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About This Role
Company Description
Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes.
Strategy & Management Consulting
Sia’s Strategy & Management Consulting global footprint and expertise in more than 40 sectors and services allow us to enhance our clients' businesses worldwide. We guide their projects and initiatives in strategy, business transformation, IT & digital strategy.
A digital leader or growing disruptor, everyone must transform themselves to maintain their advantage. The Sia Technology Business Unit supports clients through digital, operations and organizational transformation adapted to their culture and working methods. We help clients strategize and scale leveraging deep expertise and solutions in compliance and risk management, strategic technology partnerships, data science, operations and business analysis and mergers and acquisitions.
Job Description
Location: Open to NYC, SF Bay Area, or Seattle candidates (Hybrid - 3 days onsite)
The Role
We are looking for a Senior Manager to help grow Sia’s Legal AI & Technology practice and lead transformation programs for corporate legal departments and law firms. You will advise legal leaders, shape technology decisions, and lead multidisciplinary teams from opportunity assessment through implementation and adoption.
The role combines client delivery, business development, and practice leadership. You will work closely with Sia’s legal operations, AI, data, and engineering specialists to improve legal service delivery, reduce manual work, and build capabilities clients can sustain.
Key responsibilities
Advise legal leaders and shape transformation programs
- Partner with General Counsel, Heads of Legal Operations, and technology leaders to assess workflows, systems, data, and organizational readiness for AI.
- Develop business cases, prioritized use cases, technology roadmaps, and operating models, including Legal AI Centers of Excellence.
- Shape initiatives across contracting, legal intake and triage, knowledge management, matter and spend management, litigation support, and legal reporting.
Lead legal AI and technology delivery
- Lead client engagements across workstreams, owning scope, staffing, budgets, delivery quality, and executive communications.
- Guide build-versus-buy decisions, vendor assessments, pilots, and implementation of legal AI platforms, contract lifecycle management systems, and custom AI agents.
- Translate legal requirements into workflows, product backlogs, contract playbooks, and acceptance criteria. Coordinate configuration, integrations, data migration, testing, and deployment with technical teams.
- Establish evaluation methods with legal reviewers to assess accuracy, completeness, source reliability, and business usefulness. Address confidentiality, privilege, access controls, and human oversight in solution design.
Drive adoption and demonstrate value
- Lead practical workshops, training, and change programs that help lawyers and legal operations teams use AI effectively in their daily work.
- Track adoption, quality, turnaround times, capacity gains, and cost savings. Use feedback and performance data to improve solutions and establish ongoing ownership and support.
Grow the practice and develop client relationships
- Identify and qualify opportunities, expand existing accounts, and lead discovery sessions, proposals, statements of work, and pricing with Sia leadership.
- Develop offerings, demonstrations, reusable delivery methods, and client credentials that turn project experience into repeatable services.
- Build relationships with legal technology providers and collaborate across Sia’s international teams. Contribute to campaigns, thought leadership, and industry events.
Build and lead teams
- Coach consultants and managers, oversee work quality, support recruitment, and develop the team’s legal technology and applied AI capabilities.
- Coordinate internal initiatives across legal operations and technical specialists, with clear priorities, owners, and deliverables.
Qualifications
- 8+ years of relevant experience in consulting, legal operations, legal technology, or technology transformation, including substantial work with legal departments or law firms.
- 3+ years leading teams or complex client programs, with accountability for delivery, senior stakeholder relationships, and team development.
- A track record of implementing legal technology or AI solutions through rollout and adoption, with measurable operational improvements.
- Strong understanding of legal workflows, particularly contracting and legal service delivery, and the ability to translate legal requirements into practical technology solutions.
- Practical experience evaluating and using generative AI tools, designing workflows, and assessing outputs. Working knowledge of AI agents, retrieval from enterprise knowledge sources, and integration considerations.
- Experience developing proposals, scoping engagements, and contributing to account growth or new business.
- Strong workshop facilitation, executive communication, and problem-solving skills, with the ability to connect legal, business, and technical teams.
- Bachelor’s degree in a relevant field or equivalent professional experience. A law degree is welcome but not required.
Preferred experience
- Experience with platforms such as Harvey, Ivo, Legora, or enterprise CLM solutions, alongside general-purpose AI tools such as ChatGPT, Claude, or Microsoft Copilot.
- Experience developing contract playbooks, legal knowledge repositories, AI evaluation frameworks, or Legal AI governance and operating models.
- Ability to prototype workflows using low-code tools or code, and familiarity with APIs, data models, and enterprise systems.
- An established network in legal operations or legal technology, and experience building offerings or participating in communities such as CLOC or ACC.
Why join Sia
Help shape a growing Legal AI & Technology practice, work directly with legal leaders on implementation and adoption, and collaborate with consulting, data, and engineering teams to bring new solutions to market.
Additional Information Compensation & Benefits
Sia invests in total rewards that support how our people work, live, and grow.
Compensation
- Base Salary: $180,000 - $210,000. Actual compensation within this range will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by applicable law.
- Annual Performance Bonus
- Personal Sales Bonus (uncapped)
Benefits
- Medical, dental, and vision; company-paid life and AD&D; voluntary supplemental insurance and EAP
- 401(k) with company match and immediate vesting; HSA and FSA options
- College savings and student loan repayment programs
- Paid parental leave; generous PTO, nine company holidays, and one floating holiday
- Cell phone stipend; well-being and professional development programs
Benefits are subject to applicable plan terms and eligibility requirements.
Workplace
Sia values in-person collaboration. When serving clients, consultants may be required to work onsite at a client location based on engagement needs. Between client engagements, consultants are expected to work from a Sia office or approved coworking location at least three days per week. Candidates should reside within a reasonable commuting distance of a Sia office or approved coworking location. Sia maintains offices and coworking arrangements across multiple U.S. markets, including New York City, Charlotte, Greater Seattle, San Francisco Greater Bay Area, Los Angeles, Houston, Atlanta, Baltimore, Chicago, Washington D.C.
Work Authorization
Applicants must be legally authorized to work in the United States at the time of application and throughout their employment. This position is not eligible for employment visa sponsorship now or in the future.
Our Commitment
At Sia, we believe in fostering a diverse, equitable, and inclusive culture where our employees and partners are valued and thrive in a sense of belonging.
Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.
Salary Context
This $180K-$210K 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 SIA, 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 ($195K) sits 8% below the category median. Disclosed range: $180K to $210K.
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.
SIA AI Hiring
SIA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $210K - $210K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above 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 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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