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About This Role
About Community Solutions:
Community Solutions is a “field catalyst” working to align and mobilize actors, locally and nationally who impact homelessness toward the shared goal of making homelessness rare and brief, everywhere. The organization directly supports more than 140 communities in the US in implementing accountable, community-wide efforts that drive measurable reductions in homelessness (https://community.solutions/built-for-zero/the-movement/). This solutions-focused approach involves rigorous collaboration of public and private efforts, comprehensive, real-time information on those experiencing homelessness and increased access to housing. By 2027, Community Solutions' network aims to reach a tipping point where homelessness is widely perceived to be solvable and public opinion has measurably shifted to support solutions; reductions in homelessness have been achieved in a diverse and growing array of communities, and solutions-focused policies increasingly drive federal, state, and local responses.
Achieving this “tipping point” requires the political will of elected officials, which in turn requires the expectation and support of community leaders.
Position Overview:
Community Solutions has built working AI tools in-house, quickly and at a cost that wasn't possible before, and our portfolio is growing. The constraint now is that nearly all of the hands-on work- building the tools, making the build-or-partner calls, carrying the funder and technology relationships, preparing and executing a testing and deployment strategy, and forecasting the role that AI may play in our future strategy for making homelessness rare and brief nationwide sits with a small group part-time. The Senior Consultant will provide additional capacity and expertise to advance this work.
The Senior Consultant will support and advance the organization's AI work in the field: guiding the building and shipping of tools for testing and refinement within our network of 170+ communities and informing the strategy for moving high-impact prototypes into adoption across the network. The Senior Consultant will help inform and apply Community Solutions' principles around AI use and assure everything produced keeps a human in the decision loop and protects the dignity and data of the people the work serves. The Senior Consultant will review AI tools throughout development and deployment to control for bias.
The Senior Consultant will work closely with Community Solutions' internal and external partners to advance the AI portfolio, support the development and testing of tools, and help identify opportunities to move high-impact prototypes toward adoption. The Senior Consultant will also develop the budget associated with the go-to-market strategy and present recommendations to the Community Solutions Executive Team and Board of Directors for review and approval.
Defining Success for this Position in Year One:
- At least one AI tool moves from prototype to active use in Community Solutions' partner communities.
- The privacy-safe infrastructure needed to pilot tools that touch sensitive data is stood up and validated.
- At least one meaningful technology or funding partnership is established that did not exist before this person joined.
- The portfolio compounds, with tools building on each other rather than accumulating as one-offs.
- The Year One AI strategy aligns with and informs Community Solutions' post-2026 strategy.
- Community Solutions has a credible, grounded voice on AI in homelessness response, demonstrated through what we ship and what we publish.
Essential Duties and Responsibilities:
A meaningful part of the role may involve building. The Senior Consultant will determine optimal strategies for developing and testing AI tools and prototypes, will own the technical architecture for the portfolio, and make the call on what to build in-house versus what to partner on or contract out. For anything touching client data, the Senior Consultant will be responsible for establishing a privacy-safe and local deployment approach.
Much of the initial work is expected to be external-facing but will interact with the in-house development team and potential development partners to continually refine high-impact products, manage relationships with technology partners, pursue opportunities through corporate AI programs, accelerators, and philanthropy, and track the AI for good landscape closely enough to move on time-sensitive windows. The Senior Consultant will be expected to carry key build and implementation relationships directly.
For the essential field adoption and governance questions involved in testing and implementing AI tools, the Senior Consultant will oversee the work with partner communities to pilot tools, gather feedback, and determine a scale strategy for tested tools with high impact potential. This will involve guiding the translation of tools into documentation and training accessible to practitioners.
Professional Experience and Qualifications:
- The combination this role requires is unusual: technical depth, product judgment, and the ability to manage external partnerships without much hand-holding.
- Demonstrated ability to build with current AI tools and ship working software, with comfort across the stack, APIs, prototyping, and data work. Familiarity with model deployment, security, and compliance is a strong plus.
- Product judgment, meaning a track record of knowing what is worth building and what isn't.
- Experience managing partnerships with external organizations, ideally including technology or funding partners.
- Comfort operating in ambiguous, fast-moving situations and a bias toward shipping over planning.
- Commitment to Community Solutions' mission. Familiarity with homelessness or human services; data is a plus, or the ability to learn it fast matters more.
- Strong written and verbal communication skills, with the ability to work credibly with field users, technology partners, and funders.
Salary
The compensation range for this consulting position is $168,827 - $191,848. The actual compensation will be commensurate with the consultant's experience and qualifications.
Work Environment
The position provides for the flexibility to work from a remote US location without the standard support available at an office. Travel will be required to meet with communities, partners, and allies.
Diversity and Inclusion
We strive for inclusivity and diversity by attracting extraordinary people from diverse backgrounds and lived experiences. We seek to employ an all-star team of people who vary by their race and ethnicity, gender identity, sexual orientation, nationality, age, culture, religion, veteran status, physical and mental abilities. We promote equal opportunity in the recruitment, selection, training, compensation, promotion, and benefits of all employees. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Salary Context
This $168K-$191K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Community Solutions, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $168K to $191K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Community Solutions AI Hiring
Community Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $191K - $191K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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