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About This Role
About Us:
Lucia Protocol is at the forefront of AI\-driven data infrastructure, building the future of decentralized ad attribution. We are redefining how brands, publishers, and communities leverage Web3 to accurately track, attribute, and reward digital actions. Our team is innovating where AI, blockchain, and decentralized ecosystems intersect to power transparent and efficient ad measurement solutions.
Role Summary
Lucia Protocol is looking a Senior Machine Learning Engineer to join as a founding member of our AI team. You’ll design, train, and deploy the ML systems that power Lucia’s attribution engine, predictive analytics, and behavioral intelligence platform.
This role demands more than technical excellence — it requires leadership, initiative, and accountability. You must be someone who:
- Takes full ownership of outcomes.
- Detects design flaws early, raises red flags immediately, and drives resolution with urgency.
- Is relentless in ensuring the product performs as required — technically and functionally.
- Doesn’t wait to be told what to do; instead, pulls work toward you and proactively moves the roadmap forward.
- Naturally takes initiative to own both roadmap and execution, ensuring progress without external push.
You’ll be the kind of engineer who combines scientific rigor with startup scrappiness — turning insight into shipped, measurable impact.
Technical Requirements \& Responsibilities
1\. Hardware \& CUDA Proficiency
- CUDA Core Competency: Deep understanding of GPU memory hierarchy (Global vs. Shared memory) and kernel optimization. Must be able to debug CUDA out of memory errors beyond just reducing batch size.
- Hardware\-Aware Scaling: Experience selecting and optimizing for different compute tiers—from local workstations (e.g., RTX 5090 setups) to enterprise clusters (e.g., NVIDIA H200 or AMD Instinct MI300X).
- Library Experience: Proficient with CuPy, RAPIDS (cuDF/cuML) for accelerating tabular workflows, and NVRTC for runtime compilation.
2\. Model Versioning \& Lineage (The "Truth" Stack)
- DVC (Data Version Control): Managing large datasets and model weights via Git\-like workflows.
- MLflow: Standardizing the model lifecycle, including experiment tracking and the "Model Registry."
- Weights \& Biases (W\&B): Real\-time performance visualization and hyperparameter sweep orchestration.
3\. Advanced Ensembling (GBT on KNN)
- Stacking Architecture: Ability to build multi\-stage pipelines where K\-Nearest Neighbors (KNN) is used as a base learner to capture local manifold structures, with the outputs (distances/neighbors) fed into a Gradient Boosted Tree (XGBoost/LightGBM) to handle non\-linear global patterns.
- Libraries: Mastery of scikit\-learn's StackingClassifier or StackingRegressor, and manual feature augmentation using KNeighborsTransformer.
Key Responsibilities
- Model Development: Design, train, and tune high\-accuracy GBT models (XGBoost, LightGBM, or CatBoost) for complex tabular datasets.
- Agentic Orchestration: Implement and maintain OpenClaw "skills" and gateway configurations to automate repetitive ML\-Ops tasks and backend monitoring.
- Full\-Stack Debugging: Move comfortably between Python (ML) and the backend (Node.js/Go/Python) to debug API contracts, data pipelines, and system\-level issues.
- Tooling \& Automation: Utilize the latest 2026 AI developer tools (e.g., Claude Code, Cursor, MCP servers) to accelerate the development cycle and maintain high code quality.
- Performance Optimization: Profile and optimize model inference latency and memory usage for real\-time production environments.
What You’ll Do
- Architect and deploy end\-to\-end ML systems for predictive analytics, attribution, and behavioral profiling.
- Develop and optimize models driving core dashboard features such as:
- Conversion probability scoring
- Lifetime value prediction
- Risk and churn detection
- Fraud and bot identification
- Implement robust data pipelines for large\-scale behavioral and financial datasets.
- Build internal systems to track model confidence, attribution accuracy, and system reliability.
- Collaborate cross\-functionally to ensure ML outputs integrate seamlessly into product experiences.
- Take proactive ownership over roadmap priorities, execution timelines, and technical delivery.
- Escalate issues immediately when product behavior diverges from intended outcomes.
What We’re Looking For
- 5\+ years of experience in machine learning engineering or applied science, with end\-to\-end product shipping experience.
- Deep knowledge of modern ML frameworks (PyTorch, TensorFlow, scikit\-learn, SAM II \& III, JAX, etc.).
- Strong understanding of data pipelines, model deployment, and distributed systems (Airflow, Spark, Ray, etc.).
- Proven ability to own complex systems from conception to production, not just experimentation.
- A bias for action. Senior leaders here don’t wait to be pushed; they pull work toward themselves.
- Demonstrated initiative in owning the roadmap and execution.
- Obsession with product correctness, detecting design flaws and escalating them immediately.
- Highly proficient in ML fundamentals. Our interviews and tests will emphasize this heavily.
Bonus Points
- Experience with ensemble learning, causal inference, or LLM fine\-tuning.
- Background in user attribution, risk scoring, or behavioral modeling.
- Familiarity with Web3 analytics, cross\-chain data, or on\-chain user tracking.
- Prior founding or early\-stage startup experience.
Why Lucia
- Join a world\-class founding team blending AI, analytics,
- Build the core ML systems driving Lucia’s Customer Acquisition and Customer Retention Dashboard
- Build a unique large language model specifically trained on intent and customer conversation.
- Competitive salary, equity, and remote\-first culture centered on autonomy, speed, and accountability.
BEFORE YOU APPLY:
Please note that applicants who do not meet the minimum requirements will not be considered for this position. We encourage you to review the listed qualifications carefully to ensure your experience and skills align with the demands of the
BEFORE APPLYING:
We are currently in an exciting fundraising phase and expect to secure funding within the next month or two. During this period, compensation will be deferred. We understand financial circumstances and will ensure that the time commitment remains reasonable. Our expectation is to have team members who can provide valuable contributions while supporting themselves until the fundraising is complete. Are you open to joining our dynamic team and being part of this thrilling journey as we grow and secure our future together? If so, by applying to this position, you understand and are willing to proceed with this arrangement if selected.
*Note: Lucia Protocol is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, or disability status.*
Job Type: Full\-time
Pay: $150,000\.00 \- $200,000\.00 per year
Benefits:
- 401(k) matching
Application Question(s):
- Please read the "BEFORE APPLYING" section on the job posting
Experience:
- AI: 3 years (Preferred)
- Software development: 3 years (Preferred)
- Machine learning: 3 years (Preferred)
- Blockchain: 3 years (Preferred)
- Web3: 3 years (Preferred)
Work Location: Remote
Salary Context
This $150K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Decentral Holding Corporation, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 5% above the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Decentral Holding Corporation AI Hiring
Decentral Holding Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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