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About This Role
Please Note: This is a Utah-based hybrid position which will require some regular in-office days each week. Additionally, employment with BambooHR is contingent on passing both a background and credit check.
BambooHR is on a mission to set people free to do great work. Our software powers the HR work that small and mid-sized businesses depend on every day. Across engineering, our teams use AI coding agents as the default way to build: specifying intent, delegating execution to agents, verifying and integrating their output, and codifying best practices so each project compounds.
You'll lead the team that makes this possible for everyone else. Your team owns the Agent SDK — the framework other engineering teams build their agents on — along with the core platform underneath it: a RAG pipeline, a sandboxed environment for running agent-generated code, evaluation frameworks, and observability. The specifications, guardrails, and verification standards your team sets don't just govern your own engineers' work, they become the floor every other agentic engineer at BambooHR builds from.
AI tools and capabilities change rapidly. To stay resilient through change, you prioritize curiosity and continual learning, both for yourself and the team you manage, and you actively push new best practices into how your team specifies, delegates, and verifies work. You are accountable for your team's delivery and for the platform's fitness as the foundation other teams depend on: its correctness, its safety, and its reliability at scale.
You will:
Supervisory Responsibilities:
- Manage 1-2 scrum teams of Software Engineers building the Agent SDK, RAG pipeline, code execution sandbox, evaluations, and observability.
- Set the bar for how your engineers write agent specifications, delegate execution to AI coding agents, and verify agent-produced output, calibrated to the risk and blast radius of platform-level code
- Coordinate engineers steering multiple agents in parallel across concurrent platform workstreams, keeping engineering judgment and ownership intact
- Own the scaffolding your team and its downstream consumers delegate through: SDK conventions, agent instructions, evaluation harnesses, and playbooks, and decide which are worth investing in
Duties/Responsibilities:
- Partner with the teams building on your platform to understand their needs and shape the SDK's roadmap, APIs, and guardrails
- Demonstrate leadership and patience through regular coaching and mentoring on agentic craft, in individual and team settings
- Consult with Product Management, UX Designers and Tech Leads to explore and deliver software solutions
- Manage and maintain your team's execution roadmap, including a grooming pipeline and delivery expectations
- Show and maintain accountability in team and individual performance (yours and others) in sprint goals and quarterly Objectives and Key Results (OKRs)
- Participate in department hiring efforts
- Contribute to the ongoing improvement of our systems and processes
- Be a great culture addition and advocate for our company values
What You Need to Get the Job Done
- 10+ years of experience in software development
- 2+ years of real-world, demonstrable technical leadership
- Object-Oriented development experience
- Experience building or operating platform-level engineering teams — internal SDKs, developer platforms, or infrastructure that other teams build on
- Experience managing or working alongside engineers who use AI coding agents as the default way to build production software
- Proficiency in context engineering practices: structuring repo and task context, and setting standards for agent specifications, so agents execute reliably across teams
- Familiarity with core AI agent infrastructure concepts such as RAG, sandboxed code execution, evaluation frameworks, and observability
- Experience in developing Software as a Service
- Version control software, preferably Git
- Proficiency in documenting processes and monitoring performance metrics
- Exceptional interpersonal and communication skills
What Will Make Us REALLY Love You
- Hands-on experience with LLM tooling: RAG pipelines, vector databases, sandboxed/isolated code execution environments, or agent evaluation and observability tooling (e.g., tracing, prompt/response logging, eval harnesses)
- Experience setting verification and security standards for agent-produced code: injection, data-handling, and unsafe generated output
- Knowledge of best practices related to data storage and access at high volume
- MySQL/MSSQL - both query language and database structure/design
What You'll Love About Us
- List Benefits found on Boilerplate SOP
About Us
At BambooHR, we're building something different: we're building a people intelligence platform that transforms HR and sets people free to do great work! We're a proven market leader driving innovation while building lasting success through thoughtful, sustainable growth. Here, you'll find a place that champions growth: both professional and personal, both individual and collective.
We invest in potential, giving you the space to stretch your capabilities and turn good ideas into reality while providing the safety net of a supportive, values-driven culture. Our approach combines meaningful work with meaningful lives, offering competitive benefits, professional development, and the flexibility to thrive both in and outside the office.
What sets us apart isn't just what we do, but how we do it: with openness, integrity, and a shared commitment to doing the right thing. Join us in creating HR software that makes work better for everyone, while we make work better for you.
BambooHR is committed to the full inclusion of all qualified individuals and will ensure that persons with disabilities are provided reasonable accommodations throughout the hiring process. If you would like to request accommodations, please let your recruiter know.
BambooHR is An Equal Opportunity Employer-M/F/D/V
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BambooHR, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BambooHR AI Hiring
BambooHR has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Draper, UT, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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