Jobs in the United States

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Added 1 min ago
Civil Structural Engineer
GPAC
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Senior LLM Engineer, R&D
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Project Engineer, Chassis Electric Routing - TRATON R&D US, LLC
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Seasonal Customer Service Representative - The Oaks
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Engineer IV
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Systems Engineering - Early Career
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Senior Process Engineer
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Added 3 mins ago
Automation Principal Engineer
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Added 4 mins ago
Software Development Engineer, RBKS Platform Ops
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Added 4 mins ago
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Radar Modes Lead Engineer
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Torrance, CA
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Hardware Development Engineer II, AWS Mainstream Compute
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Added 4 mins ago
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Software Engineer 3
Actalent
Irvine, CA
- $55 to $63 USD / hour
Software Engineer Android Platform & Embedded SystemsShape the Future of Connected Aviation Technology

Software Engineer, AI Agents

NY, United States

$180,000 to $260,000 USD / year

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Job details

Pay: $180,000 to $260,000 USD / year
Experience: 3+ years of professional software-engineering experience with demonstrated depth in backend, distributed, or AI systems.
Posting language: English
Posted: Nov 6, 2026

Original job description

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.

We are fast, experimental, and product-obsessed, with high autonomy, high trust, and low bureaucracy. We ship to real legal clients and hold a high bar for correctness, and we use AI tools throughout our own development workflow. The team is deeply technical, highly collaborative, and owns its systems end to end - you design it, you build it, you ship it.

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: orchestration of single agents and parallel sub-agents, durable state and memory across long-running jobs, MCP-based tool integration, and trace-level observability.
Build the safety layer agents run inside: tool registration and permissioning, sandboxing, guardrails and output validation, and defenses against prompt injection from retrieved content.
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.
Own and evolve the in-house model services behind classification, extraction, and embeddings - including customer-trained custom models with active learning - and integrate them alongside frontier models.
Build model-level and end-to-end evaluation datasets, benchmarks, and regression checks for classification, extraction, retrieval, and agent workflows. Work with legal domain experts to turn real workflows, corrections, and failure cases into quality improvements, with automated evaluation in CI and monitoring of quality, latency, and cost in production.
Establish LLMOps practices for running models reliably in production: model routing and fallbacks, latency and cost budgets, prompt and context versioning, caching, and quality and reliability monitoring.
Use AI coding agents throughout development while owning the outcome - the design, specifications and test plans, code review, and verification of correctness, security, and maintainability - and ship major changes behind automated tests and controlled rollouts, with product and legal-domain validation where appropriate.
Build scalable backend services and APIs, produce technical design documentation, and partner with product, research, and security stakeholders.
Participate in the on-call rotation; diagnose and resolve production incidents affecting model and agent services, and turn findings into durable fixes.
Senior+ level: Define the foundational architecture for running LLM-powered applications and agents reliably in production across teams, and lead the most complex cross-cutting initiatives, including migrations and addressing architectural debt and systemic risks.
Senior+ level: Raise the engineering bar by promoting best engineering practice, building reusable platform primitives, mentoring engineers, and partnering with product and engineering leaders on technical direction while staying hands-on and accountable for delivering working software.

Required Qualifications

3+ years of professional software-engineering experience with demonstrated depth in backend, distributed, or AI systems. Hiring level will reflect demonstrated technical depth, scope of ownership, and impact.
Experience delivering and operating LLM-powered applications in production at scale - beyond prototypes or proofs of concept - including evaluating quality and diagnosing failures.
Experience building tool-using agent workflows or retrieval-augmented generation systems.
Strong production-level proficiency in Python.
Working understanding of the ML lifecycle, including evaluation data, model and dataset versioning, production deployment, and monitoring model quality, with the ability to collaborate with researchers on reliable model updates.
Experience designing evaluation datasets and benchmarks and measuring agent task completion, citation correctness, policy adherence, latency, and cost.
Experience designing services and APIs consumed by other engineering teams.
Experience with observability tooling such as Prometheus, Grafana, or OpenTelemetry.
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 coding agents in day-to-day development, with sound judgment about where they accelerate work and where they fail.
Self-motivated, with high ownership: you solve problems end to end, actively keep up with new developments in AI, and quickly learn and apply new technologies and approaches to your work, including areas you haven't worked in before.
Senior+ level: Demonstrated technical leadership at the corresponding level, with a track record of delivering complex distributed systems across multiple teams, and deep expertise in Python, distributed-systems fundamentals, and event-driven architectures.
Senior+ level: Proven ability to lead technical direction across organizational boundaries without direct authority, and to communicate architectural trade-offs to technical and non-technical audiences.

Preferred Qualifications

Experience training, fine-tuning, or adapting models to domain-specific tasks, including classification and extraction models and active-learning loops.
Experience building or contributing to public or domain-specific benchmarks for LLMs or agents.
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.

Candidates who meet most, but not all, of the qualifications above are encouraged to apply. # LI-KS1 #1100 Epiq eDiscovery Solutions, Inc. Technology Stack Python · LLM APIs and agent frameworks (Anthropic via Amazon Bedrock, Azure OpenAI, in-house models) · Model Context Protocol (MCP) · Qdrant · Solr · PostgreSQL · RabbitMQ · Azure · Kubernetes · Docker · Prometheus · Grafana · OpenTelemetry

The Compensation range for this role is $180,000 -$260,000 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.

Click here to learn about Epiq's Benefits.

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.

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