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About This Role
I’m a successful tech entrepreneur starting a small, focused venture studio to iteratively build several private and early\-stage software products.
I work from clear, detailed “product constitutions” / executable specs and use AI coding tools (Claude Code, Cursor, etc.) heavily. I’m not looking for someone to just write code. I’m looking for a senior, product\-minded engineer who can help me architect systems correctly from the start, avoid early mistakes, and guide AI\-assisted development.
This is a fractional, high\-judgment role focused on architecture, sequencing, and leverage — not volume coding.
What you’ll do:
- Translate product constitutions into clean, scalable system architecture
- Design data models, access rules, and invariants
- Decide what belongs in v1 vs what should wait
- Set guardrails for AI\-assisted development (what AI can and can’t touch)
- Review and improve AI\-generated code
- Occasionally implement critical pieces where judgment matters
- Help establish reusable patterns across multiple products
You will NOT be:
- Managing a team
- Working from vague requirements
- Over\-engineering infrastructure
- Building pixel\-perfect UI from design files
This role is ideal for a senior IC or ex\-founder who enjoys early\-stage product architecture, values clarity over process, and is excited about AI\-augmented development.
Responsibilities:
- Architecture \& System Design
- Translate product constitutions, executable specs, and business requirements into clean, scalable system
- architectures
- Design data models, access rules, invariants, and API contracts that hold up as products grow
- Make critical build\-vs\-wait decisions — what belongs in v1, what should be deferred, and what should
- never be built at all
- Establish the integration architecture connecting corporate systems (Paylocity, Jira, financial systems, Google Workspace) into a coherent data layer
- Design and implement the connectivity layer that allows external software — including third\-party SaaS tools, partner systems, and internal services — to plug into the MOS and other company platforms via APIs, MCP servers, webhooks, and other interoperability protocols
- Design modular, composable system boundaries so new capabilities can be added without rewriting existing ones
- AI\-Assisted Development Leadership
- Set guardrails and standards for AI\-assisted development across engineering efforts
- Define what AI coding tools can and can't touch — where automated generation is safe and where human judgment is required
- Review, refactor, and improve AI\-generated code to meet production quality standards
- Establish reusable patterns, templates, and architectural conventions that AI tools can follow consistently
- Evaluate and integrate emerging AI development tools into company workflows
- Product Engineering
- Personally implement critical system components where architectural judgment matters — integration layers, data pipelines, security boundaries, and core business logic
- Collaborate with the Senior AI Engineer on the MOS platform's agentic architecture, agent communication protocols (A2A, MCP), and module design
- Build and maintain MCP servers and API integrations that allow the MOS to read from, write to, and orchestrate actions across the company's external tool ecosystem (e.g., Asana, Jira, Slack, Salesforce, Google, Workspace, Paylocity)
- Build and validate proof\-of\-concept implementations for new product capabilities before committing to full builds
- Ensure that the natural\-language configuration layer, proactive communication engine, and agent discovery framework are architecturally sound
- Cross\-Product Leverage
- Establish reusable architectural patterns, shared infrastructure, and common libraries across product portfolios.
- Create and maintain technical decision records so architectural choices are documented and revisitable
- Ensure consistency in how products handle authentication, authorization, data access, inter\-service communication, and external system integrations
- Define standard patterns for how new external tools and data sources are connected to company platforms ensuring every integration follows a consistent, secure, and maintainable approach
- Identify opportunities to extract shared capabilities into platform\-level services
- Required Qualifications
- 10\+ years of software engineering experience, with a strong track record of early\-stage product architecture and system design
- Deep experience making v1 scoping decisions — knowing what to build now, what to defer, and what to cut
- Strong data modeling skills — you can design schemas, invariants, and access rules that hold up under real\-world complexity
- Hands\-on proficiency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or equivalent) and a clear philosophy on how to use them effectively
- Full\-stack capability with a backend emphasis — comfortable across API design, data pipelines, infrastructure, and frontend integration
- Excellent architectural judgment — you design systems that are simple enough to ship and flexible enough to evolve
- Strong API design and systems integration skills — deep experience with REST, GraphQL, webhooks, real\-time data sync, and building connectors between disparate software systems
- Familiarity with LLM application patterns including RAG, prompt engineering, agentic workflows, and tool\-use architectures
- Working knowledge of MCP (Model Context Protocol) or similar protocol\-based interoperability standards for connecting AI agents to external tools and data sources
- Ability to read, review, and substantially improve code generated by AI — not just accept it
Preferred Qualifications
- Founder or early\-stage engineering experience — you've built products from zero to one and made hard tradeoff decisions with limited resources
- Experience with multi\-agent system design, orchestration patterns, and protocol\-based interoperability (A2A, MCP, or equivalent)
- Hands\-on experience building MCP servers or similar integration layers that expose external SaaS tools to AI\-native platforms
- Familiarity with enterprise SaaS integration — particularly Paylocity, Jira, Asana, Salesforce, Culture Amp, Slack, or Guru APIs
- Experience replacing or consolidating enterprise SaaS tools with custom\-built platforms
- Background in management operating systems, OKR frameworks, or corporate strategy tooling
- Understanding of compliance and data privacy frameworks (SOC 2, etc.)
- Experience establishing engineering standards and development conventions for small, high\-output teams
- Track record of mentoring or guiding other engineers through architectural decisions without formal management authority
- Application Question(s):
- How do you currently use AI coding tools (Claude, Cursor, Copilot, etc.) in your development workflow?
- What is your hourly rate and how many hours per week are you available for a fractional engagement?
- Briefly describe a product you helped architect early that later evolved significantly. What decisions did you make early that mattered most?
Work Location: Must be located in Puerto Rico\-Hybrid remote in San Juan, PR 00901
Work Location: Hybrid remote in San Juan, PR 00901
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 Lateral, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
Lateral AI Hiring
Lateral has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Juan, PR, US.
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.
Frequently Asked Questions
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