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Last seen on its board about 1 month ago, on October 01, 2026.

19

Quantitative Researcher - SF / Seattle / US

Amplify Renewables · SF / Seattle / US

Full-time Hybrid Mid via HN: Who is hiring?

Description

Amplify Renewables | Quantitative Researcher | SF / Seattle / US | Hybrid (Remote Possible) | Full-Time | $200k - $375k (all cash) | https://www.amplifyrenewables.com

We're a systematic energy trading company trading virtuals and PTPs across the US. We are hiring a mid-level quantitative researcher; the ideal candidate is process oriented and has a team-first approach. You would work closely together with the founders to build out our expansion in the US with focus on PJM. Our team is ex-D.E. Shaw, Optiver, and Stanford. We're bootstrapped, profitable, and growing. Compensation is all-cash and paid from company profits, not venture funding.

What we're looking for:

- 3+ years of experience trading virtuals/PTPs/FTRs on US ISOs (experience with PJM strongly preferred)

- Fluent Python skills (e.g. numpy, matplotlib, pandas, or similar)

- Fluency with statistics (e.g., sampling, biases, t-stats)

- Crisp written communication — we're deep-work focused and async-heavy

Nice to have: experience with LLM-assisted development, experience building quantitative trading algorithms

Hybrid, based in San Francisco or Seattle. Fully remote US-based exceptions can be made if the candidate is a particularly strong/good fit. Candidates are expected to work in person 3 days a week.

Apply: email [email protected] with resume + an (optional) short note on a research project you’ve owned end-to-end; we’re interested in how you generated the idea, tested it, communicated to others, and implemented it. Please mention HN.

Search terms: quantitative trading, energy, power markets, ISO, forecasting, electricity, PJM

Why 19?

Each component's contribution, and the phrases that triggered it.

Seniority fit +8.0
Engagement type +4.0

3 days a week

Compensation fit +3.0
Location fit +2.5

us

Recency +1.57

posted 23 days ago

Red flags 0.0
Rails stack fit 0.0