Deep Learning Researcher / Engineer

Real greenfield DL research, no LLM prompting or fine-tuning.

Do you feel like most ML researchers are working on heuristics for generative models or image recognition? If you prefer more rigorous approaches for recognizing abstract shapes, join us at Qminers and apply your knowledge on time series data!

Apply

Position

Deep Learning Researcher / Engineer

Employment

Full-time

Work Model

On-site

Location

Spork Palace, Prague 1

Team

Deep Learning Researcher

Interested in working for Qminers?

Deep Learning Researcher / Engineer

Email successfully
sent, thank you!

• Work with massive financial-market datasets and get them ready for deep learning.
• Design, train, and iterate on large deep neural networks over massive collections of numerical time series.
• Train, tune, and evaluate deep models, and read success off real evaluation metrics, not a benchmark.
• Spend real, non-trivial time on feature engineering, because on this data that is where the signal is won.
• Own the loop end to end, from raw data to a model that earns its place in production.

Most of our day is thinking, arguing toward the best solution, and coding. We like results reasonably fast, in both development and runtime, and we give people real room to run and a direct line to the result.

Qminers is a Prague-based algorithmic trading firm competing on four continents, 24/7. Founded in 2012, we delivered a Sharpe ratio above 10 year after year for a decade. We are a team of around 50: mathematicians, quants, software engineers, and machine learning researchers.

Excellence is not a slogan here, it is the baseline.

Must have

• A master’s degree with excellent results in artificial intelligence, or another quantitative field with a strong ML/AI track record.
• Solid command of ML and deep learning methods and algorithms.
• A strong foundation in mathematics, statistics, and probability.
• Extensive experience training and evaluating ML/DL models.
• Willingness to spend substantial, non-trivial time on feature engineering.
• Strong Python and the data science stack: NumPy, pandas, matplotlib, scikit-learn, and PyTorch.

Nice to have

• Experience taking models from research into production or a live-evaluation setting.
• A feel for both sides of the role: the theory of a good model, and the engineering that makes it run and reproduce.
• Experience with time series specifically, or with sequence models on noisy, real-world data.

We are honest about fit. This is not a demo and it is not a benchmark: the data is genuinely hard, the signal is faint, and a result only counts when it holds up on real metrics. If you want to be only a researcher who hands off, or only an engineer who ships someone else’s model, this is the wrong seat. If you want to be both, it is the right one.

The best argument wins.

Meritocracy is in our DNA.

You own the outcome.

Responsibility here is total, the wins and the mistakes alike.

Your rivals are outside, not inside.

Excellent colleagues are the elevator up, not the competition.

Interview process

01

Application

Through the web form.

02

Screening call

A short conversation to align expectations. You know where you stand.

03

Technical screening

An online test and a short technical call.

04

Take-home task

A real problem, a reasonable amount of time, and detailed feedback either way.

05

On-site interview

One-to-one conversations with the team, and lunch together.

06

Decision within a week

A clear answer, and if it did not work out, what was missing so you can try again.

Reading list

If you want to prepare thoroughly, here’s our recommended reading list. It’s not required, but it can give you a head start.

Pdf

Recommended Interview Resources (DL/ML)

446 KB

Base salary

Every role has a published salary range, and the more experience and results you bring, the higher you move within it.

Profit share & bonuses

Everyone holds a share of net profit, up to low single-digit percentages depending on impact. Around a quarter of the firm’s total annual profit goes into bonuses, re-evaluated every year on real, measurable impact.

Shadow equity

A meaningful share in the firm’s performance, a standard part of compensation for senior contributors.

ESOP

For the most senior people, genuine ownership in what the firm becomes. The details are worth a conversation.

We publish our annual results, and anyone inside the firm can see how Qminers is paid and how the current year is shaping up. Financial transparency is a given, not a perk.

Interested in working for Qminers?

Send an email to hr@qminers.com or fill out the form.

Are you an experienced quant? A programmer with a passion for C++ or Python? Or are you a recent graduate with a specialization in data? We’re always keen to hear from you!

Email successfully
sent, thank you!