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About This Role
PRODUCT ENGINEER, APPLIED AI
The Firm
Paradigm is a San Francisco\-based investment firm focused on frontier technologies across the globe, with over $11 billion in assets under management. We make investments in companies and protocols at all stages, ranging from early\-stage venture financing rounds to growth equity to liquid assets.
Paradigm was co\-founded in 2018 and is co\-led by Matt Huang and Alana Palmedo. We’ve been hard at work investigating the world’s most beautiful technical problems since day one. Our research\-driven approach helps us build relationships with founders and entrepreneurs, but it also reflects our broader goal of accelerating the progress of frontier technology.
The Role
Over the last eight years Paradigm has built some of the most used open\-source software in the crypto industry, including Reth and Foundry. Earlier this year we published EVMBench with OpenAI, launched Optimization Arena and hosted the Auto Research hackathon. More recently, we open\-sourced Centaur, a self\-hosted runtime for multiplayer AI agents, which we’ve been using to transform how we work at Paradigm and Tempo.
All of these were done part\-time by people on Paradigm’s Investing \& Research team. This has made us realize that we are people\-bound, not idea or impact bound. We need to grow our team to achieve our ambitions.
We want to do three things:
- Experiment aggressively. We will push the capabilities of models, harnesses and infrastructure to the limit in service of Paradigm’s broader goals in investing, research, and building.
- Build in public. We will get our hands dirty building useful open\-source products.
- Invest. We will leverage our infrastructure \- an area we think is still ripe for disruption with AI \- to be better investors. By understanding infrastructure gaps experientially, we may incubate companies that will accrue value by addressing these gaps.
As we deploy more long\-running agentic capabilities, we may get into fundamental research, forecasting, model cost optimizations etc., but that is not our focus yet.
We are looking for fearless engineers for this project who understand infrastructure, security, AI models, harnesses, and Slackbots. You will work directly with Georgios, Arjun, Matt Slipper, and the broader Paradigm team, alongside Tempo, and other companies that are themselves operating at the frontier.
Your work will directly impact how Paradigm operates every day. If this sounds like you, reach out.
Responsibilities
- Build for the people moving the firm: Embed with I\&R, Events, and BizOps to find the work and decisions worth automating and ship agents to take it on. The features you build become how the firm sources, researches, and operates, which means your product sense directly shapes how effective Paradigm is.
- Agent surfaces: Build the user\-facing experience, primarily the Slack interface, so collaborating with agents feels fast, legible, and trustworthy.
- Extensibility and developer experience: Own the systems teams use to extend Centaur without forking it including tools, workflows, skills, and overlays, and make building on top of it feel effortless.
- Harness integration: Expand support across the different agent CLIs Centaur can run, keeping one clean execution model underneath.
- Open\-source stewardship: As an MIT\-licensed project, you're the face of it to the community, triaging issues and PRs, writing the docs and examples, and shaping the guides that pull people in.
Qualifications and Experience
- A track record of building world\-class products
- Experience applying modern AI and software systems to solve complex, ambiguous problems end\-to\-end
- Comfortable navigating everything from foundational models to full\-stack product delivery
- Pragmatic, product\-minded builder who cares about solving real problems rather than overfitting to academic ML goals
- Ability to work autonomously and as a collaborator on the team
Attributes
- Exceptional team player
- Extreme open\-mindedness
- Clarity of thought
- Clear and concise communication (both written and verbal)
- Technical depth; analytical; rigorous
- Highly curious; fast learner
- Ability to bridge technical and investing mindsets
- Interest in frontier technologies and crypto markets
Compensation Range: $250K \- $400K
Salary Context
This $250K-$400K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Paradigm, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($325K) sits 51% above the category median. Disclosed range: $250K to $400K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Paradigm AI Hiring
Paradigm has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $400K - $400K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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