Interested in this AI/ML Engineer role at BambooHR?
Apply Now →About This Role
Please Note: This is a remote position available in the state listed on this job. Additionally, employment with BambooHR is contingent on passing both a background and credit check.
AI at BambooHR: At BambooHR, we believe in leveraging cutting-edge technology to empower people and transform HR. We're actively integrating AI into our solutions and workflows to enhance efficiency and drive innovation. To that end, we're looking to our existing team members and future hires to share this forward-thinking mindset: individuals who are curious about AI's potential, eager to learn and adapt, and ready to explore how intelligent tools can elevate their work along with BambooHR's impact on setting people free to do great work. Join us in reimagining the future of HR!
Essential Job Duties
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. We're looking for software engineers who use AI to build production-quality software. As a Senior Agentic Software Engineer, you lead complex features that span multiple workstreams, and you shape the technical direction other engineers execute from. You influence what gets built across your product area, working with product, design, and support to shape both what should be built and how to build it.
AI tools and capabilities change rapidly. To stay resilient through change, you prioritize curiosity and continual learning, and you actively apply new best practices. You are accountable for outcomes that reach beyond your own features: the technical direction you set, the engineers you mentor, and the full lifecycle of the work from specification through production.
What You'll Do
Your work runs through four loops, each building on the last: specifying intent, delegating execution to agents, verifying and integrating their output, and codifying best practices and processes to compound value.
- Shape ambiguous product opportunities into technical direction that other engineers build from.
- Write agent specifications complete enough for a teammate to execute confidently, setting the quality bar for the constraints, acceptance criteria, risks, and tradeoffs they define.
- Apply agentic judgment at the workstream level, setting the example for the team on when to delegate to agents and where human judgment is needed.
- Steer multiple agents through implementation across concurrent workstreams, multiplying what you deliver while keeping engineering judgment and ownership.
- Set the verification bar for a feature or workstream, matching review depth to risk and blast radius and validating with tests, observability, and production-readiness checks.
- Own the full feature lifecycle, from specification through production monitoring, including the correctness, security, maintainability, and customer impact of the work.
- Decide which prompts, agent instructions, repo conventions, test harnesses, and playbooks are worth building to speed up the engineers around you.
- Influence product decisions across your product area, bringing engineering judgment into what gets built.
- Co-create specifications and technical direction with product, design, and support, converging on alignment across your product area.
- Mentor engineers on agentic craft, raising how the team specifies, delegates, and verifies.
What You Need to Get the Job Done
- You will need depth in these areas to lead workstreams, shape technical direction, and raise the engineers around you.
- 5+ years of software engineering experience, with a record of building and shipping customer-facing software in production, including ownership through production monitoring.
- Track record of using AI coding agents to deliver features across parallel workstreams.
- Proficiency in designing systems, data models, and architecture that other engineers build on, including performance and data-flow tradeoffs.
- Demonstrated ability to write agent specifications a teammate can execute confidently, and technical documentation readable by both people and agents.
- Depth in context engineering: structuring repo and task context so agents execute reliably across a team.
- Proven ability to design agent workflows for parallel execution, with human checkpoints throughout.
- Confidence reading unfamiliar code and running root-cause analysis to debug it.
- Demonstrated ability to perform threat-modeling and identify security vulnerabilities: injection, data-handling, and unsafe generated output.
- Fluency setting test design and evaluation standards that others follow.
- Sound judgment managing version control at parallel-workstream scale: branching, reviewing, and integrating concurrent changes safely.
- Track record of setting safe-release and reliability standards: CI/CD, feature flags, staged rollout, and rollback.
- Experience operating and maintaining software in production with observability, metrics, traces, and incident response.
- Proven ability to mentor engineers and influence technical decisions beyond your own features.
- Strong written and verbal communication, including synthesizing technical direction and tradeoffs for technical and non-technical audiences.
What You'll Love About Us
- A Great Company Culture that has been recognized by multiple organizations like Inc, and Salt Lake Tribune
- Comprehensive health, life, and disability insurance
- Generous leave policies that include 4 weeks of vacation, 12 company holidays, parental leave, and volunteer time off so you can enjoy quality of life
- 401k plans with up to 6% company match
- $2000 Paid-Paid Vacation bonus
- EAP through Headspace
- Check out all our benefits that benefit you
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
Because our team members are trusted to handle sensitive information, we require all candidates that receive and accept employment offers to complete a background check before being hired.
For information on California Privacy Policy, click here*.*
Our process utilizes AI as an assistant to efficiently process and analyze candidate data. Recruiters and hiring managers maintain full oversight and accountability, ensuring that all final selection and rejection decisions are human-made and based solely on objective job qualifications. Please see our General Privacy Notice and California Privacy Notice for more details.
See our AI Guidelines for Candidates for details on how BambooHR uses AI in recruiting, how we expect candidates to use AI, and what is not allowed.
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 9,653 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
BambooHR AI Hiring
BambooHR has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Draper, UT, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.