Interested in this AI/ML Engineer role at Honeycomb.io?
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About This Role
What We’re Building
Honeycomb is a service for the near and present future, defining observability and raising expectations of what developer tools can do! We’re working with well known companies like HelloFresh, Slack, LaunchDarkly, and Vanguard and more across a range of industries. This is an exciting time in our trajectory, we’ve closed Series D funding, scaled past the 200\-person mark, and were named to Forbes’ America’s Best Startups of 2022 and 2023!
If you want to see what we’ve been up to, please check out these blog posts and Honeycomb.io press releases.
Who We Are
We come for the impact, and stay for the culture! We’re a talented, opinionated, passionate, fiercely inclusive, and responsible group of bees. We have conviction and we strive to live our values every day. We want our people to do what they truly love amongst a team of highly talented (but humble) peers.
How We Work
We are a fully distributed company, which means we believe it is not where you sit, but how you deliver that matters most. We invest in our people and care about how you orient to our culture and processes. At the same time we imbue a lot of trust, autonomy, and accountability from Day 1\. \#LI\-Remote
- #### Little more about the team:
The LLM Observability (LLM O11y) team builds the foundational layer that makes AI workloads observable in Honeycomb. We define how AI systems are represented in telemetry (spans, traces, token usage, cost, eval signals, etc), and ensure that data from diverse ecosystems (e.g., LangChain, OpenLLMetry, Braintrust) is normalized into a consistent OpenTelemetry\-based model.
This team owns the infrastructure and data model that all downstream AI observability experiences rely on. Our work powers everything from cost analysis to anomaly detection. If you’re excited about defining and building the “source of truth” for how AI systems behave in production, this is the team for you.
#### What you’ll do in the role:
+ Design, build, and deliver backend systems and APIs. You'll architect and implement APIs and backend services in Go, emphasizing clarity, maintainability, and backward compatibility. You'll think proactively about cost, scale, and risk — anticipating technical issues and exploring alternatives and tradeoffs thoroughly.
+ Build and maintain full\-stack product features. You'll contribute frontend (React/TypeScript) and backend (Go) code to build robust product features, maintaining proficiency across the stack. You'll deliver impactful features that add value for our customers, our team, and Honeycomb overall.
+ Provide technical leadership. You'll define patterns and operational guidelines that raise the bar for the team and beyond. You'll plan, lead, and deliver complex, multi\-team projects — including ones with external dependencies — ensuring alignment and mentoring teammates along the way. We rotate tech lead responsibilities with each feature to allow everyone on the team to use and grow these skills.
+ Collaborate across disciplines. You'll work closely within a product triad model alongside design and product partners, from discovery through delivery, iterating quickly based on user feedback. You'll contribute to the team's technical roadmap and help shape quarterly product roadmaps, tying technical considerations to business outcomes.
+ Support and own your software in production. You'll participate in on\-call rotations to ensure reliable operations and proactively address user pain points. You know when to shift between forward\-looking feature work and production needs.
+ Communicate clearly and multiply your impact. Regular written and verbal communication, collaboration, and feedback are foundational skills you'll use daily. You'll mentor engineers, share knowledge, and delegate to help others grow — your goal is to make everyone around you more effective.#### What you’ll bring to the role:
+ Deep experience designing, building, and maintaining backend systems. You have substantial expertise building APIs and backend services in Go (or similar languages), with strong opinions about API design, error handling, and developer experience. You've seen many different patterns and have developed your own frameworks for evaluating how to approach new technical challenges.
+ Frontend proficiency with React and TypeScript. You have professional experience delivering frontend features and contributing to full\-stack software through all stages of the development lifecycle, though your primary expertise is in backend systems.
+ Experience leading complex technical projects. You've planned and delivered projects that span multiple teams or systems, navigating external dependencies and uncertainty. You know how to break down complex work, ship incrementally, and balance technical investment with business goals.
+ Ownership and autonomy. You proactively identify problems, propose solutions, and own your projects end\-to\-end — from planning through deployment and ongoing maintenance. You tackle tech debt proactively and can debug the toughest issues through the entire stack.
+ A track record of making others better. You've mentored engineers, led technical design reviews, and helped raise the quality bar for a team or domain. People come to you for guidance, and you help build leaders around you.
+ A collaborative and flexible mindset. You're comfortable working cross\-functionally, pivoting priorities to address evolving customer or business needs, and maintaining strong teamwork. We work by building small changes and iterating based on customer interaction.
+ Clear, open communication. We see clear, thoughtful, and regular written and face\-to\-face communication — including a willingness to give and receive feedback — as a fundamental engineering skill and one of the strongest predictors of engineering impact, not just a nice to have, and we hope you do too.
+ An interest in observability and production culture. We maintain an on\-call rotation and work hard to keep it from becoming a bad experience. You've supported software in production and have opinions about how to do it well. Bonus points if you want to fold that experience into the Honeycomb product itself.
+ Nice to have: experience with AI\-assisted development. You're proficient with AI coding tools (Copilot, Claude Code, Cursor, etc.) or are excited to incorporate them into your workflow. We're actively exploring how AI can accelerate our development practices.\#LI\-DNI
Base Salary based on level of experience
$183,340 \- $206,000 USD
What you'll get when you join the Hive:
- A stake in our success \- generous equity with employee\-friendly stock program
- It’s not about how strong of a negotiator you are \- our pay is based on transparent levels relative to experience
- Time to recharge with unlimited PTO
- A distributed\-first mindset and culture (really!)
- Home office, co\-working, and internet stipend
- Full benefits coverage for employees, with additional coverage available for dependents
- Up to 16 weeks of paid parental leave, regardless of path to parenthood
- Annual development allowance
- And much more...
Please note we cannot currently sponsor or support visa transfers at this time. Additionally, in compliance with applicable law, all persons hired will be required to verify identity and eligibility to work.
Phishing and Recruitment Scam Warning:
We take your security seriously. Please be aware that recruitment scams are increasingly common and scammers may create email addresses or websites to impersonate Honeycomb employees. To help protect you:
- All communications will come from an @honeycomb.io email address
+ We occasionally work with external recruiting agencies. These partners will use legitimate business email addresses—never personal accounts like Gmail or Yahoo.
- Our recruiting process will never ask you to provide financial or sensitive personal information, including but not limited to:
+ Social security or tax identification numbers
+ Credit card numbers
+ Bank account information
Diversity \& Accommodations:
We're committed to building a diverse, inclusive, and equitable workplace—where people of all backgrounds, identities, experiences, and abilities are welcomed, valued, and supported. We recognize that there is no single path to success and embrace nontraditional career journeys and diverse perspectives as key to building stronger, more innovative teams.
We strive to ensure an inclusive experience throughout every stage of our hiring process and are happy to provide reasonable accommodations as needed. If you require accommodations or accessible formats at any point during our hiring process, please let your recruiter know.
As an equal opportunity employer our hiring process is designed to put you at ease and help you show your best work. If there’s anything we can do to improve your experience, we’re always open to feedback.
Privacy Notice:
If you apply for a job at Honeycomb and your application is unsuccessful (or you withdraw from the process or decline our offer), Honeycomb will retain your information after your application for a period of time in accordance with local laws. We retain this information for various reasons, including in case we face a legal challenge in respect of a recruitment decision, to consider you for other current or future jobs at Honeycomb, and to help us better understand, analyze and improve our recruitment processes.
For more information regarding our privacy practices please see the Honeycomb Privacy Notice.
If you do not want us to retain your information for consideration for other roles, or want us to update it, please contact [email protected]. Please note, however, that we may retain some information if required by law or as necessary to protect ourselves from legal claims.
Salary Context
This $183K-$206K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Honeycomb.io, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($194K) sits 9% below the category median. Disclosed range: $183K to $206K.
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.
Honeycomb.io AI Hiring
Honeycomb.io has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $206K - $206K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 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).
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 4,317 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.
Frequently Asked Questions
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