---
title: 'Machine Learning Engineer | Betty CA at Betty'
canonical: 'https://feeny.ai/job/machine-learning-engineer-betty-ca-betty-sofia-226a9vnnyne6'
type: 'job'
last_seen: '2026-10-31'
---

# Machine Learning Engineer | Betty CA at Betty 

- **Company:** Betty 
- **Location:** Sofia, Bulgaria
- **Posted:** 2026-10-28
- **Last confirmed live:** 2026-10-31
- **Apply:** https://job-boards.greenhouse.io/bettyjobboard/jobs/5277907007

## Job description

## About Us

Betty is an innovative entertainment company pioneering at the intersection of real-money online casinos and casual mobile gaming.

Betty Canada is a scale up company accredited by the Alcohol and Gaming Commission of Ontario (AGCO) as a B2C operator in February 2023, we've set a new standard in the industry.

Our mission is to redefine the online casino experience by offering a uniquely transparent environment where players can relax, unwind, and enjoy themselves safely. We are committed to accessibility, fairness, and inclusivity, fostering a community of like-minded individuals who value ethical gaming practices and prioritize our players' safety and enjoyment above everything else.

## Our Values

- We are honest: we value honesty in all aspects.
- Bring the Olives: we offer premium customer experience.
- Think Big: we believe in always striving for more.

Key responsibilities:

- Design, build and ship AI solutions based on LLMs, such as AI agents, retrieval-augmented generation (RAG) and automated analysis and reporting, that teams across the business use every day.
- Help shape how Betty adopts AI: find high-value use cases, evaluate models and tools, and set up checks that AI outputs are accurate, reliable and cost-effective.
- Partner with the marketing and user acquisition teams on channel and campaign performance, using metrics such as CAC, ROAS, CPA, first-time depositors (FTDs), conversion rates and predicted LTV.
- Build marketing models such as predicted LTV for newly acquired players, attribution, marketing mix modeling, incrementality measurement and budget allocation across channels.
- Work with analysts, marketing, product and business stakeholders to turn business questions into clear requirements, success metrics and use cases.
- Work with large event-level data in Redshift, and combine and clean data from internal and external sources such as player activity, transactions, payments, CRM and marketing platforms.
- Build statistical and machine learning models for problems such as player lifetime value, churn and retention, segmentation, forecasting and driver analysis.
- Form and test hypotheses about player and commercial behavior, and turn the answers into clear recommendations, including how they affect our players.
- Find opportunities and inefficiencies across player segments, games, providers, payment flows and channels, and run deep-dives that support product launches and marketing campaigns.
- Support experimentation and A/B testing: define success metrics, analyze results and know when a result is real.
- Take models from prototype to production and own them end to end, from batch jobs on daily, weekly and monthly schedules to real-time inference where speed matters.
- Build and run real-time pipelines that stream player events (for example with Kafka or Flink), compute features on live data and serve predictions with low latency, such as real-time churn and retention signals.
- Set up the MLOps foundations: CI/CD for models, model registry and versioning, a feature store, automated retraining, and monitoring for drift, data quality and model performance.
- Present results through dashboards and clear recommendations to any audience, up to C-level.
- Help other teams use data and AI on their own, through self-service analytics and shared environments where they can safely deploy their own AI tools.
- Work within the rules of a regulated market, including responsible gambling and data privacy requirements.

## Requirements

- 3+ years of experience as a Data Scientist/ML Engineer.
- Strong knowledge of statistics and machine learning methods, such as predictive modeling, classification, clustering, regression, and optimization.
- Advanced Python with strong software engineering practices (testing, code review, Git), and advanced SQL with Redshift or a comparable cloud data warehouse.
- Proven experience deploying and running ML models in production, including CI/CD, containers (Docker), model versioning, automated retraining and monitoring for drift and performance.
- Hands-on experience building solutions with LLMs and generative AI, such as retrieval-augmented generation (RAG), AI agents and tool use, prompt engineering and NLP.
- Experience working with LLM APIs and frameworks, and with evaluating AI outputs for accuracy, reliability, and cost.
- Good judgment on where AI adds value and where a simpler method works better, and you keep up with new models and tools.
- Proven experience applying models to business problems such as forecasting, segmentation, lifetime value, retention, churn, or engagement.
- Experience with large datasets (100M+ rows and many variables).
- The ability to work with messy, incomplete, and changing data and still produce reliable results.
- Clear communication: you can explain complex work to people who don't want the detail, and manage expectations honestly.
- Comfortable working in a fast-paced environment.
- Fluent English, written and spoken.

Nice to have:

- Strongly preferred: experience with marketing data and its terminology, such as user acquisition, CAC, ROAS, FTDs, attribution, marketing mix modeling, incrementality, predicted LTV, and campaign and creative performance.
- Online casino experience from any part of the business, or experience in iGaming, sports betting, gaming, fintech or another high-volume consumer business.
- Hands-on Tableau or a comparable BI tool.
- Experience with streaming data and real-time ML: event streaming (Kafka, Flink or similar), feature computation on live data and low-latency model serving through APIs.
- Hands-on experience with MLOps and orchestration tools such as MLflow, Airflow, Kubernetes, Terraform or a feature store.
- Experience working with APIs and third-party data sources.
- Experience running LLM applications in production, including vector databases, agent frameworks, output evaluation and cost monitoring.

## What we offer

- Competitive salary
- Performance-based bonus
- Career and skills development opportunities
- Fun and collaborative team environment
- New modern office space

## About Betty 

## Company Overview
- **One-liner**: Betty is building a transparent, ethically-driven online casino platform that merges real-money gaming with casual mobile entertainment.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: New York, New York, United States
- **Founded**: 2022
- **Founders**: Annie Z. (Co-Founder) – other co-founders not publicly disclosed

## Core Business
- **Primary industry**: Online casino / iGaming / mobile gaming
- **Target customers**: B2C – adult players in regulated markets (currently Ontario, Canada)
- **Mission or purpose**: “Changing gambling for good” by offering a safe, transparent, and community-driven alternative to traditional online casinos.

## Products & Services
- **[Betty Casino](https://betty.ca)**: Real-money online casino platform featuring slots, table games, and live dealer experiences. Designed with fairness, no bots, and a focus on player well-being. Licensed by the Alcohol and Gaming Commission of Ontario (AGCO) since February 2023.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Annual Revenue ~$2.2M (2025 estimate); Total Funding $10.5M across 4 rounds (Pre-Seed, Seed)
- **Notable Investors/Partners**: CEAS Investments (lead in 2024 Seed round), Karlani Capital (lead in 2023 Seed round), Happy Hour Solutions; strategic partner Wyzia (1000% player growth through partnership)
- **Growth Signals**: Headcount grew 106% YoY (215 employees); expanded to 6 offices across 4 countries; acquired Kirkland Lake Bingo Hall (March 2026); launched first franchise in February 2023

## Competitive Advantages
- **Transparency-first model**: No bots, no hidden mechanics – a stark contrast to industry norms.
- **AGCO accreditation**: Operates under strict Ontario regulation, signaling trust and compliance.
- **Community focus**: Brand built around player safety and ethical gaming, differentiating from traditional casinos.
- **Shared platform**: Centralized technology stack allows rapid, locally-relevant launches in new markets.

## Strategic Focus
- **Global expansion**: Building a “casino brand group” by launching locally tailored operations in multiple jurisdictions, powered by a unified tech platform.
- **Product innovation**: Continuing to blend mobile game design with real-money casino mechanics to attract a broader, younger audience.
- **Partnership growth**: Deepening relationships like the Wyzia collaboration to drive player acquisition and retention.

## Why Work Here
- **Culture**: Emphasis on “real people, looking out for our players” and a supportive, transparent environment. Teams are described as “builders and operators” focused on great products.
- **Work model**: Hybrid/office presence in New York (HQ), Toronto, Sofia, London. Engineering roles based mainly in Sofia.
- **Engineering culture**: Data-driven (hiring Lead Data Scientist, Senior Data Engineers, Insight Analysts); modern tech stack; opportunities to shape a new category.
- **Notable perks**: Not explicitly listed, but the company offers a fast-growing startup environment with exposure to product, growth, and compliance.
- **Caution**: LinkedIn employer rating is 2.3/5 (4 reviews), with low marks for culture (2.1) and career development (2.5). Candidates should seek current employee perspectives.

## Sources
1. [LinkedIn – Betty Gaming](https://www.linkedin.com/company/bettygaming)
2. [Greenhouse Job Board – Betty](https://job-boards.greenhouse.io/bettyjobboard)
3. [Greenhouse Job Board – Betty Gaming Canada](http://job-boards.greenhouse.io/bettygamingca)
4. [JobAlmanac – Betty](https://www.jobalmanac.com/companies/bettyjobboard)

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