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Founding Engineer (Machine Learning & Backend Focused) - Job Opportunity at AllMind AI Inc.

Waterloo, Canada
Full-time
Mid-level
Posted: July 1, 2025
On-site
CAD 70,000-120,000 per year

Benefits

Equity participation through stock options providing direct ownership stake in company growth potential
Premium on-site fitness facilities eliminating external gym membership costs and promoting work-life integration
Regular company events fostering team cohesion and networking opportunities within the startup ecosystem

Key Responsibilities

Architect and optimize enterprise-grade backend systems using cutting-edge Golang and Python technologies to support institutional-level data processing requirements
Design and maintain sophisticated data pipelines for machine learning models using PyTorch, directly impacting investment decision-making capabilities
Create intuitive user interface components and comprehensive analytical dashboards that serve as primary touchpoints for hedge fund professionals
Enhance mission-critical APIs and integrate AI models while establishing scalable system architecture capable of handling institutional trading volumes
Transform and optimize data lake architecture to support advanced machine learning initiatives serving the financial services sector
Engineer ultra-low-latency data pipelines processing Level 3 market data with lossless storage capabilities, ensuring real-time availability for trading decisions
Develop real-time financial features including rolling order-book analysis, trade monitoring, and liquidity assessment tools
Build, validate, and deploy production machine learning models for short-term price forecasting and market microstructure signal generation
Continuously optimize existing data infrastructure to maintain competitive advantage in high-frequency trading environments

Requirements

Education

Bachelor's degree or higher in Computer Science, Software Engineering, or a related field, with a strong academic record

Experience

3 years preferred in ML

Required Skills

Proven experience in both machine learning and backend development Strong proficiency in Python and Golang, including relevant frameworks and libraries (e.g., PyTorch, pandas, SciPy) Experience thriving in fast-paced, iterative development environments A self-directed work ethic with the ability to navigate ambiguity and prioritize tasks effectively Excellent communication skills for collaborating with both technical and non-technical stakeholders Authorization to work in Canada
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Sauge AI Market Intelligence

Industry Trends

The convergence of artificial intelligence and quantitative finance is accelerating rapidly, with institutional investors increasingly seeking unified platforms that can process vast amounts of market data in real-time. Traditional financial data providers are being challenged by AI-native platforms that offer more sophisticated analytics and faster processing capabilities. The demand for low-latency data processing in financial markets has intensified as high-frequency trading strategies become more prevalent, requiring engineers who can build systems that process Level 3 market data with microsecond precision. Machine learning applications in finance are evolving beyond simple predictive models to complex market microstructure analysis and real-time feature engineering. The integration of alternative data sources with traditional market data is creating new opportunities for alpha generation, driving demand for engineers who can build scalable data pipelines that handle diverse data types. Financial institutions are increasingly investing in internal AI capabilities rather than relying solely on third-party solutions, creating opportunities for specialized fintech startups. The regulatory environment for AI in finance is becoming more structured, with institutions requiring more transparent and auditable AI systems. This trend is driving demand for engineers who can build not just performant systems, but also systems that meet compliance requirements for institutional investors. The shift toward real-time decision making in investment management is creating new technical challenges around data consistency, system reliability, and risk management.

Salary Evaluation

The offered salary range of CAD 70,000-120,000 appears below market rate for a founding engineer role in the AI/fintech space, particularly given the seniority and breadth of responsibilities. Comparable roles in Toronto or Vancouver typically command CAD 130,000-180,000 for mid-level positions, with founding engineer positions often including significant equity compensation to offset lower base salaries. The equity component will be crucial in determining total compensation competitiveness.

Role Significance

As the second founding engineer, this role suggests a very small technical team, likely 2-4 engineers total, with direct collaboration with founders and senior leadership. The team structure is likely flat with minimal hierarchy, requiring strong self-direction and cross-functional collaboration skills. The engineer will likely work closely with product leadership and potentially directly with early customers to understand requirements and iterate on solutions.
This founding engineer position represents a senior individual contributor role with significant architectural decision-making authority and direct impact on product direction. The role combines technical leadership with hands-on development, typical of early-stage startup environments where individual contributors wear multiple hats. The position offers substantial influence over technology stack decisions and system design choices that will shape the company's long-term technical foundation.

Key Projects

Building the core data ingestion and processing infrastructure that will handle real-time market data from multiple exchanges and alternative data sources Developing the machine learning platform that will serve as the foundation for the company's AI-powered analytics capabilities Creating the API layer and user interfaces that institutional clients will use to access the platform's capabilities Establishing the system architecture and engineering practices that will enable the team to scale as the company grows

Success Factors

Deep understanding of financial markets and trading systems will be essential for building relevant and effective solutions that meet institutional investor needs. The ability to translate complex financial requirements into technical solutions will differentiate successful candidates from those with purely technical backgrounds. Strong system design skills for building scalable, reliable infrastructure that can handle the demanding requirements of institutional financial applications. Experience with high-frequency data processing, distributed systems, and real-time analytics will be crucial for success in this role. Entrepreneurial mindset and comfort with ambiguity, as founding roles require making decisions with incomplete information and adapting quickly to changing requirements. The ability to balance perfectionism with pragmatic delivery timelines will be essential in a startup environment. Excellent communication skills for interfacing with both technical team members and non-technical stakeholders, including potential customers and investors. The ability to explain complex technical concepts to business stakeholders will be important for product development and sales support.

Market Demand

Extremely high demand exists for engineers with combined ML and backend expertise in financial technology, particularly those capable of handling real-time market data processing. The specialized nature of financial data engineering, combined with the growing adoption of AI in institutional investing, creates a supply-demand imbalance favoring candidates. The founding engineer aspect adds additional appeal for professionals seeking high-impact roles with significant autonomy.

Important Skills

Critical Skills

Python and PyTorch proficiency is absolutely essential as these are the primary tools for machine learning model development and data processing in the financial analytics space. The ability to build production-ready ML pipelines using these technologies directly impacts the company's core value proposition. Golang expertise for backend development is crucial for building the high-performance, low-latency systems required for real-time financial data processing. Financial applications demand exceptional performance and reliability, making strong backend development skills non-negotiable. Understanding of financial markets and trading systems, while not explicitly listed, is critical for success in this role. The ability to work with Level 3 market data, order books, and trading concepts requires domain expertise that cannot be easily acquired on the job. System design and architecture skills are vital for making technology decisions that will scale with the company's growth. Poor architectural decisions early in a startup's lifecycle can create significant technical debt and scalability challenges.

Beneficial Skills

Experience with distributed systems and cloud platforms would be valuable for building scalable data processing infrastructure that can handle institutional-scale data volumes Knowledge of financial regulations and compliance requirements would help in building systems that meet institutional investor standards and regulatory requirements Frontend development skills beyond the basic UI requirements could enable greater contribution to user experience design and customer-facing product development Experience with DevOps and infrastructure automation would be beneficial for establishing reliable deployment and monitoring processes as the platform scales

Unique Aspects

The combination of AI/ML expertise with financial domain knowledge creates a highly specialized and valuable skill set that is increasingly rare in the market
Founding engineer role provides exceptional learning opportunities and direct impact on product development and company direction
Focus on institutional clients offers exposure to sophisticated financial applications and high-stakes technical requirements
The role combines multiple technical disciplines including backend development, machine learning, data engineering, and some frontend work, providing broad technical growth opportunities

Career Growth

Career progression timeline will largely depend on company growth trajectory, with potential advancement to technical leadership roles within 18-24 months if the company successfully scales. Transition to external opportunities could occur within 2-3 years with significant skill development and industry network expansion.

Potential Next Roles

Technical Lead or Engineering Manager roles as the company scales and builds out larger engineering teams Chief Technology Officer position within AllMind AI or similar fintech startups, leveraging the foundational technical and business experience Senior Machine Learning Engineer or Quantitative Developer roles at hedge funds or investment banks, utilizing the deep financial domain expertise Technical Product Manager positions in fintech companies, combining technical skills with product strategy experience gained in the founding role

Company Overview

AllMind AI Inc.

AllMind AI Inc. operates in the competitive fintech space focused on providing AI-powered data analytics platforms for institutional investors. As an early-stage startup, the company is positioning itself to compete with established financial data providers like Bloomberg, Refinitiv, and emerging AI-native competitors. The company's focus on hedge funds and institutional investment firms suggests a high-value, specialized market approach rather than broader retail applications.

As a startup, AllMind AI is in the early market entry phase, competing against well-established players with significant resources and market presence. The company's success will depend on its ability to differentiate through superior AI capabilities and user experience. The institutional focus provides a clear target market but also means longer sales cycles and higher customer acquisition costs.
Located in Waterloo, Ontario, the company benefits from proximity to the University of Waterloo's strong computer science and engineering programs, as well as the broader Toronto-Waterloo technology corridor. The Canadian location may present challenges in accessing U.S. financial markets and customers, but also provides access to strong technical talent and potentially favorable regulatory environment.
The startup environment suggests a fast-paced, high-intensity work culture with significant individual responsibility and minimal bureaucracy. The founding engineer role indicates a collaborative, hands-on culture where team members are expected to contribute across multiple areas. The requirement for weekend work as needed suggests periods of high intensity typical of early-stage startups.
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