Algorithmic Trading Strategist
Alignerr · Seattle, WA
- Hybrid
- Contract
- $75,000 / year
- Seattle, WA
Job highlights
- Evaluate AI-driven trading logic for accuracy and risks.
- Utilize expertise in markets and execution systems.
- Identify edge cases and performance bottlenecks.
- Work remotely with flexible, task-based commitment.
- Shape AI reasoning for algorithmic strategies.
About the role
About The Role
What if your hard-won knowledge of markets, execution logic, and trading systems could directly shape how AI reasons about algorithmic strategies? We're looking for experienced Algorithmic Trading Strategists to evaluate trading logic built into AI systems — stress-testing strategies, identifying edge cases, and ensuring the reasoning behind automated decisions holds up in the real world.
This is a fully remote, flexible contract role. No AI background needed — just deep, practical experience in systematic or algorithmic trading and the ability to think critically about how strategies perform across conditions.
Type: Hourly Contract
Location: Remote
Commitment: Flexible, task-based
What You'll Do
- Review and critically evaluate algorithmic trading logic and execution paths
- Analyze order types, routing behavior, and market microstructure assumptions
- Identify risks, inefficiencies, edge cases, and performance bottlenecks in trading strategies
- Summarize strategy performance across scenarios, market regimes, and time horizons
- Validate alignment with realistic trading constraints, liquidity conditions, and execution realities
- Deliver structured, well-reasoned evaluations as part of recurring strategy-review cycles
- Work independently and asynchronously — fully on your own schedule
Who You Are (Must-Have)
- Hands-on experience in systematic or algorithmic trading — prop, institutional, or independent
- Strong command of order types, execution timing, slippage, and market microstructure
- Ability to trace and critique multi-step trading logic and communicate findings clearly in writing
- Sharp analytical mindset with a methodical approach to evaluating strategy quality
Nice To Have
- Familiarity with backtesting frameworks or execution simulation tools
- Experience building, reviewing, or stress-testing quantitative strategies
- Background in quantitative finance, financial engineering, or a related technical field
Why Join Us
- Work on cutting-edge AI projects alongside leading research labs
- Fully remote and flexible — work when and where it suits you
- Freelance autonomy with the structure of meaningful, task-based work
- Put your specialized market expertise to work in a way that has real, lasting impact on AI development
- Potential for ongoing work and contract extension as new projects launch
Key skills/competency
- Algorithmic Trading
- Trading Strategy Evaluation
- Market Microstructure
- Order Types
- Execution Logic
- Risk Identification
- Performance Analysis
- Quantitative Finance
- Critical Thinking
- AI Training
Skills & topics
- Algorithmic Trading
- Trading Strategist
- Quantitative Finance
- Market Microstructure
- Execution Logic
- Risk Management
- Strategy Evaluation
- Remote
- Contract
- AI Training
How to get hired
- Tailor your resume: Highlight direct experience in systematic or algorithmic trading, emphasizing your command of order types, execution, and market microstructure.
- Showcase analytical skills: Quantify your ability to trace trading logic, identify risks, and communicate findings clearly in writing, using examples from your experience.
- Demonstrate critical thinking: Prepare to discuss how you've evaluated strategy quality, identified edge cases, and assessed performance across different market conditions.
- Understand the role: Emphasize how your practical market expertise can directly contribute to training AI systems, even without prior AI background.
Technical preparation
Master order types and execution nuances.,Deepen understanding of market microstructure.,Practice critiquing multi-step trading logic.,Refine strategy performance analysis skills.
Behavioral questions
Describe a complex trading strategy you analyzed.,How do you identify edge cases in logic?,How do you communicate strategy risks clearly?,How do you adapt to new trading environments?
Frequently asked questions
- What specific experience is required for the Algorithmic Trading Strategist role at Alignerr?
- Alignerr requires hands-on experience in systematic or algorithmic trading, whether in prop trading, institutional settings, or as an independent trader. A strong command of order types, execution timing, slippage, and market microstructure is essential. The ability to critically analyze multi-step trading logic and clearly articulate findings in writing is also a must-have.
- Do I need prior AI or machine learning experience to be an Algorithmic Trading Strategist at Alignerr?
- No, prior AI background is not required for this Algorithmic Trading Strategist role. Alignerr specifically seeks individuals with deep, practical experience in systematic or algorithmic trading who can critically evaluate AI-generated trading logic. Your market and execution expertise is the primary focus.
- What is the work arrangement for the Algorithmic Trading Strategist position?
- This Algorithmic Trading Strategist role at Alignerr is fully remote and offered as a flexible, hourly contract. The commitment is task-based, allowing you to work independently and asynchronously on your own schedule.
- How does Alignerr utilize Algorithmic Trading Strategists in their AI projects?
- Algorithmic Trading Strategists at Alignerr are crucial for shaping how AI reasons about trading strategies. You will evaluate and stress-test the trading logic built into AI systems, identify edge cases, and ensure automated decisions are sound by analyzing performance across various scenarios and market conditions.
- What are the key responsibilities of an Algorithmic Trading Strategist at Alignerr?
- Key responsibilities include critically reviewing algorithmic trading logic and execution paths, analyzing order types and routing behavior, identifying risks and inefficiencies, summarizing strategy performance, and validating strategies against realistic trading constraints and market realities.