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About This Role
Overview
Location: Boston or New York City
Practice: SPACE (Digital and Artificial Intelligence)
Reports To: Partner - Digital & AI Solutions, Americas
About the Role
Arthur D. Little (ADL) is seeking a Manager - Data Science & AI Consulting to join our SPACE team. This is a player-coach role for an experienced artificial intelligence practitioner who combines deep technical expertise with strong consulting, leadership, and interpersonal skills.
The successful candidate will work directly with clients to identify high-value opportunities for artificial intelligence, rapidly translate business problems into technical solutions, and lead multidisciplinary teams through implementation. They are expected to remain hands-on with technology - architecting solutions, prototyping, reviewing code, and solving difficult technical problems - while simultaneously managing teams and advising senior client stakeholders.
The role sits at the intersection of a forward-deployed artificial intelligence engineer, technical product leader, and management consultant: someone equally comfortable working alongside engineers and data scientists or leading a discussion with client executives. Why Join ADL
At Arthur D. Little, you will work with a global network of industry experts, data scientists, engineers, and strategists driving digital transformation across sectors. The SPACE team is at the forefront of shaping how artificial intelligence and data redefine value creation, combining ADL's strategic heritage with deep technical expertise and hands-on delivery.
This role offers the opportunity to help clients move beyond artificial intelligence strategy and experimentation to build solutions that deliver measurable business impact - while developing teams, shaping client relationships, and contributing to the growth of ADL's artificial intelligence capabilities in the Americas. Responsibilities
As a Manager at Arthur D. Little, you lead the successful accomplishment of our assignments. Therefore, you will:
- Lead end-to-end artificial intelligence and data science engagements - from problem framing and model design through prototyping, implementation, and delivery.
- Operate as a player-coach, combining project and team leadership with direct, hands-on contribution to solution design and development.
- Rapidly prototype and build artificial intelligence and machine learning solutions to demonstrate feasibility, validate value, and accelerate client adoption.
- Design solution architectures and make pragmatic technical choices across models, data, applications, and infrastructure.
- Apply artificial intelligence, machine learning, and optimization techniques to address complex business challenges.
- Work side-by-side with client technical teams, data scientists, engineers, and business leaders to move solutions from concept and prototype into production.
- Review code and technical designs, troubleshoot difficult implementation problems, and provide technical direction to delivery teams.
- Translate ambiguous client problems into well-defined technical approaches, balancing business value, technical feasibility, speed, and scalability.
- Partner with clients to define data-driven strategies, identify value-creation opportunities, and translate analytical insights into actionable recommendations.
- Manage, mentor, and develop multidisciplinary teams of internal and external data scientists, engineers, and consultants while remaining actively involved in delivery.
- Build trusted relationships with senior client stakeholders and communicate complex technical concepts through clear, business-relevant narratives.
- Support business development by identifying opportunities, shaping proposals, designing solutions, and leading client presentations.
- Contribute to thought leadership and intellectual capital within ADL's SPACE practice, particularly in artificial intelligence, data strategy, and automation.
- Collaborate closely with ADL's global industry practices, including Life Sciences, Automotive, Telecommunications, Media, and Consumer Goods, to embed artificial intelligence-driven value creation across sectors.
Qualifications
Required Skills and Experience
- Hands-on artificial intelligence practitioner: Demonstrated track record personally designing, coding, and deploying artificial intelligence and machine learning solutions. This role is not intended for candidates whose recent experience has been primarily managing technical teams.
- Artificial intelligence and machine learning: Deep practical experience across machine learning and modern generative artificial intelligence, including large language models, retrieval-augmented generation, agents, evaluation, and optimization.
- Artificial intelligence engineering: Ability to take solutions from experimentation and prototyping through production implementation, including architecture, integration, testing, monitoring, and performance improvement.
- Programming: Advanced Python skills and strong proficiency working with data and Structured Query Language. Candidates should be comfortable personally developing prototypes, reviewing production code, and troubleshooting technical issues.
- Modern artificial intelligence stack: Hands-on experience with relevant frameworks, model providers, knowledge systems, data platforms, and cloud environments. Experience may include PyTorch, scikit-learn, Hugging Face, LangChain, LlamaIndex, vector or graph databases, Databricks, Microsoft Azure, Amazon Web Services, or Google Cloud. Specific technologies are less important than demonstrated ability to learn and apply the right tools quickly.
- Knowledge systems: Practical understanding of retrieval-augmented generation, semantic search, vector databases, graph databases, and knowledge graph approaches.
- Software development: Strong understanding of software engineering principles, including version control, testing, code quality, application integration, and agile delivery methods.
- Client consulting: Strong ability to work directly with clients, structure ambiguous problems, facilitate working sessions, communicate with senior executives, and translate between business and technical stakeholders.
- Leadership: Demonstrated ability to lead multidisciplinary teams while remaining actively involved in delivery. Able to coach junior technical talent, raise the quality of their work, and create an effective collaborative environment.
- Commercial orientation: Ability to identify opportunities, shape solutions, contribute to proposals, and build trusted client relationships that lead to follow-on work.
- Consulting experience: Prior experience in management consulting, professional services, technology consulting, or another client-facing delivery environment is highly preferred.
- Industry background: Experience in Automotive, Telecommunications, Media, Consumer Goods, Life Sciences, or other relevant industries is advantageous.
Personal Attributes
- A value-oriented technologist who is motivated by solving business problems, not technology for its own sake.
- A people person with strong interpersonal skills who builds credibility quickly with clients and colleagues at all levels.
- Comfortable moving fluidly between executive conversations, team leadership, technical architecture, and hands-on development.
- A collaborative player-coach who enjoys developing others while remaining personally accountable for the quality of the work.
- Naturally curious and able to learn new technologies, industries, and problem domains quickly.
- A self-starter with entrepreneurial drive, resilience, sound judgment, and a bias toward action in ambiguous contexts.
- A clear communicator who can translate complex technical ideas into compelling, business-relevant insights and narratives.
Arthur D. Little is an equal-opportunity employer. We are committed to ensuring that no employee, contractor, vendor, or job applicant is discriminated against because of their race, color, nationality, ethnic or national origin, religion or belief, sex, marital or civil partner status, veteran status, sexual orientation, age, gender reassignment, or disability.
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 Arthur D. Little, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Arthur D. Little AI Hiring
Arthur D. Little has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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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