Interested in this AI/ML Engineer role at Bates White Economic Consulting?
Apply Now →Skills & Technologies
About This Role
Firm overview
Bates White is a boutique consulting firm based in Washington, DC. Recognized as a top workplace, the firm provides advanced economic, financial, and econometric analysis to law firms, companies, and government agencies.
Through our supportive, collaborative, and collegial culture, we invest in our talent and provide opportunities for career advancement. We are proud to have been consistently ranked among the top firms in the Vault Guide to the Top 50 Consulting Firms, listed as a top consulting firm by Management Consulted and named one of the Top Workplaces in the Washington, DC area for the 11th consecutive year by WTOP News.
If you are looking for a place to do high-quality work and have fun along the way, read below to discover how you can be part of our team. Learn more about our firm at: www.bateswhite.com.
What you’ll do In this role you will design, develop and deploy AI-driven workflows and autonomous agents that leverage large language models (LLMs) and other AI systems across the firm’s enterprise AI platforms. You will:
- Design, build, and deploy AI workflows and agentic solutions that leverage LLMs and other AI systems, using the firm’s existing enterprise AI platforms (such as Azure AI Foundry, Azure OpenAI, Databricks, AWS (e.g., Bedrock, SageMaker AI) and Google Cloud Vertex AI / Gemini), and evaluate and onboard new platforms as needs evolve.
- Develop, orchestrate, and maintain multi-step agent workflows, including tool use, function calling, retrieval-augmented generation (RAG), and integration with enterprise data sources, applications, and APIs.
- Apply a thorough understanding of how LLMs and emerging AI systems work, covering model selection, prompt design, context management, embeddings and vector stores, and the tradeoffs among inference, retrieval, and fine-tuning to select the right approach for each use case.
- Partner with business stakeholders and firm leadership to translate business requirements into robust, production-grade AI solutions.
- Build reusable frameworks, components, and pipelines that enable the broader data engineering team to develop, test, and scale AI solutions efficiently across cloud platforms.
- Establish and apply responsible AI practices, including data privacy and security, prompt injection and abuse mitigation, and evaluation and guardrails, in alignment with the firm’s AI governance policies and client contractual obligations.
- Monitor, evaluate, and continuously improve the accuracy, performance, cost, and reliability of deployed AI systems, including through token usage optimization.
- Build and maintain CI/CD pipelines and automated testing for AI workflows and agents, and manage the model lifecycle including version upgrades, deprecations, and migrations as providers release new models.
- Extend AI solutions to multi-modal use cases as needed, incorporating vision, audio, or other modalities alongside text-based LLMs.
- Stay current with the rapidly evolving AI landscape and advise the team on emerging models, tools, and techniques and their potential applications, including alternatives to the firm’s primary Azure-based stack (e.g., AWS, Google Cloud/Gemini).
What you’ll bring to the table
- Bachelor’s degree in computer science, data science, engineering, or a related field required; advanced degree preferred.
- Minimum 7 years’ experience in software engineering, data engineering, or a closely related technical field.
- Minimum 3 years’ hands-on experience building AI solutions, specifically with large language models (LLMs) and agent-based systems.
- Thorough understanding of LLM architecture, capabilities, and limitations, with the ability to reason about and adopt new AI systems as they emerge.
- Proficiency in Python and modern AI/ML frameworks and libraries; familiarity with multi-modal models (e.g., vision, audio) is a plus.
- Demonstrated experience building agentic workflows, RAG pipelines, and LLM integrations using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or comparable tooling.
- Experience with enterprise AI platforms such as Azure AI Foundry, Azure OpenAI, or Databricks or comparable platforms on AWS (e.g. Bedrock, SageMaker AI) or Google Cloud (e.g. Vertex AI, Gemini).
- Experience working with vector databases and embedding models.
- Proficiency in prompt engineering, model evaluation, and the use of guardrail and safety tooling.
- Proficiency working with REST APIs and integrating AI systems into enterprise applications and data pipelines.
- Familiarity with MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, LangSmith) for experiment tracking and model monitoring.
- Familiarity with Microsoft Azure and other major cloud providers (AWS, Google Cloud) and cloud-based data services. The firm is Azure-focused but open to experience with other platforms.
- Familiarity with data privacy, security, and responsible-AI considerations for enterprise AI systems is preferred.
- Experience with SQL and working with both structured and unstructured data sources are advantageous.
- Strong problem-solving and analytical skills.
- Ability to work effectively under dynamic circumstances, tight deadlines, and high-pressure situations.
- Ability to successfully work with individuals of varying backgrounds, levels, and departments.
- Excellent oral and written communication skills and ability to effectively communicate complex technical and AI concepts to diverse audiences.
- May require more than 40.0 hours per week to perform the essential duties of the position.
What you can expect from us We are committed to providing an exceptional employee experience. You can expect:
- Competitive compensation—the salary range for this position is $160,000 to $190,000. This position is also eligible for bonus compensation on a discretionary basis. The actual salary offered for this position will be determined based on job-related, non-discriminatory factors including qualifications and experience, education, external market data, and internal equity.
Comprehensive benefits package—includes tuition reimbursement up to $75K, low healthcare premiums, wellness benefits, and more!*
- Hybrid work environment with three coordinated in-office days per week.
- Open culture where your voice is heard, your input is sought, and your contributions are rewarded.
- Fun and engaging culture including frequent social events.
- Amenities that include a fitness center, rooftop terrace, standing desks, espresso, fresh fruit, breakfast and afternoon snack, billiards, and ping pong.
- Employee-driven community outreach program featuring fundraising events (e.g., trivia, game shows, cooking competitions, etc.), volunteer opportunities, and matching funds along with our pro bono program.
- Investment in your career through training programs, an assigned mentor and peer coach, and frequent feedback.
- Networking opportunities through employee interest groups, Women’s Network, International Network, Diversity-Inclusion Council, and BWProud Network.
If you are interested in joining our team, please submit a resume.
Bates White is an equal opportunity employer and does not discriminate based upon race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, protected veteran status, status as a qualified individual with a disability, or any other characteristic protected by law. If you are an individual with a disability and you need an accommodation or other assistance during the application process, please call our Human Resources department at (202) 408-6110 or email your request to [email protected]*. All qualified applicants are encouraged to apply. Download the* Know your Rights Poster for more information. #LI-Hybrid
Salary Context
This $160K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Bates White Economic Consulting, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Bates White Economic Consulting AI Hiring
Bates White Economic Consulting has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, Washington, DC. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.