Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

C++ Quant Developer / Equities Pod/ London/ £ High Base

Eka Finance
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior RF Data Scientist / Research Engineer

Junior Data Scientist

Data Scientist

HR Data Analyst

Senior SQL & SSIS Data Engineer (Data warehouse)

Python Data Engineer

Role:-

 

This team member will be responsible for the implementation of technology to enable large-scale computational efforts in quantitative research, as well as related efforts, such as the preparation and transformation of data and other operational tasks. This role will work with senior technologists on the design and implementation of systems, and work closely with the quantitative research team to enable their mission

 

You will:-

 

Partner closely with the Senior Portfolio Manager to develop data engineering and prediction tools primarily for the systematic trading of equities

Develop software engineering solutions for quantitative research and trading

Assist in designing, coding, and maintaining tools for the systematic trading infrastructure of the team

Build and maintain robust data pipelines and databases that ingest and transform large amounts of data

Develop processes that validate the integrity of the data

Implementation and operation of systems to enable quantitative research (i.e. large scale computation and serialization frameworks)

 

 

 

Requirements :-

 

Master’s or PhD in Computer Science, Physics, Engineering, Statistics, Applied Mathematics, or related technical field appropriate to a computational background

Expert in C++

Advanced programming skills in Python

Strong Linux-based development

Knowledge of machine learning and statistical techniques and related libraries

Experience as a quantitative developer supporting an intraday (or faster) system ( 3 years experience at least)

Experience with the development practices of large tech (Google/Meta, etc.) or finance firms

Experience with financial data

Approx. 3-4 years of professional experience in a computer science/computational role

Experience working in a technical environment with DevOps functions (Google Cloud, Airflow, Influx DB, Grafana)

 

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.