Software Engineer (ML/Python)

TL;DR

Software Engineer (ML/Python): Designing and building a dynamic pricing engine for a live sports-wagering product with an accent on probabilistic modelling and real-time event ingestion. Focus on implementing Bayesian core updates, pricing layer optimization, and risk management systems to maximize revenue.

Location: Remote or from offices in Moscow, Ivanovo, Taiwan, or Vietnam

Company

A full-cycle engineering company building complex international products ranging from sports-wagering platforms to AI voice assistants and smart electronics.

What you will do

• Design and build a streaming ingestion pipeline for shot-level events using a real-time message bus.
• Develop a probability model using Bayesian inference with online updates (e.g., Beta-Binomial / Dirichlet-Multinomial).
• Implement a pricing layer to convert probabilities into offered odds using a principled margin model.
• Create an optimization layer using contextual bandits or Thompson sampling to maximize revenue under risk constraints.
• Build a risk and exposure engine for live liability tracking, stake caps, and anomaly detection.
• Develop visualization dashboards for model state, P&L, and exposure for non-technical stakeholders.

Requirements

• Fluent, confident spoken and written English (C1+) for direct interaction with an Australian client.
• 8+ years of experience building production software with strong Python skills and software engineering fundamentals.
• Strong background in applied ML and statistics, specifically probabilistic modelling and Bayesian inference.
• Hands-on experience with real-time streaming data systems (e.g., Kafka, Kinesis).
• Practical experience with end-to-end MLOps, including ingestion, serving, and monitoring.
• Ability to independently build monitoring surfaces, dashboards, and metrics.

Nice to have

• Background in sports betting, quantitative trading, pricing, or actuarial work.
• Familiarity with probabilistic programming frameworks like PyMC, Stan, or NumPyro.
• Experience with reinforcement learning and bandits for online decision-making.
• Experience with fraud, abuse, or anomaly detection on behavioral data.
• Knowledge of responsible gambling regulations.

Culture & Benefits

• Opportunity to work on highly complex, non-standard technical challenges.
• Flexible work arrangement: remote or office-based.
• Competitive salary and paid vacation according to the labour code.
• Long-term collaboration prospects in a level-headed professional environment.