Interested in this AI/ML Engineer role at Rec Technologies?
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About Rec
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Think about the last time you tried to book a tennis court. Or sign your kid up for a swim lesson. Or find an open rec league that actually fits your schedule. You probably ended up on a website that looked like it was built in 2003 — or god forbid, filled out a piece of paper.
$3T is spent on recreation globally, and it's built on a completely fragmented system that's largely untouched by technology.
At Rec (recreation.ai), we're using AI to change that — reimagining how the world plays, participates in community, and gets active. Our vision is simple: more recreation for everyone. We sit at a rare intersection — a consumer product loved by everyday players and an enterprise platform trusted by the organizations that power recreation.
We're a small team with firepower. Our founders were among the first few hundred at Uber, then led teams at The Athletic and MasterClass. The rest of the team combines builders from Google, Microsoft, Strava, Hipcamp, and Airbnb.
People join Rec for two reasons: you want to build AI that gets people back in the real world, and you want to work with really smart people.
About the Role
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Seb, Rec's AI agent, only gets better if someone is relentlessly closing the loop between how it performs in the real world and how it improves. As our Product Operations Manager \- AI, you're that person. You'll run the quality engine behind Seb — evals, annotation, defect triage — and you'll be the connective tissue that keeps AI initiatives moving across product, engineering, and the business. You care as much about the annotation queue as you do about whether a launch actually shipped on time.
Some days you're deep in eval results deciding whether a regression is a defect or a preference issue. Other days you're making sure five different workstreams converge on the same launch date.
What You'll Own
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- Run Seb's evaluation and quality operations — building and maintaining eval sets, managing annotation queues, and classifying issues as defects vs. preferences.
- Own the feedback loop between real\-world agent performance and the roadmap — turning quality signals into clear, prioritized fixes.
- Drive cross\-functional AI program management — keeping roadmaps, launches, and dependencies across product, engineering, and design on track.
- Build and refine the operational infrastructure (observability, scoring, review workflows) that lets the team trust Seb's quality at scale.
- Partner with engineering on the practical realities of shipping AI features — instrumentation, monitoring, and rollout sequencing.
- Spot patterns across evals and staff feedback that point to bigger product or trust issues before they become customer\-facing problems.
- Keep AI leadership and stakeholders clearly informed on program status, quality trends, and risk.
Who You Are
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- 5\+ years in product operations, program management, or a related operational role — ideally with exposure to AI/ML products.
- Comfortable getting hands\-on with evals, annotation tools, and quality frameworks, even without a formal ML background.
- Highly organized — you can run multiple workstreams at once without losing the thread.
- A clear communicator who can translate messy technical detail into a simple status update or decision
- Genuinely curious about AI and how agents actually behave in production
- Comfortable in ambiguity — you build the process where none exists yet.
- Bias toward action and high attention to detail in equal measure.
Our Values
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- Be Relentless. Move fast. Own it. Ship real value early and often.
- Win Together. Speak up, go beyond the boundaries of your role. That's how we win.
- Take Pride. Treat this like it's yours — because it is.
Pay \& Benefits
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- Compensation: $150K\-$180K \+ meaningful equity (final offer will be based on your background, experience and skillset)
- Benefits: Flexible PTO, top\-tier health/dental/vision, and a 401(k) plan to support your future.
- Work location: San Francisco — in\-office most days at our FiDi SF office.
Compensation Range: $150K \- $180K
Salary Context
This $150K-$180K range is below 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 Rec Technologies, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $150K to $180K.
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.
Rec Technologies AI Hiring
Rec Technologies has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $180K - $200K.
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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