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About This Role
The Brattle Group, a privately held, global economics consulting firm, is looking for a Data and AI Engineer to join our Boston, MA office.
The Data \& AI Engineer is an early\-career role within Brattle’s Data \& AI Engineering team, focused on hands\-on technical work across client delivery, applied R\&D, and internal capability\-building. This role is designed for candidates who are strong builders, fast learners, and clear communicators, and who want to develop technical judgment in a setting where problems are varied, ambiguous, and often time\-sensitive.
This is not a narrowly defined pipeline role or a heads\-down engineering track. Data \& AI Engineers work across data engineering, applied analytics, machine learning, and AI\-enabled workflows, staying close to implementation while learning how technical choices affect real client work.
Candidates are not expected to arrive as experts across data engineering, solution architecture, and applied AI research. They are expected to be hands\-on, curious, coachable, and comfortable learning quickly.
Where This Role Sits at Brattle:
The Data \& AI Engineering team is a specialized technical group embedded within Brattle’s consulting staff. The team supports client work while also serving as an applied R\&D function for the firm: researching emerging technologies, prototyping new analytical and AI\-enabled workflows, and translating useful methods into reusable capabilities.
The team partners with economists, consultants, industry experts, and internal stakeholders to improve how the firm works with data, analytics, machine learning, and AI through project delivery, reusable tools, documentation, training, and knowledge\-sharing.
Data \& AI Engineers work under the guidance of more experienced technical leads, including Senior Data \& AI Engineers, Solutions Architects, and Research Engineers. As experience grows, the role can develop toward deeper execution ownership, increased responsibility for solution design, more advanced AI and research work, or some combination of those paths.
Nature of the Work:
Data \& AI Engineers work on problems where the technical path is often unclear, the data is imperfect, and the constraints are real. Source materials may be incomplete, inconsistent, degraded through prior systems, or difficult to interpret. Some matters also involve restricted or confidential workflows that shape how data can be accessed, handled, or shared.
The role requires practical judgment. There may be multiple valid approaches, each with trade\-offs, and limited ability to go back to the source for clarification. Data \& AI Engineers are expected to reason carefully from imperfect inputs, document assumptions, surface limitations, and help build solutions that are defensible, reproducible, timely, and fit for purpose.
Project timelines may shift quickly based on external events, negotiations, litigation deadlines, or client needs. Some work is recurring and operational; some is one\-off or exploratory. The right candidate is a resourceful, practical builder: someone who can stay organized, adapt to changing constraints, and make progress without losing rigor or composure.
The team environment is academic, collegial, grounded, and highly collaborative. We value curiosity, humility, initiative, clear communication, and practical intelligence. The work can be technically advanced, but the culture is not ego\-driven. We are looking for people who are resourceful, friendly, proactive, flexible, and able to work well with others in a global, connected consulting environment.
Some of the day\-to\-day responsibilities of this role include:
- Prepare, inspect, clean, reconstruct, and validate data from a wide range of sources, including structured datasets, documents, reports, exports, PDFs, scans, and other formats that may not have been created for analysis
- Use Python, SQL, notebooks, version control, and related tools to build reproducible workflows, test assumptions, troubleshoot issues, and document how work was performed
- Identify data limitations, quality issues, assumptions, blockers, and open questions early
- Support applied analytics, machine learning, and AI\-enabled workflows where they are useful to the problem
- Contribute to workflows involving text extraction, classification, summarization, embeddings, retrieval\-augmented generation, model evaluation, automation, visualization, or rapid prototyping
- Use AI tools thoughtfully to accelerate learning and execution while maintaining responsibility for accuracy, confidentiality, defensibility, and quality
- Support applied R\&D by helping prototype, test, and evaluate new tools, methods, and workflows before they are adopted more broadly
- Communicate progress, technical findings, assumptions, limitations, and trade\-offs clearly to consultants, economists, technical peers, and other stakeholders
- Participate in code review, collaborative problem solving, documentation, and iterative refinement of work products
- Help turn lessons from project work and applied R\&D into reusable team assets, examples, templates, documentation, and training materials
You will not be expected to own full workstreams on day one, but you will be expected to learn quickly, take initiative, and stretch beyond a narrow technical lane.
THE CANDIDATE
- Bachelor’s degree in Computer Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Economics with strong technical coursework, or a related field
- Equivalent hands\-on technical experience, internships, research work, or project\-based experience may also be considered
- 0\-3 years of professional experience in data engineering, analytics, applied AI, machine learning, software development, research, or related technical work
- Demonstrated interest in using AI tools, machine learning methods, or automation to solve practical problems, with willingness to learn how to evaluate those tools responsibly
- Comfort working in ambiguous problem spaces where the task may need to be clarified, decomposed, and revised as new information emerges
- Strong foundation in Python for data analysis, scripting, automation, or prototyping, with exposure to libraries such as pandas, NumPy, scikit\-learn, or comparable tools
- Working knowledge of SQL and relational data concepts, including joins, aggregation, filtering, and practical data exploration
- Foundational understanding of statistics, data analysis, machine learning, or experimental evaluation, with interest in strengthening applied judgment over time
- Exposure to generative AI workflows, such as prompt design, embeddings, vector search, retrieval\-augmented generation, summarization, classification, or model evaluation
- Ability to work with structured, semi\-structured, and unstructured data, including text\-heavy documents or heterogeneous data sources
- Familiarity with software development practices such as Git, notebooks, code review, documentation, testing, and reproducible workflows
- Familiarity with cloud platforms such as Azure or comparable environments is helpful but not required
- Ability to learn new tools quickly and use AI\-assisted development responsibly without treating generated output as automatically correct
- Strong written and verbal communication skills, including the ability to explain technical work, assumptions, limitations, and next steps clearly
- Ability to manage multiple parallel workstreams in a fast\-paced environment
- Flexible mindset to adapt to changing project priorities and client needs
*Brattle offers a competitive benefits package, base salary, and bonus program for eligible roles based on individual and firm performance. The anticipated base gross salary range for this position in Boston, MA is $105,000 \- $115,000 annually. Actual salary will depend on a variety of factors, including experience and training.*
*This position is not eligible for immigration sponsorship.*
EQUAL OPPORTUNITY
The Brattle Group is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, citizenship status, national origin, ancestry, sex, gender identity and expression, age, height, weight, domestic partner status, Acquired Immune Deficiency Syndrome or HIV status (AIDS/HIV status), genetic information, sexual orientation, disability (where the applicant or employee is qualified to perform the essential functions of the job with or without reasonable accommodation), marital status, veteran status, political affiliation, drug or alcohol abuse or alcoholism, or any other characteristic protected under applicable law.
We encourage all applicants to click here to review our full Equal Employer Opportunity Statement.
THE EMPLOYER
The Brattle Group answers complex economic, finance, and regulatory questions for corporations, law firms, and governments around the world. We are distinguished by the clarity of our insights and the credibility of our experts, which include leading international academics and industry specialists. Brattle has 500 talented professionals across North America, Europe, and Asia\-Pacific. For more information, please visit brattle.com.
Salary Context
This $105K-$115K range is in the lower quartile 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 The Brattle Group, 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 $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 ($110K) sits 50% below the category median. Disclosed range: $105K to $115K.
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.
The Brattle Group, Inc. AI Hiring
The Brattle Group, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $115K - $115K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national 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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