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About This Role
Precision is an award\-winning integrated strategy and marketing agency based in Washington, DC and New York, NY. Powered by data, we bring world\-class experts in each of our service areas to conceive, develop, and run campaigns that help our clients seize opportunities and solve their biggest challenges.
We're at the center of today's biggest debates and defining moments, inside the Beltway, across New York's media and financial worlds, and in state capitals across the country. Our work sits at the intersection of politics, business, and culture, helping leaders and organizations make an impact when it matters most. We hire people who are the best at what they do, and who like to have fun doing it; we don't just break through. We break new ground.
Our team is seeking a principal to join our Strategic Communications team in New York, NY or Washington DC to lead cutting\-edge email marketing strategy for clients ranging from corporate leaders to advocacy groups.
*What You Will Be Doing:*
Client Management
- Lead and oversee multiple client email initiatives or programs from conception through execution, owning the full campaign lifecycle: planning, content development, QA, deployment, and performance analysis.
- Manage work streams across multiple client accounts, ensuring timely delivery and coordination.
Digital Specific
- Email Program Leadership: Own end\-to\-end email strategy for client accounts, including acquisition, retention, drip, and triggered campaigns. Drive personalization and segmentation initiatives to deliver tailored messaging that increases audience relevance, conversion, and long\-term engagement.
- Audience Segmentation: Build data\-driven audience segments for targeted email campaigns and automated customer journeys, leveraging behavioral data, preferences, and engagement signals to optimize experiences.
- Testing \& Optimization: Design and execute A/B and multivariate testing programs to improve engagement metrics and inform future campaign strategy. Use test learnings to develop and document best practices across the team.
- Lifecycle Marketing: Plan and manage personalized user journeys across email channels, partnering with creative, data, and account leads to align execution with broader campaign objectives.
- Platform Skills: Build, edit, schedule, and publish content across email, social, grassroots advocacy, CMS, and creative platforms. Maintain deep expertise in ESP and marketing automation platforms (e.g., Salesforce Marketing Cloud, Marketo, Braze, or similar), and proactively identify opportunities to leverage new capabilities on behalf of clients.
- Reporting: Drive digital reporting for email and broader digital channels. Identify patterns and trends, recommend next steps, and train junior staff on reporting strategy and analytics tools (e.g., Google Analytics, Tableau, or equivalent).
Collaboration
- Work collaboratively across communications, data, creative, and paid advertising teams; build strong relationships within Precision and with client stakeholders.
- Maintain campaign calendars, coordinate with cross\-functional teams, and ensure compliance with email marketing best practices and regulations.
- Well\-prepared for meetings—offers clear updates, anticipates questions, and proposes solutions and next steps.
- Guide junior staff by reviewing work and providing written and verbal feedback.
Project \& Time Management
- Own assigned tasks end\-to\-end: understand how work ladders up to larger goals, meet deadlines without reminders, organize documents for team sharing, and manage multiple projects simultaneously.
- Manage up when necessary to keep programs on track.
- Meets deadlines set by client lead.
Strategy
- Produce clear, persuasive content based on client strategy with minimal guidance; regularly seek feedback and review and edit junior team members' work.
- Identify potential issues or strategy gaps early, recommend adjustments, and support teams in course\-correcting.
- Contribute to strategic planning and goal\-setting for email and lifecycle marketing channels across the portfolio.
*What We Are Looking For:*
- 4\+ years of experience in email marketing or digital marketing, with a proven track record of building and scaling effective email programs.
- Deep expertise in ESP and marketing automation platforms — Salesforce Marketing Cloud, Marketo, Braze, Exact Target, or similar — required.
- Experience with CRM tools and systems leveraged to drive customer engagement and segmentation.
- Proficiency in digital analytics tools (e.g., Google Analytics, Tableau, or equivalent); strong analytical skills and a data\-driven approach to decision\-making.
- Working knowledge of HTML and CSS for email; ability to QA and troubleshoot templates.
- Experience building, executing, and scaling cross\-functional campaigns from concept to completion.
- Familiarity with customer segmentation, profiling, targeting, and lifecycle marketing best practices.
- Excellent written and verbal communications skills.
- Ability to work with colleagues across marketing, PR, analytics, and technology disciplines.
- Creative, energetic, detail\-oriented, and entrepreneurial.
*What We Will Give You:*
- Competitive salaries
- Annual bonus opportunity
- Flexible hybrid remote/in office plan
- Retirement plan with automatic 3% safe\-harbor contribution from Precision
- Healthcare coverage \- medical, dental and vision with 90% of costs covered by Precision
- Paid vacation and sick leave
- Paid parental leave
- Professional development stipend
The salary range for this role is $85,000\-105,000\.
We are currently working in a hybrid model and require staff to work in the office 50% of working days per month.
Precision is committed to building a diverse team that will positively and authentically impact the communities we serve. Centering our strategies around the authentic voices and cultures of the communities we are engaging with is paramount to our work – and our work can only be as inclusive as our team is representative. We strongly encourage women, Black, Latino, Hispanic, AAPI, and Indigenous people, LGBTQ\+, gender expansive or GNC folks, people of all ages, disabled people, and veterans to apply.
Salary Context
This $85K-$105K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Precision, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($95K) sits 43% below the category median. Disclosed range: $85K to $105K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Precision AI Hiring
Precision has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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