SOURCE is expanding its proprietary trading, quantitative-research, artificial-intelligence, data, automation, and financial-technology operations across the foreign-exchange, equities, futures, and digital-asset markets.
We are seeking traders, quantitative analysts, algorithm developers, AI engineers, programmers, data scientists, financial engineers, and technically sophisticated operators to contribute to the research, testing, validation, deployment, and continuous improvement of systematic trading environments.
The Source operating environment integrates proprietary trading methodologies, quantitative analysis, machine learning, multi-agent AI orchestration, automated software engineering, market-data infrastructure, strategy simulation, execution analytics, risk management, private servers, VPS systems, and high-performance computing.
Selected participants enter a structured operating environment combining advanced systems, practical assignments, direct evaluation, research collaboration, and progressive access to increasingly valuable responsibilities and opportunities.
Selected candidates may work with a coordinated set of systems rather than an isolated strategy or retail trading tool. The environment is designed to connect:
Market Data → Research → Hypothesis → Strategy → Simulation → Risk → Execution → Telemetry → Review → Continuous Improvement
SOURCE evaluates candidates on demonstrated capability, learning velocity, analytical discipline, reliability, and the ability to create measurable value. Traditional credentials are respected, but they are not the only route into the organization. Strong self-taught candidates, independent traders, technical founders, and professionals entering from adjacent disciplines are encouraged to apply.
Proprietary Technology and Research Environment
Applicants who advance may receive exposure to selected systems and infrastructure including:
- Proprietary trading strategies, algorithms, indicators, and expert advisors
- Quantitative-research pipelines and automated strategy-analysis systems
- Artificial-intelligence agents for research decomposition, hypothesis generation, data interpretation, anomaly detection, risk review, documentation, and operational automation
- Multi-agent research and software-development workflows
- Claude Code and OpenAI Codex-class agentic engineering environments
- LangGraph-based orchestration for long-running, stateful, and human-supervised agent workflows
- Model Context Protocol integrations connecting AI systems with data, software, tools, APIs, and operational workflows
- Retrieval-augmented research systems and persistent institutional knowledge
- Automated code generation, testing, debugging, refactoring, and deployment
- Backtesting, forward testing, simulation, walk-forward analysis, Monte Carlo analysis, and optimization
- Market-data pipelines, broker and exchange APIs, WebSocket and FIX-related research
- Trading dashboards, performance analytics, execution telemetry, and risk controls
- Paper-trading and controlled live-execution environments
- Private cloud, VPS, server, GPU, and high-performance computing resources
- Collaborative research workspaces, structured assignments, and direct review
- Commercial AI and automation systems developed across the broader Source ecosystem
The objective is not to use AI as a source of generic market commentary. SOURCE is building systems in which humans direct, verify, challenge, and improve intelligent agents across research, engineering, trading, risk, and operations.
The Source Research Model
SOURCE operates as a collaborative research environment combining traders, programmers, analysts, data specialists, technologists, and AI systems around a shared objective: improve the quality, speed, validation, and execution of systematic decision-making.
Participants may contribute to a distributed research process in which:
- Different assumptions are tested in parallel
- Data and strategy results are independently reviewed
- Weak methodologies are rejected
- Strong ideas are refined and retested
- Human judgment is compared with model output
- AI systems learn from structured human analysis and feedback
- Research becomes reusable organizational intelligence
- Strategies are evaluated across different data sets, time periods, execution assumptions, and market regimes
- Performance is judged through risk-adjusted metrics rather than isolated winning trades
SOURCE values:
- Evidence over assertion
- Repeatability over one-time outcomes
- Tracked performance over recollection
- Risk-adjusted returns over screenshots
- Forward and live validation over attractive backtests alone
- Transparent analysis of losses, limitations, and failure conditions
- Documented reasoning and versioned strategy development
- Measurable improvement over unsupported confidence
Exceptional contributors may help shape research methodologies, data sets, testing frameworks, operating standards, algorithms, risk systems, dashboards, and future generations of Source AI agents.
Responsibilities
Responsibilities are assigned according to candidate capability and pathway and may include:
- Conducting quantitative, technical, fundamental, sentiment, or market-structure research
- Testing trading strategies, models, assumptions, and execution logic
- Designing, reviewing, or improving algorithms and automated workflows
- Performing backtesting, forward testing, simulation, walk-forward analysis, and Monte Carlo analysis
- Auditing data quality, strategy integrity, execution assumptions, and performance claims
- Developing or evaluating AI agents used in research, software engineering, risk, and operations
- Working with Python, MATLAB, APIs, databases, market-data systems, broker platforms, and analytical software
- Building or evaluating dashboards, telemetry, research tools, and operator interfaces
- Documenting methodology, evidence, conclusions, and failure cases
- Supporting traders, programmers, analysts, research scientists, and technical operators
- Completing clearly scoped assignments and maintaining accurate research records
- Collaborating across distributed testing and review groups
- Improving Source algorithms, research processes, AI systems, infrastructure, and operating standards
- Protecting proprietary information and working responsibly within controlled environments
Advanced traders and operators are expected to understand the methodologies, data, assumptions, risk logic, infrastructure, and performance of the systems they operate. The objective is to develop professionals capable of piloting advanced AI-assisted trading and research systems—not merely following signals or operating software without understanding it.
Source University
Source University is the talent-development, research, and operator-activation environment through which selected candidates develop system familiarity, complete applied assignments, demonstrate capability, and qualify for deeper participation across the Source ecosystem.
It was developed to address a persistent gap in the market: serious candidates can find unlimited information, but rarely gain access to an integrated combination of expert direction, proprietary systems, practical assignments, quantitative validation, AI assistance, professional infrastructure, collaborative research, and defined advancement opportunities.
Source University may combine:
- Private one-on-one mentorship
- AI tutors and specialist agents
- Adaptive instruction
- Project-based learning
- Trading and capital-system development
- Algorithm and software engineering
- Research assignments and proof-of-work
- Data, automation, dashboard, and infrastructure projects
- Direct review and performance feedback
- Progressive access to systems, teams, and opportunities
Source University is not limited to preparing traders. Strong participants may develop into:
- Research traders
- Quantitative analysts
- Algorithm developers
- AI systems operators
- Data engineers
- Automation specialists
- Risk analysts
- Trading-infrastructure specialists
- Full-stack developers
- Technical researchers or educators
- Commercial AI builders
- Sales engineers
- Project or research-team leaders
- Client-facing technical professionals
This broader model allows SOURCE to identify where each participant can create the greatest value rather than forcing every candidate into one predetermined role.
Selection, Progression, and Access
SOURCE is building a measurable meritocracy.
Candidates are evaluated on:
- Quality of analysis
- Reliability and follow-through
- Learning velocity
- Analytical honesty
- Initiative
- Documentation
- Collaboration
- Risk discipline
- Strategy performance
- Return-to-drawdown quality
- Ability to follow systems
- Ability to challenge assumptions intelligently
- Capacity to direct AI without becoming dependent on it
- Technical, commercial, operational, or relationship value
- Trust and readiness for greater responsibility
The progression model is straightforward:
- Learn the environment and complete an initial capability review
- Receive assignments appropriate to current experience
- Demonstrate research, analytical, technical, trading, or commercial value
- Work with increasingly advanced systems and responsibilities
- Validate performance and reliability
- Advance into trading, research, engineering, leadership, commercial, or capital pathways
Greater contribution earns greater access.
That access may include:
- More advanced assignments
- Proprietary research and systems
- Better infrastructure and computing resources
- Direct mentorship
- Leadership of testing or research groups
- Specialist recognition
- Invitations into advanced teams
- Paid projects
- Trading participation
- Capital review
- Long-term roles within the Source ecosystem
Not every applicant enters at the same level, and not every candidate advances through the same route. SOURCE is selecting for both present capability and the ability to improve rapidly under direct feedback.
Compensation, Trading, and Capital Pathways
The published $50,000–$100,000 annual compensation range applies to candidates selected for direct salaried or comparable paid roles.
Other candidate relationships may include:
- Paid contract or project assignments
- Paid quantitative, technical, or research work
- Trading-profit participation
- Performance-based trading
- Structured research internships
- Training-centered research contribution
- Source University and private development
- Prop-firm evaluation and funded-account development
- Sponsored or partially sponsored infrastructure
- Specialized AI, engineering, data, automation, or commercial roles
Qualified traders may be supported in preparing for prop-firm evaluations, developing independently maintained live accounts, and progressing toward full-time trading.
Certain advanced trading arrangements may have compensation potential of up to approximately $10,000 per month, depending on the system, account size, performance, risk controls, workload, and agreement.
SOURCE may also support qualified candidates with the systems, professional relationships, infrastructure, certification or licensing direction, and operating knowledge required to pursue advanced trading and fund-development pathways.
Proven candidates may be considered for funding ranging from $100,000 to $1,000,000 to establish or expand a trading operation or fund-related initiative using Source systems. Maximum allocations require a validated record progressing through simulation, forward testing, controlled live performance, risk review, operational reliability, and progressive capital increases.
Candidates with trading capital, funded-account experience, investor or prop-firm relationships, technical infrastructure, commercial capability, or the financial ability to support their development are encouraged to identify those resources during the qualification process.
What SOURCE Is and Is Not
SOURCE is a private technology, research, trading, and operating ecosystem.
We are not a signal room, mass-market robot vendor, influencer brand, MLM, or conventional trading academy built around passive content and unsupported income claims.
Our model centers on:
- Proprietary systems
- Applied research
- Professional infrastructure
- Quantitative validation
- Artificial intelligence
- Human judgment
- Collaborative testing
- Documented performance
- Progressive responsibility
- Real operating capability
Not signals, but systems.
Not passive instruction, but applied capability.
Not isolated claims, but measured performance.
Applicants who advance will see how SOURCE integrates AI, algorithms, data, research workflows, human review, automation, server infrastructure, performance analytics, and commercial operating systems into one environment.
SourceUniversity.com is the primary development and candidate gateway.
SourceLeadEngine.com demonstrates how the broader organization applies AI, automation, workflow architecture, data, infrastructure, and agentic systems to functioning commercial environments. It is evidence that SOURCE does not merely discuss artificial intelligence, we build and deploy it.
Candidate Profile
We are interested in candidates with experience or demonstrated aptitude in one or more of the following:
- Systematic, quantitative, discretionary, or high-frequency trading
- Forex, equities, futures, options, or digital assets
- Algorithm development
- Quantitative research
- Python, MATLAB, or other technical languages
- Machine learning and artificial intelligence
- Claude Code, Codex, agentic software engineering, or AI-assisted development
- LangGraph, LangChain, multi-agent systems, or workflow orchestration
- Data engineering, analytics, databases, and visualization
- Financial engineering
- Risk management
- Broker, exchange, market-data, or execution infrastructure
- VPS, cloud, server, network, or high-performance computing
- Full-stack development and interface engineering
- Automation and API integration
- Technical writing and research documentation
- Entrepreneurship, operations, sales engineering, business development, or capital relationships
Applicants are not expected to possess expertise in every area.
We are seeking people who are:
- Intellectually rigorous
- Highly organized
- Technology-oriented
- Independent and proactive
- Detail-focused
- Entrepreneurial
- Strong communicators
- Capable of working independently and collaboratively
- Comfortable receiving direct feedback
- Able to document their reasoning
- Serious about risk
- Motivated by systems, evidence, and continuous improvement
- Capable of learning quickly and operating in a rapidly evolving technical environment
Performance can outweigh pedigree. Candidates with unusual ability, strong independent work, nontraditional backgrounds, or adjacent professional experience should apply.
Application
Submit your résumé through Indeed with a brief statement describing your interest and the area in which you believe you can contribute most effectively.
Relevant supporting material may include:
- Trading or funded-account history
- Research or performance reports
- Algorithms, code, repositories, or technical projects
- Backtests, models, dashboards, or data work
- AI-agent or automation projects
- Technical writing or documentation
- Commercial systems or operating results
- Investor, prop-firm, brokerage, exchange, or industry relationships
A formal portfolio is not required to apply.
Candidates are encouraged to review the Source University environment before submitting their application:
https://SourceUniversity.com
Submit your résumé through Indeed with a brief statement describing your interest and the area in which you believe you can contribute most effectively. Candidates selected for the next stage will be invited to complete a Source Onboard profile.
SOURCE is interested in both established professionals and high-potential candidates capable of demonstrating exceptional learning speed, discipline, technical judgment, research quality, trading performance, or commercial value.
Job Types: Full-time, Part-time, Internship, Contract, Commission
Pay: $50,000.00 - $100,000.00 per year
Expected hours: No less than 40 per week
Benefits:
- 401(k)
- Employee assistance program
- Employee discount
- Flexible schedule
- Flexible spending account
- Health insurance
- Paid time off
- Professional development assistance
- Referral program
Education:
Experience:
- Trading: 1 year (Preferred)
Work Location: In person
