SteelEye is a fast-moving RegTech (Regulatory Technology) start-up that is helping financial companies (e.g. banks, investment firms, brokers, hedge funds, and asset managers) meet their obligations under various global financial regulations.

Our work enhances financial compliance, prevents market abuse, and promotes trust in the financial markets. Our people are passionate about leveraging data and technology to make this happen.

As an ML Engineer, you’ll help build and deploy data science capabilities that power our communications and trade surveillance analytics. You’ll work across feature engineering, model development and experimentation, collaborating with engineering and product stakeholders to deliver reliable, measurable improvements.

Key responsibilities:

Build and iterate on NLP/comms surveillance models and analytics to extract signals from communications datasets (e.g., classification, clustering, entity extraction, similarity)
Develop trade surveillance analytics and features that improve detection quality and reduce false positives
Perform feature engineering on structured and unstructured data, working with Python + SQL to create reusable, testable datasets/features
Design, run, and evaluate experiments (offline metrics, error analysis, bias/edge-case analysis), and communicate findings clearly
Deploy models and pipelines with engineering best practices (testing, monitoring, versioning, reproducibility), owning assigned components end-to-end within the scope of the task
Collaborate with engineering/product to define requirements and translate them into implementable specifications
Implement and orchestrate workflows using Airflow/Prefect; integrate outputs into downstream systems such as Elasticsearch where needed
Maintain high code quality through code reviews, documentation, and continuous improvements

Requirements:

2–3+ years of relevant experience in data science / ML engineering / applied analytics roles
Strong Python skills, including Pandas/Numpy, and solid SQL
Applied ML experience with scikit-learn plus at least one deep learning framework (PyTorch or TensorFlow)
Experience taking models from experimentation to production (packaging, deployment patterns, monitoring/metrics, retraining considerations)
Familiarity with cloud and containers: AWS, Docker, and ideally Kubernetes
Experience building/orchestrating pipelines with Airflow and/or Prefect
Comfortable working with search/data retrieval systems such as Elasticsearch
Strong collaboration and communication skills; able to work effectively and deliver scoped work consistently

Interview Process: The interview process is structured to assess candidates thoroughly across various competencies and skills relevant to the role. Here's a description of each stage:

Intro call with Human Resources Business Partner
Technical Assessment
First Stage Overview Interview with our senior Data Engineers
Final Interview with our Head of Data Engineering

About SteelEye:

SteelEye is a dynamic B2B FinTech company dedicated to enabling financial institutions, including banks, investment firms, brokers, hedge funds, and asset managers, to efficiently and accurately meet their regulatory obligations under various global financial regulations. As the finance industry’s pioneering integrated trade and communications surveillance solution, SteelEye empowers financial firms with data-driven tools and comprehensive insights, all from a single platform, allowing them to focus on what truly matters.

At SteelEye, we pride ourselves on fostering a diverse, equitable, and inclusive workplace where everyone's contributions are valued. We are committed to being an inclusive employer, embracing individuals of all races, religions, gender identities, sexual orientations, national origins, ages, socioeconomic statuses, medical conditions or disabilities, and other protected statuses. We actively seek talent from diverse backgrounds, experiences, personalities, and perspectives, believing that our differences drive innovation and success.

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