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About This Role
Technical Lead/Architect
Remote \- United States
About the Role
We're a small team within Blackboard building a new AI\-native product. This is not a feature team, and it's not an enhancement to an existing product — we're building something new.
We use Claude Code, Cursor, and AI\-assisted tooling to generate the majority of our code. Our engineers are architects, reviewers, and product thinkers — people who direct AI coding agents effectively rather than writing every line by hand.
As Technical Lead, you set this culture. You will establish the norms, the workflows, and the standards for how AI\-native development actually works at scale on a small team. The person we're looking for is excited by this model, not threatened by it.
You are the technical leader. You will design, architect, and build the core system — a stateful, multi\-layer architecture spanning data ingestion, decision logic, and AI\-powered delivery — and recruit a small senior team to build alongside you.
This is a builder role first. If you're looking to step into a seat where you primarily direct others, this isn't the right fit. We need someone who still loves building — and is exceptional at it. You will own the full technical vision and be accountable for it.
What You'll Build
Core Intelligence Architecture
- A multi\-layer system where each layer is decoupled, auditable, and independently evolvable
- A persistent learner state model built to reason across complex, longitudinal user data
- A rules\-based decision layer where logic is explicit and transparent — every recommendation can be explained
Data \& Integration Layer
- Event\-driven pipelines that translate real\-time platform activity into structured, reliable signals
- APIs that connect the intelligence layer to the user\-facing product
- LMS integration for real\-time data ingestion, designed to scale across platforms over time
AI \& Agent Architecture
- A multi\-agent system backed by shared state — precision\-bounded interventions, not open\-ended AI responses
- Prompt engineering and guardrail systems that keep agent behavior auditable and aligned
- Observability infrastructure so every system decision can be monitored, debugged, and improved
Engineering Culture \& Team
- Establish an AI\-native development culture: AI tools as the default, human judgment as the filter
- Weekly ship cycles, high observability, and a bias toward iteration over speculation
- Hire and mentor a small senior team (2–3 Product Engineers) as the product scales from v1 to v2
- Stay on the bleeding edge of the AI ecosystem — Claude Code, Cursor, OpenAI Agent SDK, MCPs, and whatever comes next. Make the call on what we adopt, when, and why. That judgment sets the standard for the whole team.
The Right Person
You believe that intelligence should be deterministic where it can be, and generative where it should be. You're skeptical of LLMs as decision\-makers but excited about them as expression engines. You've seen what happens when AI systems are unauditable — and you've built against that failure mode.
You're energized by constraints: small team, big surface area, real stakes. You move fast, accept imperfection in v1, and know that iteration is how great systems are built. You want the work to matter.
You think in systems, not screens. You hold strong opinions, loosely held — and you care more about impact than credit.
Please include a link to your portfolio/GitHub as part of your application.
Required Skills and Experience
- Proven track record shipping complex, stateful AI or data\-driven systems in production — not just prototypes
- Fluency in LLM application development: orchestration, RAG pipelines, prompt engineering, guardrail design
- Experience designing multi\-agent systems with shared state, async architectures, and observability infrastructure
- Strong architectural judgment — you understand why rules should decide and AI should assist, and you build accordingly
- Demonstrated ability to ship at startup velocity: high ownership, weekly cycles, bias toward action
- You work natively with AI coding tools (Cursor, Claude Code, or equivalent) — this is how you actually build, not a checkbox
- Experience recruiting and mentoring engineers
- Fluency in written and spoken English
Preferred Skills and Experience
- Prior experience at an AI\-native startup or as a technical co\-founder
- Familiarity with LMS ecosystems: LTI 1\.3, xAPI/SCORM, platform APIs
- Background in domains where explainability and institutional trust are non\-negotiable (EdTech, health tech, fintech)
About Blackboard
Blackboard advances teaching excellence and unlocks the full potential of technology to deliver meaningful outcomes. We empower institutions to deepen connections between educators and learners, inspire engagement, and drive long\-term academic success across the full learner journey. For more information, please visit www.blackboard.com.
*The expected salary range for this position is $169,400 \- $250,000\. The range reflects base salary only and does not include additional compensation such as company* *bonus* *or benefits. Placement within the pay range will depend on a variety of factors, such as experience, skills, internal parity, and location.*
Candidates must be legally authorized to work in the country where the role is based at the time of hire and must maintain that authorization for the duration of employment. The company does not provide visa sponsorship or immigration support for this position.
This job description is not designed to contain a comprehensive listing of activities, duties, or responsibilities that are required. Nothing in this job description restricts management's right to assign or reassign duties and responsibilities at any time.
Blackboard is an equal employment opportunity/affirmative action employer and considers qualified applicants for employment without regard to race, gender, age, color, religion, national origin, marital status, disability, sexual orientation, gender identity/expression, protected military/veteran status, or any other legally protected factor.
\#LI\-JO1
Salary Context
This $169K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Anthology Inc., 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. This role's midpoint ($209K) sits 16% above the category median. Disclosed range: $169K to $250K.
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.
Anthology Inc. AI Hiring
Anthology Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $250K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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