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About This Role
About the Job:
LaunchDarkly's AI Specialist will be at the forefront of revolutionizing how AI engineers think about, release, monitor, and optimize their agents, scaling up our AgentControl from initial market validation and product relaunch to an industry\-leading product.
You will represent deep, specialized AgentControl product knowledge, sharing your expertise with prospects and customers, gathering critical feedback that shapes product development, and driving early\-stage adoption. Your expertise will be instrumental in establishing the Solutions Engineering frameworks that will guide our future success.
This is a role for builders and storytellers who thrive in undefined spaces. You will be expected to *build* *–* meaning the construction of integrations, technical content, whitepapers, and tutorials, all alongside attractive demos of our product customized to strategic engagements. You'll work with the innovation coming out of LaunchDarkly's Product team and make a significant impact on how companies build and deploy AI applications.
#### Responsibilities:
### Expert\-Level Solutions/Sales Engineering
- Bring serious, credible expertise in practical AI applications to customer conversations.
- Spearhead the early\-stage evaluation, implementation, and adoption of AgentControl at scale, working closely with existing customers to ensure activation and churn prevention (alongside feedback).
- Partner with the AI SME team to develop and document Solutions Engineering playbooks and best practices that scale beyond our early customers.
- Partner closely with the AI Strategy Lead and AI SME team surfacing revenue\-related insights as the product is deployed.
- Lead AgentControl POVs to validate technical win and secure revenue from our largest customers.
### Collect and Propagate Product Feedback
- Collaborate extensively with product and engineering teams to ensure product concepts are technically feasible and align with LaunchDarkly's strategic goals.
- Drive continuous improvement by monitoring product performance, user experience, and market response, iterating based on actionable data and insights.
- Ensure AgentControl's roadmap tracks market demand and delivers an exceptional experience for the AI developer persona.
### Scale the AgentControl Business
- Deliver integrations against common, quantified customer requests in the form of code contributions, architecture diagrams, and whitepapers
- Function as a key technical asset in technical partnerships with advantageous potential partners (like Anthropic, DataBricks, etc…)
- Publicly evangelize AgentControl at mainstream industry conferences, webinars, partner engagements, and strategic meetings
### Technical Leadership \& Communication
- Partner with LaunchDarkly's AI SE SME team to support broader organization enablement on AI and the AgentControl product.
- Support Field Team Enablement of AgentControl.
- Work with the AI Researcher, the PMM team, and the AI Strategy Lead to build and maintain competitor playbooks.
#### Qualifications:
AI \& Technical Expertise
- Extensive experience with AI applications including building, implementing, or selling AI solutions at scale
- Experience building multi\-agent systems using frameworks like LangGraph, AgentBuilder, or AgentCore
- Hands\-on experience evaluating AI agent performance at scale using automated evaluation methods
- Deep understanding of LLM mechanics (you've read 'Attention is All You Need' and can explain transformer architecture in detail)
- Experience building or interfacing with MCP (Model Context Protocol) servers
- Strong Python skills with experience building in PyTorch or TensorFlow
- Strong foundation in software engineering principles and current market trends
- Typically requires a minimum of 12 years of related experience
Mindset \& Approach
- Strong but loosely\-held opinions about AI—you have a point of view but update it based on evidence
- Ability to anticipate where the AI landscape is heading and position products accordingly
- Deep curiosity about how AI changes software development, with an obsession for staying current on new AI technology
- Natural storyteller who can take new technology and craft compelling narratives that resonate with technical audiences
- Thrive in ambiguity—you love figuring it out, building new processes, and working in undefined spaces
### How This Role Connects
The AI Specialist operates within LaunchDarkly's broader AI organization:
- You report through the AgentControl Specialist POD organization while maintaining a dotted\-line relationship to the SE team.
- You collaborate closely with the AI Researcher, translating their strategic insights into customer\-facing conversations and surfacing field intelligence that informs research priorities
- You coordinate with other AI SMEs across the SE organization to build collective expertise
### How You'll Be Measured
- NNARR of AgentControl
- Activation \- usage in terms of both volume (\#AIC, \#Evals) and breadth (\#AI Experiments, \#AI Guarded Releases)
- Quality of customer feedback integrated into product development
- Effectiveness of Solutions Engineering playbooks and documentation (\# of enablement sessions / internal engagement (e.g. views), impact on conversion rate).
- Successful early\-stage implementations and measurable customer impact
- Contribution to product\-market fit through customer insights and technical validation
Pay:
*Target pay ranges based on Geographic Zones\* for Level 4:*
- Zone 1: *San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle* \- $214,800 \- $295,350\*
- Zone 2: *Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago \- $193,400 \- $265,870*\*\*
- Zone 3: *All other US locations \- $182,600 \- $251,0202*\*
*LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location.*
- *Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas.*
*\*\*Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary.*
About LaunchDarkly:
Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise. Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software. The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong. Developers can target product experiences to any customer segment and maximize the business impact of every feature. And by gradually rolling out new application components, they escape nightmare "big\-bang" technology migrations.
The LaunchDarkly platform was built to guide engineers to the next frontier of DevOps by:
- Improving the velocity and stability of software releases, without the fear of end customer outages
- Delivering targeted experiences by easily personalizing features to customer cohorts
- Maximizing the business impact of every feature through the ability to experiment and optimize
- Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types
- Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability
At LaunchDarkly, we believe in the power of teams. We're building a team that is humble, open, collaborative, respectful and kind. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status. LD invites any applicant to review our written Affirmative Action Plan. To do so, contact People Ops at [email protected].
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Salary Context
This $182K-$295K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LaunchDarkly, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($238K) sits 9% above the category median. Disclosed range: $182K to $295K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
LaunchDarkly AI Hiring
LaunchDarkly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oakland, CA, US. Compensation range: $295K - $295K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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