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About This Role
### About the Role
We're seeking a Senior Machine Learning Engineer to own the systems that determine how products are surfaced, ranked, and discovered across Arena Club's marketplace, spanning search relevance, ranking, and personalization on every surface and category. This is production machine learning in the request path, where model quality translates directly into conversion, engagement, and revenue. The mandate covers the full lifecycle: taking a ranking approach from concept through deployment, monitoring, and continuous iteration, then validating each improvement in rigorous controlled experiments. Impact here is measured not by models shipped but by demonstrable lift in the metrics that move the business. This is a player\-coach role. Beyond building models directly, you will establish the methodological standards and rigor for data science across the organization and mentor new Data Scientists who join as the team grows.
### What You'll Do
Recommendations \& Ranking
- Design, train, and deploy recommendation and ranking models that improve relevance and conversion across the marketplace
- Own a recommendation and search index that beats a strong third\-party control on conversion and cuts fallback rates
- Build homepage, onboarding, and category\-level personalization ranking
- Develop retrieval and candidate\-generation systems using embeddings and semantic search
- Reuse cross\-domain features such as item scores and tier weights as ranking signals across surfaces
Production ML \& ML Operations
- Own the full ML lifecycle: data ingestion, feature engineering, training, evaluation, deployment, monitoring, and iteration
- Deploy and operate low\-latency inference in the request path, and design scalable systems for serving and pipeline execution
- Build and maintain batch and near\-real\-time data pipelines using Python and PySpark
- Deploy and operate ML workloads on AWS (EC2, S3, and related services)
- Improve reproducibility, experiment tracking, and observability across the stack
- Collaborate with backend engineers to integrate models into customer\-facing systems
Cross\-Functional Partnership \& AI\-Accelerated Development
- Partner with Product, Engineering, and the marketplace squad to translate high\-level needs into technical problem statements
- Design and interpret A/B tests to validate model and product changes
- Communicate model behavior, trade\-offs, and timelines in plain language to non\-technical stakeholders
- Leverage AI tools and agents throughout the ML lifecycle for code scaffolding, debugging, and optimization
- Operate with high ownership and autonomy in a fast\-moving, ambiguous environment
### Qualifications
- Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative field; advanced degree preferred
- 5\+ years in applied ML or ML engineering, with a track record of shipping models to production in a consumer\-facing environment
- 3\+ years building recommendation, ranking, search relevance, or personalization systems
- Expert\-level proficiency in Python and the ML ecosystem (PyTorch, TensorFlow, Scikit\-learn)
- Experience with learning\-to\-rank, embeddings, retrieval, and semantic search
- Advanced SQL for complex data extraction and processing.
- Experience applying ML to user behavior data (clickstream, transactional, event logs)
- Strong AWS experience (EC2, S3, and related services) for model hosting and data workflows
- Comfort with experimentation (A/B testing, lift measurement, business impact interpretation)
- Knowledge of MLFlow or an equivalent experiment\-tracking and model\-registry tool
### Preferred Qualifications
- Experience with AWS OpenSearch, Algolia, or other search and retrieval systems
- Experience with Marketplace recommendation system modeling
- Experience with vector databases and approximate nearest\-neighbor search
- NLP experience, including embeddings, text classification, or semantic search
### The Arena Club Standard
Life at Arena Club isn't for the faint of heart, and that's by design. We're building products and experiences the collectibles world has never seen. This is a proving ground. It demands your best every single day, because anything less means you're falling behind.
From day one, you're in the game. Trusted to deliver, expected to own outcomes, and driven to raise the bar higher than you thought possible. We don't just execute, we innovate, compete, and win together. That's how real breakthroughs happen.
If you want routine or predictability, you won't find it here. But if you're ambitious, relentless, and hungry to prove yourself on a team built to dominate — step into the arena. You'll discover growth and reward here, unlike anywhere else.
Salary Context
This $170K-$230K range is above the median 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 Arena Club, 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 ($200K) sits 9% below the category median. Disclosed range: $170K to $230K.
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.
Arena Club AI Hiring
Arena Club has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $230K - $230K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
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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