Machine Learning Researcher – Quantitative Trading

Oxford Knight; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Oxford Knight is expanding its Machine Learning and AI capabilities within a world‑class quantitative market‑making environment in London. You will use ML to extract signals from vast market data, build and test models and develop new trading strategies across cross‑asset teams.

The role blends quantitative research, trading and software engineering, with freedom to experiment and to attend leading academic conferences.

Qualifications

• Deep Machine Learning expertise from either an applied or academic environment.
• Strong programming and quantitative problem-solving skills.
• Experience analysing large datasets and building/testing ML models.
• Broad understanding of modern ML techniques and model architectures.
• Ability to balance cutting‑edge research with pragmatic, commercially useful solutions.
• Collaborative nature with excellent communication skills.

Responsibilities

• Use Machine Learning to extract signals and insights from vast datasets of market data.
• Analyse data, build and test models and develop new trading strategies.
• Split time between alpha generation within a trading team and broader ML research across the firm.
• Collaborate with research, trading and software engineering teams to maximize PnL and apply ML to real-world problems.
• Experiment with model architectures, feature transformations and hyperparameters while justifying choices.

Job description

Client:

One of the world’s top quantitative market makers is expanding its Machine Learning and broader AI capabilities. Already a market leader, the firm has invested extensively in its data and research infrastructure in recent years and is continuing to grow its ML teams to stay at the forefront of the industry.

As a cross-asset liquidity provider, the firm executes huge volumes of trades every day and works with enormous datasets, creating an unusually rich and fast feedback environment for applying Machine Learning to real-world problems.

Role:

You’ll use Machine Learning to extract signals and insights from vast datasets of market data, analysing data, building and testing models and developing new trading strategies.

The work sits at the intersection of quantitative research, trading and software engineering. Depending on your interests and expertise, your time could be split between alpha generation within a trading team and broader ML research across the firm.

The environment is extremely collaborative, with research and discoveries shared across teams to maximise PnL collectively. For example, insights developed while working with an FX trading team may have applications within Equities or other asset classes.

Beyond direct trading applications, the firm is also exploring how the latest developments in ML and AI can improve research, engineering and operational workflows across the business.

You’ll have significant freedom to experiment with model architectures, feature transformations and hyperparameters, while being expected to understand why techniques work and make pragmatic decisions about which approaches are appropriate for a particular problem.

Requirements:

• Deep Machine Learning expertise from either an applied or academic environment
• Strong programming and quantitative problem-solving skills
• Experience analysing large datasets and building/testing ML models
• Broad understanding of modern ML techniques and model architectures
• Ability to balance cutting‑edge research with pragmatic, commercially useful solutions
• Collaborative nature with excellent communication skills
• Trading or financial markets experience is not required
• Help shape the firm’s future ML direction and, to an extent, developments across the wider quantitative trading industry
• Work with enormous proprietary datasets in a rapid‑feedback trading environment
• Significant freedom to research and experiment with new ML techniques
• Opportunities to attend leading academic and industry conferences
• Good work‑life balance

Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.

Machine Learning Researcher – Quantitative Trading London