifications
PhD in Computer ScienceMaster's degree in Machine LearningMaster's degree in Artificial IntelligenceMaster's degree in Statistics