Job Title: Machine Learning Engineer, Quantitative Systems

Location: San Francisco, CA (Onsite)

Team: Engineering

Partner: A pioneering AI company building autonomous, quantitative systems for growth marketing, backed by Quiet Capital.

About Our Hiring Partner

Our hiring partner is engineering the future of digital growth by building an AI Operator that autonomously manages advertising spend with the precision and rigor of institutional trading. Their platform replaces manual agency processes, delivering a transformative 20-50% efficiency gain by applying algorithmic decision-making to a traditionally qualitative field.

Already entrusted with managing critical budgets for leading global e-commerce brands, the company is on a clear trajectory to scale its autonomous volume into the billions. As an early-stage team based in San Francisco, they are assembling foundational talent to build the core intelligence and execution systems. This is a rare opportunity to shape the technology stack of a company applying quantitative finance principles to a massive, global market.

Why This is a Foundational Opportunity

1. Build Core Intelligence: You will architect the very models and strategies that autonomously govern millions in advertising spend, directly impacting client P&L.
2. Bridge Theory & Production: Move beyond research to own the full lifecycle—from strategy design and back testing to live execution and monitoring in volatile markets.
3. Early-Stage Impact: Join as a key early engineer with significant influence over technical direction, product evolution, and company culture.
4. SF-Based Frontier Work: Contribute to one of the most technically ambitious San Francisco startup jobs, operating at the intersection of AI, quantitative finance, and growth marketing.

About the Role

We seek a Machine Learning Engineer to build the models, optimization systems, and algorithms that power our autonomous decision engine. You will not just build models; you will design the core financial strategies that dictate how capital is deployed and build the execution layer that carries them out in real-time.

Your mission is to identify market inefficiencies, translate them into automated trading strategies, and build robust integrations that execute those decisions. We need builders who can bridge theoretical research with production pragmatism—shipping iterative solutions to capture immediate value while evolving them into generalized, risk-aware platforms.

What You'll Do

1. Design Trading Strategies: Develop the core algorithms governing autonomous decision-making. This includes building predictive customer LTV models, risk-aware budget allocators, and detection systems for creative fatigue.
2. Build the Execution Layer: Create and maintain high-reliability API connectors to major ad platforms (Meta, Google, TikTok) to execute bid, budget, and targeting updates in real-time.
3. Deploy and Monitor at Scale: Own the full model lifecycle from research to production. Build observability infrastructure to detect concept drift, monitor performance, and ensure system stability amidst changing market conditions.
4. Backtest and Verify: Develop sophisticated simulation and backtesting infrastructure to validate strategy robustness across market conditions and client verticals before live deployment.

Who You Are

1. A Senior Practitioner: You have 4+ years of experience applying machine learning, optimization, and statistical methods to solve real-world, high-stakes problems. You view deep learning as one tool among many.
2. A Systems Builder: You architect platforms, not write one-off scripts. You have extensive experience deploying models into high-throughput production environments and designing for API reliability, rate limits, and fault tolerance.
3. Math-Fluent: You possess a strong foundational grasp of probability, statistics, and linear algebra. You can implement concepts from research papers but know when a simpler, more robust solution is the right engineering choice.
4. Pragmatic and Impact-Driven: You prioritize velocity and tangible business impact. You ship quickly to test hypotheses and iteratively refine systems based on live market feedback.

Bonus Points

1. Mathematical Depth: Formal education or significant experience in areas like Convex Optimization, stochastic processes, or control theory.
2. AI-Native Workflow: Proficiency with modern AI-powered development tools (e.g., Cursor, Claude Code) to accelerate engineering workflows.
3. Domain Experience: Background in quantitative finance, algorithmic trading, or programmatic advertising (Real-Time Bidding systems).

Compensation & Benefits

This role offers a highly competitive package commensurate with the seniority and impact of the position.

1. Salary: $160,000 – $240,000
2. Equity: A significant early-stage equity package.
3. Relocation: Support for candidates moving to the Bay Area.
4. Daily Meals: Provided lunch and optional dinner.
5. Comprehensive Benefits: Full health, dental, and vision coverage, alongside an unlimited PTO policy.

Apply Now

If you are a pragmatic ML engineer obsessed with building scalable, reliable systems that make autonomous, high-value decisions, we encourage you to apply. This is a defining opportunity among San Francisco startup jobs for those who want to build the core intelligence of a category-defining company.