Overview This position is fundamentally different from most job roles. We are a small group of entrepreneurs, PhDs, strong SW engineers, full-time traders, and international business people who enjoy the puzzles of the financial markets as peak intellectual entertainment. We are building a Rust-based, agentic algorithmic trading platform targeting a highly lucrative niche. The core system is already largely in place, and we are now entering the monetization phase. Our compensation model is intentionally fair and uncommon : It isn’t a "founder takes all, salary-only for all others” setup. Financial independence is a possible outcome of sustained team performance. We are looking for strong generalist engineering support to further enhance, extend, and maintain our algo-trading engine. This is a once-in-a-lifetime opportunity for someone who truly understands our approach and wants to build something exceptional with a small, high-caliber team where everyone owns a meaningful piece of the outcome.
Tasks Full stack, low-latency programming (Rust) - [MUST HAVE]: Build and maintain high-performance, modular trading systems in Rust (backend, bots, optimization-system, etc)
Data engineering (Python): Develop TB-scale ETL pipelines using Python and Dagster for market data, features, backtests, and optimizations
Time-series data platforms: Design and optimize TimescaleDB/PostgreSQL + S3 based data warehouses (ingestion, compression, query performance)
Infrastructure & Kubernetes: Operate IaC-driven infrastructure on Hetzner (Terraform) and production K3s clusters (Helm, Kustomize)
Monitoring & observability: Build dashboards and alerting for trading systems, data quality, latency, and system health + setup the TB-scale monitoring system behind it
UI development: Create internal and end user-facing analytics dashboards + React based user interfaces
Practical algorithmic trading: Design trading signals, trade management, and large-scale strategy optimization frameworks
Quantitative research & ML/AI: Develop, evaluate, and deploy quantitative models, ML-based trading signals (feature engineering, validation, inference) and practical AI-agents
Team collaboration: Work closely with traders and bot operators to monitor live systems, investigate issues, and improve performance
Requirements We search for an "almost complete", hand-on quantitative full stack developer who covers at least 5 of these 9 areas with expert knowledge. We do not care about formal degrees ; however, they are often a strong indicator of capability. We are equally open to being proven wrong.
Below is an overview which frameworks/technologies we use:
Full stack, low-latency programming (Rust) - [MUST HAVE]: SW best practices for Rust; Rust frameworks like Axum, Tokio, Rayon, Serde, tokio-postgres, clorinde, etc; Distributed, low-latency coding with Rust; Contract-First API Development (OpenAPI)
Data engineering (Python): Dagster with TB-scale data pipelines; Databento/Bybit/etc market data APIs; DB/TSDB migrations with Refinery
Time-series data platforms: S3, TimescaleDB/PostgreSQL; PGMQ messaging systems; High-performance, compressed TB-scale ingestion pipelines for time-series data
Infrastructure & Kubernetes: Hetzner (ARM/AMD nodes); Terraform; K3s Kubernetes clusters; Helm; Kustomize; Autoscaling nodes via K8s+HCloud; Github actions CI/CD; ArgoCD; CNPG, Traefik
Monitoring & observability: Grafana stack (Grafana, Mimir, Loki, Alloy, k6); SRE knowledge
UI development: Grafana charts; Volkov Business Suite for Grafana; Apache ECharts; React (JS/TS)
Practical algorithmic trading: Practical knowledge about how to optimize trading stratgies and financial data; Solid knowledge of market microstructure and order-flow–based trading concepts, e.g. bid/ask dynamics, tick and lot structure, order book behavior, absorption, and volume/delta imbalances; Quantower; C#/.net for trade execution adapters
Quantitative research & ML/AI: Theoretical quant knowledge (e.g. Regime clustering, mean reversion, Stoch volatility models, Brownian motion, CVaR, Monte Carlo sim, Sharpe ratio, etc); ML/AI: Deep learning architectures, LLMs, Reinforcement learning, Hyperparameter optimization, etc; Practical AI skills: Claude agents/skills, n8n, MindsDB
Team collaboration: Strong team and communication skills in English (German is a plus); Tools: FumaDocs, MS Teams, Jira
All of our core developers joined with real expert knowledge in at least 5 of these areas. Rust is the only strict must-have. Our expectation is that you bring the intrinsic motivation to become highly proficient across all of them over time .
We do not believe in narrow specialization. Instead, we provide an environment with strong sparring partners, deep technical discussions, and real production responsibility—allowing you to grow rapidly. You will work alongside exceptional engineers, quants, and traders, all pushing toward the same goal
Benefits Due to the high potential payouts (up to $1M per year) and VSOP participation at the core company, this role is fully profit-share based. We expect an entrepreneurial mindset from every team member. Don’t apply if this isn’t for you
You will collaborate with an elite group of engineers —among the strongest you’re likely to have worked with. All of our core developers are driven by pure intrinsic motivation and have spent years working day and night to reach this level
Work on a high-performance, production-grade trading platform, covering the full stack of the engine — from low-latency execution to data, optimization, and infrastructure. We value strong generalists who can reason across systems and effectively leverage modern AI tooling
Risk-free probation period for both sides (part-time) — ideal to test long-term fit while keeping your current job (10–20h/week over 6 months)
International, remote-first culture
We have a very streamlined hiring process:
Stage 01 — Cover Letter (Skill Self-Assessment)
Use the cover letter to explain why you are the right candidate for this role. Additionally, assess your skill level in each area listed above and briefly justify your rating with concrete experience or examples
Stage 02 — Interview (~2 hours)
A deep, wide-ranging (mainly technical) conversation — no LeetCode nonsense, no live coding. This is a real discussion between engineers. We will explore all areas listed above, both broadly and in depth, to understand how you think, design, and reason at scale. All questions are based on real problems and recent developments in our engine — no artificial data-structure puzzles or interview-theater questions
Stage 03 — Probation Period (6 months, part-time)
A six-month trial collaboration to ensure a strong mutual fit. You will already work on real systems and contribute meaningfully. We will also meet in person during this phase. Compensation during this period is already structured as a reduced but meaningful profit share .
Stage 04 — Long-Term Collaboration Offer
If the fit is right on both sides, we will offer you a long-term collaboration with a very competitive compensation package ( Profit-Share + VSOP )
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