Interested in this AI/ML Engineer role at Epiq?
Apply Now →About This Role
At Epiq , your work contributes to complex, global legal outcomes. You’ll join a values‑driven community where integrity guides decisions, relentless service sets the bar, and we thrive on big challenges together. We invest in your growth with enterprise‑wide learning and mobility. We celebrate who you are, and we respect life beyond work with flexibility that’s recognized externally. Enabled by modern platforms and AI, you’ll do the most meaningful work of your career and see your impact at scale.
Job Description:
Epiq is seeking an experienced and visionary AI Senior Product Manager to lead the strategy, roadmap, and execution of our AI\-powered products. You will be responsible for identifying market opportunities, defining product requirements, and collaborating with cross\-functional teams to deliver innovative solutions that drive business value.
Job Responsibilities
- Define and articulate the product vision, strategy, and roadmap for AI\-driven products, aligning with overall company objectives.
- Conduct thorough market research, competitive analysis, and customer feedback analysis to identify unmet needs and opportunities for AI innovation.
- Translate business requirements and user needs into detailed product specifications, user stories, and use cases for AI models and features.
- Manage the entire product lifecycle from ideation and conceptualization to launch and post\-launch optimization.
- Collaborate closely with engineering, data science, design, and marketing teams throughout the product development process.
- Utilize Agile methodologies to guide product development, including backlog prioritization, sprint planning, and release management.
- Perform data analysis to understand product performance, identify areas for improvement, and inform future product decisions.
- Develop and maintain strong relationships with internal and external stakeholders through effective interpersonal communication and professional collaboration.
- Champion the voice of the customer and ensure that product development is customer\-centric.
- Identify and mitigate potential risks and challenges in product development and deployment.
Job Qualifications
- Bachelor's degree in Computer Science, Engineering, Business, or a related field; Master's degree preferred.
- 5\+ years of experience in product management, with at least 2 years focused on AI/ML products.
- Proven expertise in Agile Methodology for product development.
- Strong Analytical Thinking and problem\-solving skills, with the ability to translate complex data into actionable insights.
- Demonstrated proficiency in Business Analysis, including the ability to understand Business Processes and define clear Business Requirements.
- Experience with Data Analysis and leveraging data to drive product decisions.
- Exceptional Interpersonal Communication and presentation skills, with the ability to articulate complex technical concepts to non\-technical audiences.
- Solid understanding of Product Design principles and user experience best practices.
- Extensive experience with Product Development and the full product lifecycle.
- A track record of successful Product Management in a fast\-paced environment.
- Strong commitment to Professional Collaboration and Teamwork.
\#LI\-KS1 \#LI\-Remote
The Compensation range for this role is 115,000 to 185,000 USD annually and may be eligible for an annual bonus.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
Must be authorized to work in the United States for any employer.
Your specific salary will be determined based on several factors:
- Location\-based market rate for the role
- Your abilities in relation to the job specification
- Performance during screening and interview
- Pay parity with the wider team in the considered location
Further details about the package will be provided during the initial screening call with the Talent Acquisition Team.
Epiq Leadership Compass
Fosters Relationships \& Collaboration
Builds trust and alignment through open communication, shared goals, and strong partnerships to drive collective success.
- Build trust\-based partnerships
- Nurture long\-term relationships
- Remove collaboration barriers
- Celebrate cross\-team success
Engages \& Influences
Inspires action and alignment through clear communication, purposeful influence, and a compelling vision.
- Use storytelling to build buy\-in
- Align communication with organizational goals
- Guild alignment through strong engagement
Maximizes Performance
Sets and reinforces performance standards that drive results, ensure accountability, and align with Epiq’s goals.
- Use data to identify improvement opportunities
- Make informed decisions
- Align team goals with boarder strategy
- Empower teams to manage their own goals
- Translate vision into clear priorities
- Prepare for disruptions with strong change management
Achieves Operational Success
Drives continuous improvement and operational excellence through smart processes, data insights, and quality execution.
- Improve workflows for team efficiency
- Use clear documentation and expectations
- Resolve issues quickly using data and feedback
It is Epiq’s policy to comply with all applicable equal employment opportunity laws by making all employment decisions without unlawful regard or consideration of any individual’s race, religion, ethnicity, color, sex, sexual orientation, gender identity or expressions, transgender status, sexual and other reproductive health decisions, marital status, age, national origin, genetic information, ancestry, citizenship, physical or mental disability, veteran or family status or any other basis protected by applicable national, federal, state, provincial or local law. Epiq’s policy prohibits unlawful discrimination based on any of these impermissible bases, as well as any bases or grounds protected by applicable law in each jurisdiction. In addition Epiq will take affirmative action for minorities, women, covered veterans and individuals with disabilities. If you need assistance or an accommodation during the application process because of a disability, it is available upon request. Epiq is pleased to provide such assistance and no applicant will be penalized as a result of such a request. Pursuant to relevant law, where applicable, Epiq will consider for employment qualified applicants with arrest and conviction records.
Salary Context
This $115K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Epiq, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $115K to $185K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Epiq AI Hiring
Epiq has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $185K - $185K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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