Interested in this AI/ML Engineer role at ProductBoard?
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About This Role
San Francisco, United States
The opportunity
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As an AI Success Engineer, you’ll help customers adopt AI\-first product management by guiding them through Spark — showing them how AI can transform the way they discover, prioritize, build, and communicate. You’ll work directly with product and engineering teams to turn Spark from something they’ve heard about into something they use every day.
You’ll manage your own book of business, lead activation sprints, and build the consultative instincts that make the difference between a customer who uses Spark and one who can’t imagine working without it.Why this matters for your career
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AI is rewriting how product teams work — and this role puts you at the front of that shift, helping real customers move from curiosity about AI to depending on it every day. You won’t just talk about AI\-first product management; you’ll coach product and engineering leaders through it hands\-on, using their own data and workflows.
The skills you’ll sharpen here — AI fluency and prompt design, consultative solutioning, and translating technical capability into business outcomes — are exactly the skills that will define customer\-facing technical careers for the next decade.On a Typical Day, You Will...
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- Guide customers toward AI\-first product management — help them see Productboard Spark not as an add\-on but as a core part of how they work. Demonstrate AI\-augmented workflows using the customer’s own data and show how Spark accelerates discovery, prioritization, and roadmap communication.
- Manage a book of accounts — build trusted relationships with product leaders, understand their goals, and ensure each account progresses through activation to sustained adoption within allocated service hours.
- Deliver activation sprints within the standard playbook — run use case sprints guided by established best practices and templates, escalating when novel or high\-risk situations arise.
- Build early AI fluency with customers — configure Spark context so it reflects the customer’s domain, coach users on effective prompting, and follow up with prompt templates and hands\-on support when Spark adoption stalls after the first Sprint.
- Support the transition from activation to adoption — structure monthly touchpoints around meaningful signals (MAU Index, Spark usage depth), not vanity metrics, and identify when guidance versus additional hours versus escalation is the right call.
- Partner with Account Executives and Renewals Managers — support AE\-led business reviews with product health data, Spark usage metrics, and adoption summaries. Identify expansion signals and surface them to the AE for commercial qualification.
- Contribute to internal improvement — share playbook improvements, document solutioning patterns, and support onboarding of new ASEs. Feed observations about customer friction points and process gaps back to the team.
- Feed insight into our product direction — share customer patterns and Spark adoption challenges that help Product and Enablement teams improve the platform and our delivery model.
Our Tech Stack
Productboard, Slack, Salesforce, Google Suite, Zendesk, Claude, Zoom, Gong, Jira, GitHub, Loom.What success looks like
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- Customers trust you as a credible technical advisor — product leaders seek your guidance on best practices and Spark adoption strategies.
- Your accounts consistently hit Time to Value targets.
- Monthly touchpoint coverage is at 100% across your book, with success plans current and aligned with customer goals.
- Spark usage grows across your accounts as customers move from basic prompts to more integrated workflows, with you coaching the progression.
- You deliver use cases within allocated service hours and escalate early when burn rate or scope issues arise.
- Renewals in your book are supported with thorough product health data and value documentation, reducing last\-minute scrambles for the AE and RM.
- Your playbook feedback and solutioning patterns are picked up by peers, improving how the team delivers.
- Customers begin to view Spark as part of how they work, not just a tool they log into — a sign of real adoption taking hold.
About you
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- Experience in customer success, solutions architecture, implementation consulting, or strategic advisory roles within B2B SaaS.
- Direct experience managing a portfolio of accounts across the post\-sales lifecycle — activation, adoption, and renewal support.
- Comfortable leading conversations with Director and VP\-level product leaders — skilled in objection handling and solutioning across varied customer contexts and types.
- Solid product management knowledge — familiar with discovery methods, prioritization frameworks (RICE, Value vs. Effort), OKRs, and roadmap communication. You understand the workflows Spark is designed to improve.
- 1–2 years of hands\-on AI experience in a professional context — not just personal productivity, but actively using AI to deliver customer outcomes, accelerate workflows, or build AI\-augmented solutions. You understand prompt engineering fundamentals, can design basic multi\-step AI workflows, and have coached others on effective AI usage.
- Familiar with enterprise product management tools and integration ecosystems — Jira, Slack, Salesforce, and similar platforms.
- Strong customer\-facing communication and relationship\-building skills — you adapt your approach based on the audience and build trust through reliability, follow\-through, and genuine curiosity about the customer’s business.
- Self\-motivated and organized — you manage your book of business independently day\-to\-day, ask for help on the right things, and contribute positively to team culture.
Bonus:* Hands\-on experience with Productboard or Spark.
- Background as a product manager yourself, not only advising them.
- Experience in a high\-growth or startup B2B SaaS environment.
*The expected base pay range for this position in the San Francisco area is $92,500\-$122,600* *In addition to the base pay, this role is eligible for competitive equity awards and benefits.Productboard's pay ranges are determined by role, level, and location. Within the range, the successful candidate's starting base pay will be determined based on factors including job\-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future.*You can look forward to the following benefits \+ More
Competitive compensation, stock options, company 401k
Budget for online courses, books, and conferences
️ Flexible PTO and paid sick days
Commuter Benefits
Volunteer Day for you to help causes close to your heart
Carrot Fertility Benefits
Free snacks, drinks, and yummy catered lunches
️ ️ Company contribution to gym and wellness memberships* Flexible working hours and home office. Hybrid Schedule Mon/Tues/Thurs In Office
Mental Wellness Program to support your well\-being and self\-care### About Productboard
Productboard is the leading intelligent product management platform, used by 6,000\+ companies including Salesforce, SAP, Autodesk, and Kroger. Headquartered in San Francisco, with engineering hubs in Prague and Brno. Backed by Index Ventures, Kleiner Perkins, Sequoia Capital, Bessemer, Tiger Global, and Dragoneer.
We've spent the last decade building the foundation for how product teams work. We're now in our 0\-to\-1 moment again, building Productboard Spark — the AI\-first, agentic experience that turns raw feedback and signals into clear problems, strong specs, and faster execution. Spark is what powers everything you'd be working on.### Benefits
- Stock options
- MacBook \+ 34" monitor
- Budget for online courses, books, and conferences
- 5 weeks of vacation \+ 9 sick days
- Volunteer days
- Carrot fertility benefits
- MultiSport card
- Flexible working hours and home office
- Parental benefits
- Language lessons
- Mental wellness program
- Free snacks, drinks, and catered lunches in our office
### Relocation
If you're authorized to work in the EU and want to move to Prague or Brno, we offer a one\-time relocation bonus of $6,000–$13,000 USD, depending on whether you're moving alone or with a partner or family.
We're unable to provide visa sponsorship for this role.### Are you building with us?
If the work above is what you want to spend the next chapter of your career on, apply below!Equal Opportunity Employer Statement
We are an equal opportunity employer and champion equity. We aim to help people from all backgrounds, cultures, and groups realize their full potential at Productboard. We do not tolerate any discrimination or harassment based on gender identity, race, color, religion, age, sexual orientation, non\-disqualifying physical or mental disability, national origin, veteran status, or any other bias covered by appropriate law. All aspects of employment, including hiring, training, promotion, and terminations, are based on merit, competence, performance, and business needs. We are committed to an inclusive hiring process and provide all candidates with equal opportunity to demonstrate their abilities. Togetherness is one of our core values, and our Diversity Council helps to ensure that we uphold the values of authenticity, humanity, and diversity to create an environment where every person matters. We are committed to leading by example to drive societal change.*By applying you agree to Productboard's* *Global Candidate Privacy Policy*
Salary Context
This $92K-$122K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At ProductBoard, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($107K) sits 40% below the category median. Disclosed range: $92K to $122K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
ProductBoard AI Hiring
ProductBoard has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $122K - $122K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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