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About This Role
About Orum
Orum’s AI\-powered suite frees salespeople to do what they do best: connect, listen, and sell. Our products gives sales teams everything they need to connect faster, sell smarter, and grow revenue. From intelligent dialing and real\-time conversation insights to AI\-driven coaching and virtual sales floors, Orum is powering thousands of sales teams to have more meaningful conversations and turn every call into measurable impact. Companies who use Orum connect 5x faster and book millions in new pipeline every month.
As a company, we are a remote\-first team of builders and dreamers creating a future where work feels more meaningful and connected. If you’re excited to change how the world sells, join us. For more information, visit https://www.orum.com/
The Role
Orum's Product Team plays a pivotal role in driving this vision forward. We're tackling some of the toughest challenges sales teams face, and there’s still so much to build. Notably, Orum sits on database of over 1B outbound phone calls, and our vision involves capitalizing on that data to produce better outcomes, whether that’s using predictive analytics to improve call outcomes, leveraging data into actionable agentic coaching, or building a data agent that uses all available sources to give reps highly targeted, highly accurate lead lists. In this role, you’ll work with a team of AI/ML and Data focused engineers to drive the AI and Data\-powered vision of the product.
What you’ll do:
We are looking for a Founding Staff Product Manager, Data \& ML to own the vision for Orum's data product strategy. This person will serve as the product leader for the data pillar, identifying opportunities to turn data and machine learning into meaningful customer value. Some of your responsibilities include:
- Own and define the roadmap for Orum's data products
- Rapidly prototype models on messy behavioral datasets and translate data signals into user\-facing product features
- Identify and define new product opportunities driven by data
- Partner closely with Engineering to bring ideas into production
- Inform other products’ roadmaps by generating killer insights from Orum’s data
What we're looking for:
- Proven experience shipping user\-facing ML products
- Experience taking ML products from prototype to production in a fast\-paced product environment
- Strong background building ML\-driven or data\-oriented products
- Deep technical foundation in software engineering or data science
- Strong analytical and experimental mindset, including experience designing, running, and interpreting experiments
- Experience operating in fast\-paced startup environments
Bonus if you have:
- Experience building agentic tools
- 0\-1 founding experience
Orum takes a market\-based approach to compensation, offering equity and a base salary as part of the compensation package. The estimated base salary range for this role is TBD per year. Exact compensation may differ from this range and depends on the candidate’s experience, domain expertise, and interview performance.
Company Values
- Excellence \- Deliver high\-quality work across every function
- No Jerks \- Build a team that's respectful, inclusive, and collaborative
- Accountability \- Own outcomes and follow through
- Integrity \- Always act honestly with customers, partners, and each other
- Stewardship \- Manage our resources responsibility for sustainable growth
- Great Ideas Win \- Innovation thrives when the best ideas lead, no matter where they come from
Benefits and Perks (FTE)
- Flexibility to work anywhere in the US
- Flexible Vacation Policy
- 30\+ paid holidays annually, including observed holidays, the first Friday of every month off and a two\-week year\-end holiday break.
- Meaningful stock options in Orum
- 90% coverage for employees and dependents for healthcare, dental, and vision insurance plans
- Comprehensive Life \& Disability package
- Parental leave for the primary or the secondary
- $1k equipment reimbursement for work\-related items that's yours to keep
- Company retreats and meetups for all employees to connect in person
- Company ERG \- Women Of Orum (WOO)
*Orum Is An Equal Opportunity Employer*
We're committed to continually adding to our diverse team that represents various backgrounds, perspectives, and skills. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. If you need assistance or accommodation due to a disability, you may contact us at [email protected]. In short, we want you to join in on the ride if you're talented for one of our roles, with no other qualifiers.
Compensation Range: $205K \- $275K
Salary Context
This $205K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ORUM, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($240K) sits 30% above the category median. Disclosed range: $205K to $275K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
ORUM AI Hiring
ORUM has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $275K - $275K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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