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About This Role
Alt is unlocking the value of alternative assets, starting with the $5 B trading\-card market. We let collectors buy, sell, vault, and finance their cards in one place and we are backed by leaders at Stripe, Coinbase, Seven Seven Six, and pro athletes like Tom Brady and Giannis Antetokounmpo. Our next frontier is real\-time pricing at scale—the Alt Value that powers every trade, loan, and product on the platform.
We're hiring a Senior Machine Learning Engineer who thrives on owning models end\-to\-end, from research through production. In this role, you'll own productization of Alt's pricing and underwriting models — the systems that turn raw card and market data into the Alt Value and cash advance terms that every buyer, seller, and lender on the platform depends on. You'll be the person who matures models to production\-grade services, keeps them accurate and fast at scale, and pushes the boundary of what we can automate.
### Why This Role Exists
Alt is at an inflection point — our marketplace is scaling fast, and our pricing intelligence infrastructure has become a genuine competitive moat. We've proven that model\-driven pricing works; now we need to push coverage, accuracy, and speed further while bringing down the overhead to run it. This is a high\-ownership opportunity to take our pricing and underwriting models from "working" to "excellent" — and to define the ML infrastructure standards that will scale with the company for years to come.
### What You'll Own
- Optimize our pricing models to significantly reduce infrastructure costs while maintaining and improving their accuracy, especially for high\-value assets.
- Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates.
- Lead the full ML lifecycle from model training and feature generation to production deployment and monitoring.
- Collaborate closely with our Expert Pricers to become a domain expert in the trading card market and inform model improvements.
- Design and execute experiments and backtesting to discover and validate new features that improve the models' predictive power and coverage.
- Own the models' AWS infrastructure, writing code for our pricing APIs to ensure the models can serve at scale and with low latency.
### Metrics You'll Own:
### Northstar Metric: Model\-Based Pricing Coverage (% of cards confidently priced by models vs. manually)
### KPIs:
- ### Pricing Accuracy (% Error)
- ### Pricing Freshness (End\-to\-End Model Orchestration Time)
- ### Underwriting Performance (Advance disbursement rate vs. Target default rate)
### What Great Looks Like (6 Months)
- ### Shipped leaner, more accurate pricing models. You've cut infrastructure cost meaningfully while improving accuracy, especially on high\-value assets.
- ### Moved underwriting from good to great. You've iterated on the underwriting model to increase cash advance disbursements without breaching risk thresholds.
- ### Earned trust with Expert Pricers. You're a go\-to partner for the pricing team — you understand the domain deeply enough that your model changes reflect real market judgment, not just data.
- ### Hardened the production path. The pricing APIs are faster, more observable, and easier to reason about, with monitoring in place to catch model drift or degradation before it hits customers.
### Who you are
Must\-haves:
- 7\+ years of engineering experience, with 5\+ years building and shipping production ML/AI models.
- Deep proficiency in production\-grade Python and SQL, including building custom feature\-engineering pipelines (not just off\-the\-shelf scikit\-learn). Think time\-decay weighting, leakage\-safe k\-fold cross\-validation, and cascading fallback/imputation logic.
- Experience training and validating gradient\-boosted or ensemble estimators against strict accuracy/error tolerances, with segment\-specific tuning (e.g., by category or asset type).
- Experience leveraging LLMs, foundation models, and AI dev tools for both internal tooling and user\-facing product use cases in production.
- Experience with MLflow or a comparable tool for experiment tracking and model registry/versioning.
- Comfortable owning production model\-serving infrastructure on AWS — capacity planning, auto\-scaling, and diagnosing memory/timeout failures at scale.
- Experience with CI/CD pipelines, orchestrating production workflows, and IaC for provisioning and modifying cloud infrastructure.
- Pragmatic and focused on delivering value incrementally rather than pursuing perfection.
Nice\-to\-haves:
- Experience with real\-time or low\-latency models serving at scale.
- Previous startup experience — you understand and thrive on the pace, adaptability, and ownership required in a fast\-moving environment.
- Interested in or knowledgeable of trading cards, collectibles, or alternative asset markets.
### What You'll Get From Us
- A seat at the table to help shape the future of Alt and the alternative asset space
- Autonomy and ownership on projects that matter
- $100/month work\-from\-home stipend
- $200/month wellness stipend
- WeWork office stipend
- 401(k) retirement benefits
- Flexible vacation policy
- Generous paid parental leave
- Competitive healthcare benefits, including HSA, for you and your dependent(s)
Base salary range: $235,000\-250,000 plus equity. Offers may vary based on experience, location, and other factors.
Salary Context
This $235K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Germain Hotels, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($242K) sits 11% above the category median. Disclosed range: $235K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Germain Hotels AI Hiring
Germain Hotels has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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