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About This Role
The Company
GitKraken is the developer experience (DevEx) platform of choice for more than 40 million developers and 100,000 organizations globally. Combining built\-in AI and powerful workflow orchestration, GitKraken empowers development teams to eliminate unnecessary toil, streamline collaboration, and accelerate productivity. GitKraken’s seamless integrations with leading Git providers, issue tracking tools, and AI solutions make it the most versatile DevEx platform available across desktop, command line, IDE, web, and mobile environments. Discover smarter, faster development at www.gitkraken.com or follow us on LinkedIn.
The Role
At GitKraken, our goal is to help developers and their teams focus, create, and collaborate while minimizing distractions, context switching, and wasted time. Our developer experience platform supports millions of developers across desktop, command line, IDE, browser, web, and mobile.
We’re looking for a pragmatic, startup\-minded Senior Machine Learning Engineer or Applied Data Scientist who can take an idea from concept to production. Sometimes that idea will come from the data. Sometimes it will come from the business. In both cases, you’ll be expected to determine what’s possible, identify the fastest credible path forward, and ship solutions that create measurable impact.
This is a high\-ownership role for someone comfortable working across data, product, and engineering. You should be able to frame ambiguous problems, explore messy data, build models or heuristics, integrate with production systems, measure outcomes, and iterate quickly. We care about practical impact, traction, and speed of learning. We are not looking for someone who waits for perfect specs or over\-polishes a solution before proving it matters.
What You'll Do
- Identify high\-value opportunities from product, customer, and operational data
- Evaluate ambiguous ideas quickly and determine what is feasible, useful, and worth shipping
- Identify high\-value opportunities from product, customer, and operational data
- Build practical 80/20 solutions that create leverage quickly, then refine them based on traction
- Own end\-to\-end execution across data exploration, modeling, experimentation, backend integration, and productization
- Partner with engineering, product, design, and leadership to turn rough ideas into shipped capabilities
- Use ML, analytics, heuristics, and automation pragmatically rather than forcing a model where one is not needed
- Define success metrics, instrument outcomes, and improve solutions based on real\-world usage
- Help shape how GitKraken uses AI and data to improve developer workflows, team velocity, and product experience
Our Tech Lens
We value strong fundamentals over a rigid checklist and are always open to adopting new technologies, here is a snapshot of our current ecosystem:
- Languages: Python (for data/ML execution), alongside Go and TypeScript across our core product and backend environments.
- Data \& Infrastructure: Snowflake for data warehousing, AWS for cloud infrastructure, and Datadog for monitoring and observability.
- AI Ecosystem \& DevEx: We live and breathe developer experience. We heavily leverage and build around modern AI development tools and LLMs like Cursor, Claude Code, and Codex to accelerate execution and shape the future of workflows.
What We're Looking For
- Deep experience in machine learning, applied AI, or a similarly hands\-on product data role at a Senior level
- A track record of shipping data or ML\-powered capabilities into real products or operational workflows
- Comfort moving from messy problem statements to practical execution without a lot of structure
- Ability to work across the stack, not just in notebooks
- Strong product judgment and a bias toward simple solutions that deliver measurable value
- Experience deciding whether a problem is best solved with ML, rules, analytics, automation, or workflow design
- Ability to balance speed and rigor, including knowing when “good enough to learn” is the right answer
- Strong communication skills and the ability to explain tradeoffs clearly to technical and non\-technical partners
- Ownership mindset: you don’t wait for perfect specs, and you follow through from idea to impact
Bonus Points
- You’ve built and shipped data or ML\-powered features, not just analyses
- You can prototype quickly and are comfortable refining after launch
- You know how to avoid getting buried in edge cases before the core value is proven
- You like working in a company with a bias toward action, accountability, and high ownership
- You want your work to directly influence product direction and business outcomes
How you'll be rewarded
Excellence — Competitive compensation with annual performance\-based pay increases
Balance — Flexible Paid\-Time\-Off Policy \& paid company holidays (chosen by our employees)
Parent life — Generous paid parental leave
Pets — Pet insurance plan (with no exclusions)
Health — Health, dental, and vision insurance with competitive employer cost\-sharing
Headquarters — Modern, fully equipped offices designed to maximize productivity in a hybrid environment
Culture — Great Place to Work Certified
Growth — Paid career development opportunities, audiobook subscriptions, and mentorship
Future — 401(k) retirement plan plus company matching
Travel — Company paid domestic trip after your 1\-year anniversary \& an international trip every 5 years
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At GitKraken, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
GitKraken AI Hiring
GitKraken has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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