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About This Role
Position: Product Manager \- AI\-Native Business Systems
Location: South San Francisco
Time Type: Full Time
### The Opportunity:
Ohalo is moving beyond R\&D into a phase of massive operational scale. As our breakthrough "Boosted Breeding™" technology rewrites what is biologically possible in the field, our entire enterprise operation from Commercial Ops and Agentic workflows to backoffice G\&A must scale with the exact same ambition. We are seeking a Product Manager to bridge our core Infrastructure \& AI Native Business Systems efforts with company\-wide operational execution.
This is not a siloed RevOps or traditional Business Intelligence role. Your mandate is company\-wide. Partnering across our infrastructure and platform teams, you will expand Ohalo's core operational capabilities with AI including harnessing agent frameworks, MCP tooling, maintaining skills, etc and ensure best practices are accessible across every department. You will turn foundational AI capabilities into pragmatic, high\-impact operational systems, ensuring the whole company runs on a trusted, unified digital layer rather than a patchwork of vibe coded applications. This is a role for a hands\-on operator who understands systems and can prototype solutions directly to prove value before hardening into new platform capabilities.
### Responsibilities:
- Scale Company\-Wide AI Deployment: Partner across engineering teams to extend core AI capabilities across the entire business. Drive company\-wide adoption of agent infrastructure.
- Manage MCP \& Tooling Access: Define and govern Model Context Protocol (MCP) integrations and agent skills. Ensure native agents (spanning leadership team to commercial ops and backoffice G\&A) have secure, reliable access to internal tools and systems of record.
- Improve Evals \& Quality Standards: Stand up evaluation frameworks for operations and establish consistent feedback loops. Monitor AI agent performance, latency, accuracy, and cost\-to\-serve, continuously refining skills and prompts to ensure high\-fidelity outputs.
- Architect Enterprise Reporting \& Ground Truth: Serve as the company's absolute metric authority. Define, standardize, and govern KPIs across Commercial, Ops, People, and G\&A so leadership makes decisions based on a single source of truth calculated at the system level.
- Automate Operational Friction: Identify repetitive manual reporting and execution workflows company\-wide. Deploy AI\-native automations and measure real ROI, operational efficiency gains, and time saved.
- Own Technical Delivery \& Prototyping: Maintain an extreme ownership mindset. Personally prototype v1 workflows and reporting to validate proof\-of\-concepts before scaling across platform engineering.
- Prevent Tool \& System Sprawl: Enforce strong platform governance so new agent tools and dashboards plug seamlessly into Ohalo's core architecture rather than spawning fragmented UI layers or data silos.
### Requirements / Candidate Profile:
- Technical \& Analytical Foundation: Bachelor's degree in Computer Science, Data Analytics, or a related quantitative field preferred. Highly fluent in SQL/Python and comfortable interfacing with modern API/data architecture.
- Proven Track Record: 5\+ years of product management experience building internal platforms, business systems, or operational tooling with company\-wide scope.
- AI Infrastructure \& Agent Literacy: Deep understanding of modern AI technology concepts including LLM evaluation frameworks (evals), model context protocols (MCPs), and agent orchestration/harnessing.
- Hands\-On Builder Spirit: Actively uses AI tooling, agentic coding harnesses, or vibe coding platforms in daily prototyping and workflow design. You don't just write specs; you build scrappy prototypes.
- Systems Unification Experience: Demonstrated history of taking manual, Excel\-based, or multi\-department fragmented processes and turning them into scalable automated systems.
- Influence Without Authority: Exceptional communicator who earns cross\-functional trust with Commercial, Operations, G\&A, and Engineering through effective collaboration and shipping useful tools.
### Nice to Have / Bonus Points:
- Direct experience partnering with AI Ops, MLOps, or Core Infrastructure teams to productize AI tools internally.
- Experience in a "Business Systems," "BizOps," or "ProductOps" function at a high\-growth startup.
- Experience with graph databases or complex data warehouse integrations.
- Background in a data\-rich, operationally intensive vertical (robotics, life sciences, or advanced manufacturing).
*The anticipated pay range for this role is $140,000 \- $190,000 per year for our South San Francisco location. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, budget availability, internal equity, geographic location and external market pay for comparable jobs.*
About Ohalo:
Ohalo® is sowing the seeds of tomorrow. Founded in 2019, Ohalo develops novel breeding systems and improved plant varieties that help farmers grow more food with fewer natural resources, increasing the yield, resiliency, and genetic diversity of crops to sustainably feed our population. Ohalo's breakthrough technology, Boosted Breeding™, will usher in a new era of improved productivity to radically transform global agriculture. For more information, visit www.ohalo.com.
*Notes: If you previously applied for a job at Ohalo Genetics, we encourage you to restate your interest in the position by submitting your application.*
*No visa sponsorship is available for this position at this time.*
*This organization participates in E\-Verify. Posters linked* *here.*
*No recruiters, please.*
Salary Context
This $140K-$190K range is below the median for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Ohalo, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($165K) sits 24% below the category median. Disclosed range: $140K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ohalo AI Hiring
Ohalo has 1 open AI role right now. They're hiring across AI Product Manager. Based in Burlingame, CA, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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