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Receptionist Consultant Jobs (NOW HIRING)

Sully.ai

Applied AI Engineer

Santa Clara, CA · On-site

Scribes, Receptionists, Coders, Consultants and other agents that operate inside real clinical workflows. We are backed by Amity Ventures, YC, Baidu Ventures amongst others, have raised $40M+, and ...

Pyramid Consulting

Receptionist

Slater, IA · On-site

$16 - $16.50/hr

Immediate need for a talented Receptionist . This is a 06+months contract opportunity with ... Pyramid Consulting, Inc. provides equal employment opportunities to all employees and applicants ...

Pyramid Consulting

Receptionist

Holtwood, PA · On-site

$18 - $19/hr

Immediate need for a talented Receptionist This is a 24+months contract opportunity with long-term ... Pyramid Consulting, Inc. provides equal employment opportunities to all employees and applicants ...

ASK Consulting

Receptionist

Boston, MA · On-site

$16 - $19.09/hr

Description "All candidates must be directly contracted by ASK Consulting on their payroll and cannot be subcontracted. We are unable to provide sponsorship at this moment". Job Title: Receptionist ...

New

ASK Consulting

Receptionist

Boston, MA · On-site

$16 - $19.09/hr

Description "All candidates must be directly contracted by ASK Consulting on their payroll and cannot be subcontracted. We are unable to provide sponsorship at this moment". Job Title: Receptionist ...

New

ASK Consulting

Receptionist

Boston, MA · On-site

$16 - $19.09/hr

Description "All candidates must be directly contracted by ASK Consulting on their payroll and cannot be subcontracted. We are unable to provide sponsorship at this moment". Job Title: Receptionist ...

New

Pyramid Corporation

Receptionist

Holtwood, PA · On-site

$18 - $19/hr

Receptionist Immediate need for a talented Receptionist. This is a 24+months contract opportunity ... Pyramid Consulting, Inc. provides equal employment opportunities to all employees and applicants ...

CANIZARES LAW GROUP, LLC

Receptionist

Miami, FL · On-site

$15 - $19.75/hr

Explain consultation options and payment requirements. * Follow up with prospective clients through ... Previous receptionist, customer service, call center, sales, or law-office experience is preferred.

CANIZARES LAW GROUP, LLC

Receptionist

Miami, FL · On-site

$15 - $19.75/hr

... Bilingual Receptionist to join our Miami office. The ideal candidate is organized, dependable ... Explain consultation options and payment requirements. Follow up with prospective clients through ...

Ferguson Superstore

Receptionist

Broken Arrow, OK · On-site

$13.75 - $18.25/hr

Receptionist Position at Ferguson Superstore Ferguson Superstore is looking for a professional and ... Consultants as needed using computer software • Provide various administrative support as it ...

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How much do receptionist consultant jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for receptionist consultant in the United States is $17.82, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $19.95 per hour, depending on experience, location, and employer.

What is a receptionist consultant?

A Receptionist Consultant is a professional who provides expert advice and solutions to organizations on how to improve their front desk operations and customer service. They assess current receptionist practices, recommend strategies for efficiency, and may assist in training staff. Receptionist Consultants can work independently or as part of a consulting firm and are often hired to help streamline visitor management, phone handling, and administrative processes. Their goal is to enhance the overall experience for clients, guests, and employees who interact with the reception area.

What are the key skills and qualifications needed to thrive as a receptionist consultant?

To thrive as a Receptionist Consultant, you need strong organizational skills, proficiency in office administration, and a background in customer service, often supported by relevant certifications or experience. Familiarity with scheduling software, CRM systems, and office productivity tools is typically required. Excellent interpersonal communication, problem-solving, and adaptability are standout soft skills for this role. These competencies are crucial for managing client interactions, streamlining administrative processes, and ensuring a positive front-office experience.

What are some common challenges faced by receptionist consultants, and how can they be effectively managed?

Receptionist Consultants often encounter challenges such as handling high volumes of calls and visitors, managing multiple tasks simultaneously, and adapting to the unique procedures of different client organizations. Success in this role relies on maintaining strong organizational skills, remaining calm under pressure, and quickly learning new office protocols. Building good communication with both clients and internal teams also helps streamline processes and ensures a positive experience for all parties involved.

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Receptionist Consultant jobs near you
Infographic showing various Receptionist Consultant job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 89% Full Time, 6% Part Time, and 4% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $37,057 per year, or $17.8 per hour.

Applied AI Engineer

Santa Clara, CA • On-site

Sully.ai
See more jobs at Sully.aiexternal

Full-time

Re-posted 26 days ago

Job description

About Sully.ai
Sully.ai is building the AI operating system for healthcare. Our mission is to make doctors superhuman through AI, so every patient can get faster, better, and more accessible care.
Healthcare is one of the most important systems in the world, yet clinicians still lose hours every day to documentation, scheduling, intake, coding, and administrative work. We are changing that with AI medical employees: Scribes, Receptionists, Coders, Consultants and other agents that operate inside real clinical workflows.
We are backed by Amity Ventures, YC, Baidu Ventures amongst others, have raised $40M+, and are already deployed across hundreds of healthcare organizations. Our products process tens of thousands of clinical encounters every day, automate millions of minutes of administrative work, and help clinicians spend more time with patients.
The role
As an Applied AI Engineer on the Research team, you will work directly with our research and engineering org on the AI medical employees at the core of Sully - Scribe, Receptionist, Consultant, Coder, and what comes next. Your job is to turn the frontier of agent research into agents and eval systems that run in production for real clinicians within days.
This is a builder-researcher role. You read the latest work in agent, harness and context engineering, evals, and reinforcement learning, then ship from it. We do not separate "research" and "engineering" - you are expected to do both, and the work is measured by what lands in front of clinicians, not by a benchmark in isolation. Our agents are architecture- and configuration-driven: most of the behavior comes from decomposition, prompts, routing logic, context assembly, and evals rather than one-off code. You will own the systems that decide what the model sees, measure whether it worked, and make it better on its own over time.
You will own three things: evals, agents, and self-improving loops. Just as importantly, you will help define how Sully does applied research at all - the harnesses, eval infrastructure, and feedback loops that make every future agent faster to build and safer to ship. The environment is high-stakes by design: in clinical AI, accuracy has zero margin for error, and that constraint is the most interesting part of the job.
What you'll do
Build and scale evaluation systems
  • Design and scale automated evaluation pipelines (LLM-as-judge + clinician review) with clinical-grade benchmarks for accuracy, factuality, attribution, safety, and instruction-following.
  • Build the tooling and dashboards researchers use to run evals, and the CI gates that block regressions before they reach patients.
  • Treat measurement as a science: understand reliability, variance, and scalability of your eval methodology, not just the headline number.
  • Turn fuzzy notions of "is this good enough?" into clear, defensible metrics that researchers, engineers, and clinical stakeholders can act on.
Build and ship medical AI agents
  • Design, implement, and ship agents (Scribe, Receptionist, Consultant, Coder, and new ones) on Sully's stack - decomposition, parallel specialists, dynamic output contracts, and per-agent model routing.
  • Engineer the harness: memory, context compression, retrieval, session/state, and communication patterns between agents.
  • Own prompts as infrastructure - structured, versioned, composable per specialty and per organization - because without a correction loop as a safety net, prompt quality is load-bearing.
  • Take a research result to production end-to-end, fast.
Build self-improving and auto-evolving loops
  • Build closed-loop systems where clinician feedback and production traces drive targeted, evidence-grounded improvements to the specific components that need them - prompt quality that improves weekly, not quarterly.
  • Build self-improvement loops that automate model understanding and surface where each agent is failing and why.
  • Stand up continuous evaluation so production traffic is the experiment and the observability layer is the eval harness.
Push context and loop engineering forward
  • Track emerging research (context engineering, loop engineering, agent harnesses, evals, RL) and bring the best ideas into our agents quickly.
  • Experiment with the frontier: RL environments for clinical tasks, small fine-tuned models for focused extraction, synthetic data generation, and model selection per agent role.
  • Run rigorous experiments: define the hypothesis, build the pipeline, run the model, analyze the result, decide what to do next.
Partner across research, engineering, and clinical
  • Work directly with Engineering, QA, and Clinical to validate, harden, and roll out new capabilities.
  • Explain technical tradeoffs clearly to research, product, and clinical stakeholders.
  • Write up findings so they shape product decisions - and so others can build on them.
What good looks like
  • Fuzzy quality questions become numbers: every shipped agent has automated evals with regression gates, and "did this get better?" is answerable in minutes, not weeks.
  • Deployed agents perform reliably in production - measurable gains in accuracy, hallucination/omission reduction, or safety on the workflows you own.
  • New agent capabilities go from research idea to production in days, end-to-end, without sacrificing clinical safety.
  • A working closed-loop improvement system turns clinician feedback and traces into targeted component fixes, so agent quality improves on a weekly cadence.
  • The harness, eval tooling, and templates you build make the next agent faster to ship - applied research becomes more scalable, not more bespoke.
  • The research team has a builder who can read a paper at midnight and ship the improvement the same week, and who raises the technical bar for everyone around them.
What we're looking for
Required
  • Strong Python and applied ML background; comfortable with PyTorch / Hugging Face and an agent framework.
  • Proven experience designing agentic systems and LLM evaluation / benchmarking frameworks - not just calling an API, but building the harness and the measurement around it.
  • Demonstrated ability to design rigorous experiments and translate findings into production.
  • Fluency with prompting and context engineering: you have opinions about what the model sees and why it matters.
  • A portfolio of things you have built with LLMs that got them to do hard tasks - agents, benchmarks, synthetic data, fine-tunes, harnesses. Show us your contribution. We weight slope over pedigree.
  • Strong written communication and documentation habits - you can turn a vague behavioral problem into a hypothesis, an experiment, a result, and a clear writeup for technical and clinical stakeholders.
  • Hungry for new information: you read papers and ship from them, you stay current, and you have taste about what is signal.
  • High ownership and bias toward action.
Strong signals
  • Reinforcement learning, post-training, reward modeling, or fine-tuning small models for focused tasks.
  • Multi-agent systems and agent harness design (memory, context compression, routing, decomposition).
  • Experience with healthcare technology, EHRs, or clinical integrations and awareness of HIPAA/PHI handling.
  • Published research or deep open-source / applied work in LLMs and agent evaluation.
  • Startup or founder experience, and comfort operating in 0→1, high-ambiguity environments.
  • Experience using AI-native development tools to move quickly.
  • High-stakes / regulated domain experience where accuracy has zero margin for error.
Who this role is not for
This role may not be a fit if you:
  • Want to do pure research with no obligation to ship into production.
  • Want to do pure application engineering and avoid the measurement and research side.
  • Treat evals as an afterthought rather than the core craft.
  • Need fully specified tickets before taking action.
  • Are uncomfortable with ambiguity or with the stakes of clinical AI.
  • Need a stable, fully-documented product - our agents move fast and you will help build the harnesses, evals, and playbooks as you go.
  • Keep up with the field passively but rarely turn new ideas into working systems.
Interview process
  • Introductory conversation
  • Technical screen or systems walkthrough
  • "Show us what you built" - walk through an LLM agent, eval harness, or fine-tune you have shipped
  • Applied eval / agent exercise (e.g. design an eval harness for a clinical extraction task)
  • Final cross-functional interview

Location: Open to US remote, but ideally office based/hybrid in Bay Area.
Department Research Locations Mountain View

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