Portfolio Manager - AI Systematic Strategies

Onyx Alpha Partners; New York (NY); On-site; USD 250,000 - 400,000; Full time

Benefits offered by this job

Non-compete: 12–18 months

Job summary

Onyx Alpha Partners seeks a senior quantitative researcher to run a short-horizon systematic book using signals from generative AI models. You will own the full lifecycle from signal idea to live execution within a tightly integrated team of AI engineers.

You will apply Python for research and C++ for latency-critical code, with a focus on speed-accuracy trade-offs and real-time performance monitoring in a live trading environment.

Qualifications

• 7 to 12 years running a systematic book at a multi-manager, systematic fund or prop firm.
• Track record judged on Sharpe, drawdown and capital used, not headline PnL.
• Machine learning in a live trading process with speed-accuracy trade-off.
• Short-horizon experience where execution slippage is measured.
• Python for research; C++ for latency-critical code.
• Working knowledge of transformers, model distillation and inference optimisation.
• Familiarity with MLOps: retraining, drift monitoring and model versioning.
• A preference for the simplest model that works.

Responsibilities

• Own the full model lifecycle, from signal idea to live execution.
• Collaborate with AI engineers in a single pod to ship changes fast.
• Balance latency and alpha via distillation and inference optimisation.
• Monitor model performance, detect decay, and retrain as needed.

Skills

Python for research

C++ for latency-critical code

Transformers familiarity

MLOps: retraining, drift monitoring, v

Job description

The Mandate

Run a short-horizon book at an established multi-manager hedge fund pod, trading signals derived from generative AI models. You sit in the same pod as the AI engineers and own the full stack, from signal idea to live execution.

The Hard Questions (What You Will Solve)

• How do you shrink a large model to fit your latency budget without losing the alpha?
• How do you spot model decay early enough to retrain, and tell a real signal from a made-up one?
• How do you avoid crowded AI-driven trades, and when does a human step in?

The Structural Edge

• One integrated pod. Research, engineering and trading sit together, so changes ship fast.
• Dedicated inference hardware, not generic cloud credits.
• You own the full model lifecycle, from distillation to retraining.
• Access to proprietary and alternative data beyond standard text feeds.

Ideal Profile

• 7 to 12 years running a systematic book at a multi-manager, systematic fund or prop firm.
• Track record judged on Sharpe, drawdown and capital used, not headline PnL.
• Machine learning in a live trading process, with a clear view of the speed-versus-accuracy trade-off.
• Short-horizon experience where execution slippage is measured.
• Python for research. C++ for latency-critical code.
• Working knowledge of transformers, model distillation and inference optimisation.
• Familiarity with MLOps: retraining, drift monitoring and model versioning.
• A preference for the simplest model that works.

Compensation & Preferences

• Non-competes: client is able to hire talent with 12-18 month NC.
• Base salary: $250,000 to $400,000 depending on experience and track record.
• Performance: Formulaic PnL payout, typically 10% to 15% of net PnL, subject to the platform's drawdown and risk framework.

This is not a guarantee of compensation or salary; a final offer amount may vary based on factors including but not limited to experience, domain expertise, and geographic location.

At Onyx Alpha Partners, we are committed to connecting the most sought after talent in the financial world, to opportunities that expand the universe of unconstrained performance within their chosen discipline. If this opportunity aligns with your career aspirations, we encourage you to apply and explore the potential for growth and unparalleled success.