Senior ML Engineer

DeFi Alliance LLC; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

DeFi Alliance LLC is seeking a senior machine learning engineer to own end-to-end ML features from requirements to production. You will build and operate production Python systems for data collection, feature extraction, scoring, and AI-assisted research, while ensuring model reliability and observability.

You will work directly with stakeholders and report to the CTO, relocating to NYC if needed to join onsite teams.

Qualifications

• Senior, self-directed ML engineer who can take an ambiguous problem from first experiment through a reliable production release.
• Deep experience with Python and applied machine learning; comfortable moving between data exploration, training code, application code, APIs, and production debugging.
• Strong modeling judgment: problem and label definition, feature design, evaluation, backtesting, leakage, missing data, calibration, interpretability, and model selection.
• Enough software and data engineering depth to ship your own work: build pipelines and services, integrate external APIs, manage model artifacts and schemas, and maintain production workflows without heavy engineering support.
• Practical experience with LLM systems: structured outputs, model and prompt evaluation, observability, retries, cost and latency tradeoffs, and safe handling of untrusted inputs.
• Clear communicator with good product judgment who can work directly with non-technical stakeholders and turn model output into a useful decision or operating tool.
• Extremely high-agency, entrepreneurial, self-driven.
• NYC-based or willing to relocate (non-negotiable).

Responsibilities

• Own applied ML end-to-end: turn a loosely defined problem into dataset, experiment, model, and production system.
• Build and operate production Python systems for data collection, enrichment, feature extraction, scoring, evaluation, and AI-assisted research.
• Develop models people can trust: define labels and features, build evaluation sets and backtests, catch leakage and bad source data, compare approaches, and know when a simpler model is right.
• Move work from the model lab into production: own artifacts, feature and prompt compatibility, APIs, background jobs, observability, failure handling, and releases.
• Improve our LLM systems including structured extraction, research agents, prompt and model evaluation, and guardrails for untrusted data.
• Work directly with stakeholders to decide what is worth building, explain model behavior and tradeoffs, and iterate based on real-world usage.

Skills

Python

Applied machine learning

Production systems

LLM systems

Data pipelines

Model evaluation

Stakeholder communication

Entrepreneurial drive

Relocation willingness

Tools

APIs

Data pipelines

LLM tooling

Job description

Senior ML Eng

Location: NYC (onsite only - not remote)

Alliance is the leading accelerator for crypto & AI founders. Since 2020 we've backed 300+ startups (Rain, Pump, Synthetix, Pendle, and many more), now collectively valued at $15B+.

We're hiring a Machine Learning Engineer to join our in-house engineering team. You'll report directly to Carter (CTO) and will be responsible for owning features from the requirements definition stage to production.

What You’ll Do

• Own applied ML end-to-end: turn a loosely defined problem into a dataset, an experiment, a model, and a production system without relying on a PM or a large engineering team.
• Build and operate production Python systems for data collection, enrichment, feature extraction, scoring, evaluation, and AI-assisted research.
• Develop models people can trust: define labels and features, build evaluation sets and backtests, catch leakage and bad source data, compare approaches, and know when a simpler model is the right answer.
• Move work from the model lab into production: own artifacts, feature and prompt compatibility, APIs, background jobs, observability, failure handling, and releases.
• Improve our LLM systems including structured extraction, research agents, prompt and model evaluation, and the guardrails needed to use untrusted external data safely.
• Work directly with stakeholders to decide what is worth building, explain model behavior and tradeoffs clearly, and iterate based on how the system is actually used.

What we’re looking for

• Senior, self-directed ML engineer who can take an ambiguous problem from first experiment through a reliable production release.
• Deep experience with Python and applied machine learning; comfortable moving between data exploration, training code, application code, APIs, and production debugging.
• Strong modeling judgment: problem and label definition, feature design, evaluation, backtesting, leakage, missing data, calibration, interpretability, and model selection.
• Enough software and data engineering depth to ship your own work: build pipelines and services, integrate external APIs, manage model artifacts and schemas, and maintain production workflows without heavy engineering support.
• Practical experience with LLM systems: structured outputs, model and prompt evaluation, observability, retries, cost and latency tradeoffs, and safe handling of untrusted inputs.
• Clear communicator with good product judgment who can work directly with non-technical stakeholders and turn model output into a useful decision or operating tool.
• Extremely high-agency, entrepreneurial, self-driven.
• NYC-based or willing to relocate (non-negotiable).

Examples of strong qualifications (good to have but not required)

• Shipped ML products that people actually use, with evidence of owning the path from raw data and experimentation through deployment, monitoring, and iteration.
• Strong public work: a standout GitHub, useful open-source contributions, published research, technical writing, or unusually good independent experiments.
• Experience building prediction, ranking, classification, recommendation, or anomaly-detection systems on messy real-world data.
• Experience building LLM evaluation systems, structured extraction pipelines, research agents, or other production AI workflows.
• Founder, early ML hire, or senior individual contributor at a fast-moving startup, especially where you operated without a dedicated ML platform or large engineering team.
• Clear signals of exceptional technical or quantitative ability: strong research, competition results, Math/Physics Olympiad performance, or a top technical academic background.

Why NOT join us

• Not willing to get hands dirty: doesn't matter how important you were in past organizations; at Alliance we're all builders, not managers (even though many of us were managers in past lives).
• Prioritizing work/life balance: this role demands focus, hunger, and a career-defining level of commitment. You must be locked in.
• Low agency: if you need someone else to set your priorities or keep you on track, you will fail.
• You can't relocate to NYC. This is non-negotiable: our founders are here, and so are we.

Why join us

• Work with the most ambitious founders in crypto and AI. Learn firsthand from hundreds of startups succeeding - or failing.
• Join a small, high-trust, high-performance team with outsized impact. We're ex-Meta, WhatsApp, Coinbase, YC, and have collectively founded multiple venture-backed startups.
• We're backed by S-tier investors including Initialized Capital, Founders Fund, Multicoin, and Dragonfly, along with angels such as Balaji Srinivasan (ex-Coinbase CTO), Kevin Weil (CPO at OpenAI), Kevin Lin (Twitch co-founder), and Jeremy Allaire (Circle CEO), among many others.
• Direct ownership and visibility: your work shapes how the next generation of founders discovers Alliance.
• Career accelerator: this role sets you up, experience- and network-wise, for any high-impact path in crypto/AI – at startups, venture firms, or your own company.
• Alliance startups are reinventing industries - from media to payments - and improving the lives of everyday people. You'll have a front-row seat as they change the world.