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Usability Engineer Jobs (NOW HIRING)

ManTech

Forward Deployed Engineer

Washington, DC · On-site +1

$141K - $236K/yr

The Forward Deployed Engineer (FDE) will leverage their strong technical background and knowledge to support the Sponsor's system accreditation efforts, to include creating Body of Evidence (BOE ...

Heartflow

Staff Human Factors Engineer

San Francisco, CA

$190K - $250K/yr

We are seeking an experienced Staff Human Factors Engineer to lead usability engineering across our AI-driven medical device portfolio. Reporting to the Sr. Director of Systems Engineering, you will ...

Adobe

Machine Learning Engineer

San Jose, CA

Our Reliability Engineering & Operational Intelligence (REOI) team is building AJO's autonomous operating system - an AI-native platform that proactively improves product quality, accelerates issue ...

Stratum Networks

$126K - $157K/yr

Usability/Human Factors Engineer -Level 2 BlueHalo PRIME contract is seeking a Usability/Human Factors Engineer 2 to join the software development team who will develop and sustain integrated ...

Booz Allen Hamilton, Inc.

Agentic AI Engineer

Mclean, VA · On-site

$77K - $176K/yr

Share Agentic AI Engineer The Opportunity: As an experienced engineer, you know how to design, develop, and deliver production-grade agentic AI systems that demonstrate the practical value of ...

Istari Digital

Forward Deployed Engineer

Huntsville, AL · On-site

$143K - $165K/yr

As a Forward Deployed Engineer, you'll be embedded directly inside our customer organizations, operating as part of their team while remaining tightly connected to Istari's product and engineering ...

Istari Digital

Forward Deployed Engineer

Dayton, OH · On-site

$143K - $165K/yr

As a Forward Deployed Engineer, you'll be embedded directly inside our customer organizations, operating as part of their team while remaining tightly connected to Istari's product and engineering ...

Imh

The Advanced Care Technology Research and development (R&D) engineer role supports the advanced care technology and visualization engineering department. An enterprise wide team providing services ...

QTech US Inc

Agentic AI Engineer

Burlington, MA

Role: Agentic AI Engineer with Dynatrace exposure Work Location & Reporting Address: Burlington/Boston, MA / Princeton, NJ (Onsite) Job Type: Contact (6-12 months) Job Details: Role: Agentic AI ...

New

BC Forward

AI Engineer - Prompt 1

Redmond, WA · Remote

$55/hr

Applications Dev & Test - AI Engineer - Prompt 1 Location: Remote Must be available 10:00 AM - 4:00 PM PST. Pay Range: $55hr (W2) Job ID: 373419 About BCforward BCforward is a leading global IT ...

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Usability Engineer information

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$81K

$106.4K

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How much do usability engineer jobs pay per year?

As of Jun 20, 2026, the average yearly pay for usability engineer in the United States is $106,377.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,500.00 and $100,000.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior-level engineers in specialized fields such as software engineering, data engineering, or systems architecture can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries like technology or finance. These roles often require advanced certifications, leadership responsibilities, and proficiency with complex tools and systems.

What does an usability engineer do?

A usability engineer designs and evaluates products or systems to ensure they are user-friendly and efficient. They conduct user research, create prototypes, and perform testing using tools like usability testing software to improve user experience and interface design.

What are the key skills and qualifications needed to thrive in the Usability Engineer position, and why are they important?

To thrive as a Usability Engineer, you need strong analytical abilities, experience in user experience (UX) design, and a solid understanding of human-computer interaction, typically supported by a relevant degree in Human Factors, Psychology, or Design. Familiarity with prototyping tools (such as Figma or Axure), usability testing platforms, and UX research methodologies is frequently required, and certifications like Certified Usability Analyst (CUA) can be advantageous. Excellent communication skills, empathy, and attention to detail are crucial soft skills that distinguish top performers in this field. These competencies enable Usability Engineers to design intuitive products, effectively translate user feedback, and collaborate efficiently with cross-functional teams.

What are some typical challenges faced by Usability Engineers in their daily work?

Usability Engineers often encounter challenges such as balancing user needs with business or technical constraints, advocating for usability improvements within multidisciplinary teams, and effectively synthesizing diverse user feedback into actionable design changes. They may need to facilitate testing sessions, interpret large sets of qualitative and quantitative data, and prioritize recommendations under tight deadlines. Navigating these challenges requires strong problem-solving skills and the ability to communicate the value of usability to stakeholders. By addressing these obstacles successfully, Usability Engineers play a crucial role in shaping user-friendly products and enhancing overall customer satisfaction.

What jobs will boom in 2026?

Usability engineering is expected to grow as companies increasingly focus on user experience, with demand for professionals skilled in user research, interface design, and usability testing rising. Growth will be driven by advancements in technology, digital transformation, and the need for accessible, user-friendly products across industries. Staying current with tools like prototyping software and gaining certifications in UX or usability can enhance job prospects.

What is a Usability Engineer job?

A Usability Engineer ensures that products, such as websites, software, and applications, are user-friendly, efficient, and accessible. They conduct user research, design usability tests, analyze user behavior, and provide recommendations to improve the overall user experience. Their role often involves working closely with designers, developers, and product teams to create intuitive and effective interfaces. By applying usability principles, they help reduce user frustration and enhance product adoption.

How much does a usability engineer make?

The average salary for a usability engineer typically ranges from $70,000 to $120,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in user research, prototyping, or usability testing can earn higher salaries, often exceeding $130,000 per year.
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Usability Engineer jobs near you
Infographic showing various Usability Engineer job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 78% Full Time, 19% Part Time, and 2% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $106,377 per year, or $51.1 per hour.
AI Governance & Explainability Engineer

AI Governance & Explainability Engineer

Arbitration Forums Inc.

Tampa, FL • Remote

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

DEPARTMENT: Data Insights and Innovation

JOB TITLE: AI Governance & Explainability Engineer

JOB CODE: AIGEE

REPORTS TO: Data Governance Lead

FLSA STATUS: Exempt

EMPLOYMENT TYPE: Full-Time


JOB PURPOSE:

This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion).

The AI Governance & Explainability Engineer is a hands‑on technical role within the Data Governance team responsible for ensuring AI, GenAI, and Agentic AI solutions are explainable, governable, auditable, and production‑ready.

This role embeds governance directly into the AI technology stack, translating policies, regulatory expectations, and risk requirements into technical controls, automated checks, standardized artifacts, and release gates across the AI lifecycle.

The role combines AI/ML engineering depth, GenAI & Agentic AI design knowledge, and governance discipline to ensure AI solutions deliver explainability, can be trusted, defended, and audited in production, particularly within the Microsoft Fabric and Purview ecosystem.


DEPARTMENTAL EXPECTATION OF EMPLOYEE


Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model

  • Acts as a role model within and outside AF.
  • Performs duties as workload necessitates.
  • Maintains a positive and respectful attitude.
  • Communicates regularly with the departmental leader about department issues.
  • Demonstrates flexible and efficient time management and ability to prioritize workload.
  • Consistently reports to work on time, prepared to perform duties of the position.
  • Meets Department productivity standards.


ESSENTIAL DUTIES AND RESPONSIBILITIES

  • AI Governance by Design Engineering (Execution Focus not Policy writing)
    • Embed governance, explainability, and risk controls directly into AI, GenAI, and Agentic AI workflows
    • Translate enterprise AI policies, standards, and Responsible AI principles into:
      • Technical guardrails
      • Automated checks
      • Required evidence artifacts
      • CI/CD release gates
    • Implement governance as code and automation, eliminating reliance on manual or after-the-fact reviews.
  • AI Governance, Explainability & Human Oversight
    • Advise solution teams on explainability requirements for automated, semi-automated, and decision-support AI systems.
    • Ensure human-in-the-loop (HITL) controls are implemented where required by risk level or use case.
    • Define, generate, and manage explainability outputs that are:
      • Appropriate to the end-user or reviewer persona
      • Aligned to the decision context and operational use
    • Document explainability assumptions, limitations, and residual risk as governance evidence.
  • Metadata, Lineage & Governance Evidence Management
    • Operationalize AI Governance in Microsoft Purview by registering and maintaining:
      • AI models, features, prompts, agents, notebooks, and pipelines
    • Maintain end to end lineage across:
      • Data → features → models → inferences → outputs
      • Apply ownership, stewardship, sensitivity, and classification metadata.
    • Ensure governance is maintained:
      • Discoverable
      • Versioned
      • Traceable
      • Audit-defensible
  • GenAI & Agentic AI Governance Enablement
    • Apply governance patterns to LLMs, RAG, and Agentic AI solutions
    • Ensure governance traceability when synthetic data or augmented data is used for training, testing, or evaluation.


  • Implement Agentic AI lifecycle governance, including:
    • Observability of agent actions, deviations, and failures
    • Oversight of planning, reflection, and tool-use behavior
    • Controls on autonomous vs. constrained operation
  • Enable GenAI explainability, including:
    • Retrieval transparency for RAG (sources, relevance)
    • Inference context documentation
    • Decision trace generation where applicable
  • Explainability, Interpretability & Model Risk Controls
    • Own and operate explainability capabilities used for governance, audit, and trust.
    • Implement and operationalize techniques such as:
      • Feature attribution (e.g., SHAP or equivalent)
      • Driver and proxy detection
      • Global and local model explanations
    • Identify bias signals, risk indicators, and explainability gaps.
    • Store and manage explainability and observability outputs as governed, audit-ready artifacts.
    • Support audit, compliance, and risk review activities with defensible evidence.
  • Monitoring, Observability & Incident Readiness
    • Define and implement AI monitoring metrics, alerts, and thresholds for:
      • Performance degradation
      • Bias and ethical risk indicators
      • Drift and instability
    • Partner with MLOps and platform teams to integrate monitoring into production pipelines.
    • Support AI incident response and post-incident reviews with governance evidence.
    • Ensure all observability outputs are retained, traceable, and audit‑ready.
  • Governance Checkpoints & Release Gating
    • Define and enforce governance checkpoints within CI/CD pipelines (DEV-> TEST/UAT -> PROD).
    • Implement automated release checks for:
      • Required documentation and evidence artifacts
      • Explainability artifacts
      • Monitoring configuration
      • Data usage, lineage completeness, and medallion-layer alignment
    • Partner with Engineering and MLOps teams on promotion decisions while owning governance readiness, not platform approval.

QUALIFICATIONS

Required Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related field.
  • Minimum 7 years of experience in AI/ML engineering, data science, GenAI/LLMs, NLP, Agentic AI, data governance, or related roles.
  • Demonstrated experience operationalizing AI governance, explainability, and risk controls in production environments.
  • Deep understanding of Agentic AI architectures and lifecycle considerations.

Technical Skills

  • Strong proficiency in Python with hands‑on experience in AI/ML engineering workflows.
  • Working knowledge of Microsoft Fabric (Lakehouse, OneLake, notebooks, pipelines).
  • Experience with Microsoft Purview (catalog, lineage, classification, ownership).

Experience with AI/ML and GenAI tooling, including:

  • Azure AI Foundry / Azure ML
  • ML explainability libraries (e.g., SHAP)
  • LLMs, RAG architecture, and prompt engineering
  • Familiarity with Agentic AI frameworks and patterns (e.g., tool use, planning, reflection).
  • Experience integrating governance controls into CI/CD pipelines using GitHub or Azure DevOps.
  • Understanding of cloud platforms (Azure preferred; AWS/GCP a plus
  • Experience producing audit‑ready technical documentation and evidence artifacts.
  • Familiarity with reporting and visualization tools (e.g., Power BI) for governance and monitoring views.

Soft Skills

  • Strong analytical and problem‑solving abilities, particularly in risk‑based decision‑making.
  • Excellent written and verbal communication skills, with the ability to translate technical details into governance‑relevant insights.
  • Ability to lead governance execution initiatives and influence cross‑functional teams without direct authority.
  • Strong organizational skills with attention to detail and audit readiness.
  • Auto insurance or claims industry experience preferred.

Preferred Qualifications

  • Experience evaluating or governing model training approaches (e.g., NLP, generative models) without owning full training pipelines.
  • Familiarity with synthetic data governance (generation methods, limitations, risk documentation).
  • Experience with additional AI platforms (Databricks AI, Snowflake Cortex, Dataiku).
  • Experience in regulated industries (insurance, financial services, healthcare).


AMERICANS WITH DISABILITY SPECIFICATIONS


PHYSICAL DEMANDS

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.

While performing the duties of this job, the employee is occasionally required to stand; walk; sit; use hands to finger, handle, or feel objects, tools, or controls; reach with hands and arms; climb stairs; balance; stoop, kneel, crouch, or crawl; talk or hear; taste or smell. The employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities required by the job include close vision, distance vision, color vision, peripheral vision, depth perception, and the ability to adjust focus.


WORK ENVIRONMENT [Standard language tied to each job description]

This is a fully remote position requiring reliable high-speed internet access and a dedicated workspace.

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.