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About This Role
About JazzX AI:
*Vision:* *Enterprises operating on institutional intelligence—governed, self\-improving, and scalable beyond individual expertise.*
JazzX AI is defining the future of enterprise work—by building AI\-native digital workers that actually get the job done.
We believe enterprises don't scale expertise—they lose it. Knowledge stays trapped in individuals, judgment gets applied inconsistently, and the best talent spends time on work that should run itself. We're changing that.
JazzX AI transforms messy enterprise reality into institutional intelligence: governed digital workers that capture expert judgment, make every decision explainable, and continuously improve through real\-world execution. The result is faster decisions, higher\-quality outcomes, and reliable execution at scale—in domains where getting it wrong isn't an option.
We're starting with lending and due\-diligence workflows—complex, regulated, high\-stakes. From here, we're building the backbone for enterprise intelligence across industries.
This is early\-stage, hard, and consequential work. If you want to be part of bringing AI systems to market that actually run in production, handle real complexity, and deliver real outcomes—not demos, not chatbots—JazzX AI is the place.
Headquartered in Los Altos, CA. Backed by SAIGroup.
About SAIGroup :
SAIGroup a private investment firm that has committed $1B to build and scale next\-generation, AI\-powered enterprise software companies. SAIGroup's portfolio serves 2,000\+ global enterprise customers, generates nearly $800M in annual revenue, and employs 4,000\+ people worldwide — providing JazzX AI with long\-term capital, deep operating expertise, and access to real\-world enterprise scale from day one.
Learn more about JazzX AI:
Website: https://jazzx.ai
LinkedIn: https://www.linkedin.com/company/jazzx\-ai
Learn more about SAIGroup:
Website: https://saigroup.ai
About the Role
We are seeking an experienced AI Engineer with deep expertise in Reinforcement Learning (RL) to join our team as a Senior Staff Architect. In this role, you will be responsible for shaping the vision, architecture, and technical execution of RL\-driven AI reasoning models and systems that power next\-generation enterprise AGI platform.
You will lead the design, development, and optimization of cutting\-edge RL solutions, from experimentation and simulation through production deployment. This includes building scalable training architectures, architecting multi\-agent and hierarchical RL frameworks, and ensuring that the RL systems are resilient, efficient, explainable and safe.
As a senior technical leader, you will partner with cross\-functional teams—including product, core platform engineering, and research—to define architectural best practices, establish governance standards, and enable seamless integration of RL into our broader AGI platform. You will also drive innovation by exploring novel RL techniques, mentoring engineers and researchers, and ensuring the RL infrastructure can scale to support high\-throughput training and real\-world scenarios and enterprise use cases end to end.
Ultimately, your work will be critical in bridging research and production, ensuring that the latest RL advancements translate into reliable, impactful, and enterprise\-ready AI solutions.
Key Responsibilities
- Architecture \& Design: Define and drive the end\-to\-end architecture for reinforcement learning–based systems, including training pipelines, simulation environments, reward shaping, and model serving.
- Research \& Development: Apply cutting\-edge RL techniques (policy optimization, model\-based RL, hierarchical RL, multi\-agent RL, etc) to solve complex enterprise problems.
- Scalability \& Infrastructure: Design distributed training systems, leverage cloud\-native infrastructure, and optimize for performance, reproducibility, and cost\-efficiency.
- Leadership \& Mentorship: Provide technical leadership to AI engineers and researchers; mentor junior team members; review designs and code with a focus on scalability, robustness, and clarity.
- Collaboration: Partner with product, data, and platform teams to align RL solutions with strategic business goals and integrate them into production systems.
- Evaluation \& Monitoring: Define frameworks for benchmarking, continuous evaluation, and feedback\-driven improvements in deployed RL models.
- Compliance \& Safety: Ensure RL systems align with ethical AI practices, safety constraints, and regulatory standards for enterprises.
Required Qualifications
- 10\+ years of experience in AI/ML engineering, including at least 5 years specializing in reinforcement learning research and production systems.
- Demonstrated success in designing and deploying large\-scale RL architectures in enterprise environments.
- Deep expertise in reinforcement learning algorithms, including on\-policy (PPO, A3C) and off\-policy (SAC, DDPG) methods, along with hands\-on work in simulation frameworks (e.g., OpenAI Gym, Isaac Gym, PettingZoo, MuJoCo).
- Practical experience with multi\-agent reinforcement learning (MARL), including coordination strategies for complex environments.
- Strong proficiency in Reinforcement Learning with Verifiable Rewards (RLVR) and GRPO\-like policy optimization approaches, applying reinforcement learning principles both rigorously and pragmatically.
- Experience with test\-time compute optimization techniques, including inference\-time search, chain\-of\-thought reasoning, and adaptive computation strategies for improving model performance during deployment.
- Proven ability in large language model (LLM) training and fine\-tuning, across both supervised and reinforcement learning–driven techniques.
- Advanced software engineering skills in Python, C\+\+, or Java, with deep expertise in ML frameworks such as TensorFlow, PyTorch, JAX, or Ray RLlib.
- Hands\-on experience with distributed training infrastructure (Kubernetes, GPU/TPU clusters, and cloud ML platforms).
- Excellent communication, collaboration, and leadership skills, with experience working across multidisciplinary teams.
Preferred Qualifications
- PhD in Computer Science, Machine Learning, Robotics, or related field.
- Experience leading enterprise AI adoption and guiding organizational strategy for RL powered systems.
- Contributions to open\-source RL frameworks or publications in top\-tier conferences (NeurIPS, AISTATS, ICML, ICLR, AAAI).
- Background in safety, alignment, or explainability of RL agents.
Why Join JazzX AI:
What draws people to JazzX AI is the opportunity to build something truly foundational.
We are at the beginning of a new era — one where enterprise systems will no longer be static tools, but dynamic collaborators that learn, reason, and evolve alongside the people who use them. At JazzX AI, you'll help build a platform that redefines how work happens in the AGI era.
JazzX AI brings together vision, deep technology, and purpose. This is not about experimenting with AI features; it's about turning the promise of AGI into practical, auditable, and human\-aligned systems that enterprises can trust and rely on at scale. The work here directly impacts productivity, efficiency, and decision\-making in complex, real\-world environments.
If you're motivated by building systems that matter — systems that combine intelligence with responsibility — JazzX AI offers the chance to do that work at a deeper level, with real ownership and lasting impact.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At JazzX AI, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
JazzX AI AI Hiring
JazzX AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Altos, CA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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