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About This Role
Location: Hybrid (onsite 2\-3 days/week in Whittier, CA)
Status: Contract to Hire
Reports To: VP, Unstructured Data Analytics \& AI Innovation
Role Overview
PSB Insights is seeking a contract to hire Manager, AI Innovation and Solutions to lead the charge in defining "what's next" for our advanced research and data analytics capabilities. This role sits at the intersection of AI product engineering, advanced unstructured data analytics, and strategic research. You will not only apply cutting\-edge methodologies to solve high\-impact client engagements but will actively architect, build, and deploy custom agentic AI systems, automated workflows, and data products and solutions.
The ideal candidate is a builder, a hands\-on developer, and a strategic thinker. You possess deep technical expertise in Python, R, and SQL, and have a proven track record of designing and implementing agentic workflows (e.g., multi\-agent systems, tool\-use LLMs, autonomous research loops) within cloud environments \- specifically Google Cloud Platform (GCP) and Azure. You know how to design human\-in\-the\-loop workflows.
We are looking for an applied AI developer who can lead complex initiatives, converting highly ambiguous business questions into scalable technical architectures and turn emerging AI capabilities into practical, reliable products and workflows. This role is not focused on training foundation models; but, rather, it is focused on selecting, integrating, evaluating, and orchestrating existing models to solve real business and research problems.
Key Responsibilities:
AI Innovation \& Solution Development
- Agentic AI Engineering: Architect, prototype, and build custom agentic workflows, tool\-calling systems, and multi\-step AI applications to automate, scale, and deeply enrich qualitative and quantitative research processes.
- Product \& Solution Building: Lead the end\-to\-end development of proprietary AI\-driven tools, custom APIs, and analytics products that differentiate PSB’s capabilities in the market. Manage structured outputs, function calling, JSON schemas, and API orchestration.
- Cloud Architecture (GCP and Azure): Deploy, manage, and optimize AI models, databases, and data pipelines utilizing Google Cloud Platform and Azure services (e.g., Vertex AI, Microsoft Foundry, etc.).
- Research \& Development: Investigate and explore emerging AI frameworks, LLMs, vector databases, and NLP techniques, rapidly translating academic or industry breakthroughs into production\-ready business solutions. Integrate LLMs through APIs, including OpenAI, Anthropic, and Google models, into client\-facing applications and internal workflows.
Advanced Analytics \& Data Engineering
- Multi\-Engine Programming: Write, optimize, and maintain production\-grade code in Python and R for advanced text/image/video processing, statistical modeling, machine learning, and data visualization. Develop AI solutions using modern machine learning frameworks, including the Hugging Face ecosystem.
- Database \& Query Design: Architect structured databases and write highly complex SQL queries to extract, clean, and merge diverse datasets (first\-party surveys, social data, digital scraping, and client databases).
Project Leadership \& Client Strategy
- Methodology Standardization: Establish and champion best practices for code reproducibility, analytical rigor, data security, quality assurance, and documentation across the team. Utilize modern software development practices, including Git/GitHub for version control and Agile methodologies for project delivery.
- Collaboration: Partner closely with data scientists, project managers, and senior leadership, providing technical mentorship where needed.
Candidate Profile:
Technical Requirements
- Applied AI Engineering: Advanced Python skills, with hands\-on experience building AI\-enabled applications, agentic workflows, tool\-calling systems, structured outputs, APIs, and human\-in\-the\-loop processes using models from OpenAI, Anthropic, Google, or Microsoft.
- Agent and Integration Technologies: Experience with LLM orchestration frameworks and protocols such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, and Model Context Protocol (MCP), including connecting agents to databases, APIs, enterprise systems, and external tools.
- Modern Data Stack: Strong SQL skills and experience with cloud data platforms such as Databricks, BigQuery, or Microsoft Fabric, including structured, unstructured, and vector data architectures, data pipelines, and workflow orchestration.
- Cloud and Product Deployment: Experience building and deploying secure, production\-ready AI applications on GCP and/or Azure using tools such as Vertex AI, Cloud Run, Microsoft Foundry, Azure Functions, Docker, Git/GitHub, and CI/CD workflows.
- AI Quality, Security, and Retrieval: Experience with RAG, embeddings, vector and hybrid search, reranking, LLM evaluation, tracing, monitoring, prompt and model versioning, access controls, data privacy, prompt\-injection mitigation, and production testing.
- R and Survey Data Engineering: Experience using R for survey\-data ETL, supporting reusable internal libraries or packages, and maintaining production analytical workflows.
Experience \& Education
- Education: Bachelor’s degree in quantitative, computational, or scientific discipline (e.g., Computer Science, Data Science, Statistics, Economics, Engineering). An advanced degree with research experience is a bonus.
- Experience: 3\+ years of professional experience in data science, AI engineering, analytics, or market research, with a minimum of 2 years specifically focused on building or developing AI solutions, digital products, or NLP pipelines.
- Proven Portfolio: A portfolio of built products, open\-source contributions, custom dashboards, or published research showcasing your ability to take a solution from concept to functional product.
Professional Competencies
- Structured Problem Solving: Exceptional ability to translate highly ambiguous, complex client business problems into step\-by\-step logic and robust technical workflows.
- Strategic Communication: An innate ability to explain "how the black box works" to non\-technical stakeholders in a highly persuasive, visual way.
- Agility \& Curiosity: A passionate self\-starter who actively experiments with new AI models and thrives in a fast\-paced, rapidly evolving hybrid environment.
Why PSB Insights
Within PSB, we’re building what’s next. Our PSB Labs team sits at the forefront of how we evolve research \- leveraging AI\-engineered approaches, agentic workflows, and cloud\-native solutions to unlock insights from massive, chaotic datasets. When traditional methods aren’t enough, this team pioneers new ways forward.
At PSB, you will have the unique opportunity to act as both a technical founder and an executive consultant \- building proprietary systems while immediately seeing their impact on some of the world's most influential brands.
Benefits
- Healthcare, dental, vision, FSA, life insurance, short\- and long\-term disability, and pet insurance
- 401(k) with generous employer match
- Flexible Time Off (FTO)
- End of Year Holiday Office Closure
- Paid parental leave after 12 months
- Modern office locations with premium hybrid amenities
*PSB Insights is an equal opportunity employer and values diversity of thought, background, and experience. The anticipated annual base salary range for this position is* *$70,000–$119,000 USD**, depending on qualifications, geography and experience. In addition to base salary, PSB offers a comprehensive benefits package and opportunities for incentive compensation, where applicable.*
Salary Context
This $70K-$119K range is in the lower quartile 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 PSB Insights, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($94K) sits 57% below the category median. Disclosed range: $70K to $119K.
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.
PSB Insights AI Hiring
PSB Insights has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Whittier, CA, US. Compensation range: $119K - $119K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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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