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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 AI Labs is the innovation and engineering hub behind Epiq’s next\-generation AI platform for corporate legal departments and global law firms. Operating with the speed and autonomy of a startup and the resources of a global alternative legal services provider, the team builds intelligent agents, reasoning engines, knowledge systems, and structured workflows for litigation, investigations, compliance, and corporate knowledge work.
The team is highly collaborative, deeply technical, and focused on rapid iteration, thoughtful design, and end\-to\-end ownership.
The Opportunity
You will build the agentic systems at the center of Epiq AI Labs’ legal AI platform, including the orchestration runtime used by other teams, the retrieval layer agents reason over, and the evaluation infrastructure used to measure quality. You will own systems from initial design through production operation.
Legal workflows require a high standard of correctness, traceability, confidentiality, and tenant isolation. You will establish the primitives for orchestration, grounded retrieval, and measurable quality that other engineering teams will build on.
Essential Job Responsibilities
- Design the agentic platform used by other teams, including orchestration, tool registration and permissioning, durable state and memory, sandboxing, and trace\-level observability.
- Build agent workflows with multi\-step reasoning, tool use, human\-in\-the\-loop checkpoints, and recovery from partial failure during long\-running execution.
- Evaluate developments in model capabilities, agent\-design patterns, and evaluation practices, and translate them into platform architecture.
- Design retrieval\-augmented generation systems with hybrid search over legal corpora and citation grounding to specific sources.
- Build evaluation infrastructure, including golden datasets, regression harnesses, and offline and online evaluation loops.
- Establish model\-operations practices for routing, latency and cost budgets, prompt and context versioning, caching, and production reliability.
- Build scalable backend services and APIs; produce technical design documentation; partner with product, research, security, and legal domain experts; and strengthen engineering standards.
Required Qualifications
- 5\+ years of software\-engineering experience with demonstrated depth in backend, distributed, or AI systems.
- Demonstrated experience delivering and operating LLM\-based applications in production at scale, beyond prototypes or proofs of concept.
- Demonstrated experience with agentic architectures and orchestration frameworks.
- Demonstrated experience with retrieval\-augmented generation systems at scale.
- Strong production\-level proficiency in Python.
- Demonstrated experience evaluating agentic\-system accuracy and performance.
- Experience designing services and APIs consumed by other engineering teams.
- Experience with observability tooling such as Prometheus, Grafana, or OpenTelemetry.
- Experience operating in containerized cloud environments.
- Strong system\-design and architecture experience, including production of technical design documents.
- Excellent written and verbal communication, including the ability to work effectively with non\-technical domain experts and stakeholders.
- Demonstrated proficiency using AI tools in software development.
Preferred Qualifications
- Experience with fine\-tuning, model adaptation, or systematic evaluation across model families.
- Experience with large\-scale data pipelines or document\-processing systems.
- Experience with multi\-tenant SaaS architectures and data\-isolation requirements.
- Experience in a startup or scale\-up engineering environment.
- Experience in legal technology, enterprise workflow, or knowledge\-management systems.
Technology Stack
· Python · LLM APIs and agent frameworks · Qdrant · Solr · PostgreSQL · RabbitMQ · Azure · Kubernetes · Docker · Prometheus · Grafana · OpenTelemetry.
\#LI\-KS1
The Compensation range for this role is $195,000 \-$255,000 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 $195K-$255K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($225K) sits 5% above the category median. Disclosed range: $195K to $255K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Epiq AI Hiring
Epiq has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in New York, NY, US. Compensation range: $195K - $255K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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