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patient access representative temporary

TikTok

Technology
 
Research Scientist Graduate (Conversational AI)- 2027 Start (PhD)
 
Location
 :
 
Seattle
 
Employment Type
 :
 
Regular
 
Job Code
 :
 
A103012B
 
Responsibilities
 
We build the next-generation unified Agent system for TikTok's global e-commerce customer service — running in 30+ languages across one of the largest e-commerce surfaces on the internet.
 
Our north star is a self-evolving Agent: post-training, harness, memory / context engineering, tools, and evaluation form one closed loop, and every served conversation becomes the next iteration's training / eval / retrieval / skill-induction signal. This loop is already running in production — cases are mined, root-caused, turned into constrained candidates, replayed against frozen regression sets, and shipped behind guardrails.
 
Two things make this team different from most "LLM application" work:
 <ul>
<li>We build the agent runtime itself — Codex / Claude-Code-class — not prompts on top of a vendor API.</li>
<li>Evaluation and experimentation are first-class systems, not an afterthought. A self-improving loop optimizes whatever signal you give it, so the hardest and most valuable engineering here is making the judgment trustworthy — not just making the model change.</li>
</ul>
As a new grad, you'll own a real end-to-end piece from day one and ship it to production.
 
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
 Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
 
Responsibilities: 
<ul>
<li>Agent runtime (harness / agent loop). Orchestrate skills, tools, and context; implement loop control & intervention, progressive disclosure, and behavior-level guardrails. Build the production safety layer — pre-flight budgets and timeout truncation, serve-time gates, shadow / swap-in answer delivery, and safe fallback paths.</li>
<li>Context & memory for long multi-turn agents. Agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval. Treat context as an evolving, itemized playbook — with structured diffs and a deterministic curator — rather than an ever-growing prompt.</li>
<li>Post-training & the data flywheel. SFT / DPO / RL to internalize rules into weights (so the prompt gets shorter, not longer), plus distillation to smaller serving models. Turn served conversations into training / eval / retrieval signals.</li>
<li>Tools, Skills, and MCP. Tools-as-APIs, connectors, skill / tool search for large inventories, and skill-library governance — description conflicts, trigger evals, cross-skill mis-fire matrices, and on-demand loading instead of dumping every definition into context.</li>
<li>Evaluation you can bet a launch on. LLM-as-judge with human-agreement calibration; statistical rigor — paired comparison, confidence intervals, repeated sampling, pass^k; held-out and time-rolling eval splits with overfitting alarms; cascaded scoring and cross-family judge panels to make evaluation affordable at scale.</li>
<li>The self-evolving loop. Case mining → automatic root-cause → constrained candidate generation → replay verification against frozen regression sets → canary → flywheel. Build the plumbing that makes it auditable: candidate registry with exact runtime read-back, change lineage, and an archive of rejected candidates you can sample from next round.</li>
<li>Online experimentation & causal readout. Shadow / canary / A-B, non-inferiority gates, traffic-split health, metric definitions that survive scrutiny, and off-policy counterfactual evaluation where live A/B isn't possible.</li>
<li>Safety & anti-gaming. Keep the evaluator and the release gate outside the loop that edits the system; maintain never-optimized anchor sets; monitor full execution traces rather than final answers alone; pair every quality objective with a cost-side constraint.</li>
<li>Own one high-leverage end-to-end surface and ship it to production across 30+ languages, measured on real business metrics (CSAT, resolution / containment rate).</li>
</ul>
Qualifications
 
Minimum Qualifications: 
<ul>
<li>Individuals who are completing or have recently completed a PhD degree in CS/AI/Math/Quantitative degree or a related discipline.</li>
<li>Strong Python plus one of C++ / Go / Rust / Java</li>
<li>Solid ML / DL / NLP fundamentals, with genuine hands-on experience with LLMs or agents (coursework, research, internship, competition, open-source, or a serious side project)</li>
<li>Basic statistical literacy — you can compute a confidence interval, explain what a p-value does and doesn't mean, and tell the difference between "the number went up" and "the system got better"</li>
<li>Able to read a paper or an engineering blog and turn it into working code</li>
</ul>
Preferred Qualifications
 <ul>
<li>Have built the runtime, not just called an API — even at research / hobby / competition scale: your own agent loop / harness, a memory / context-management system, a RAG or tool-use agent, or a fine-tuned / post-trained model</li>
<li>Post-training: SFT / DPO / RLHF / RLAIF / RLVR, reward modeling, reward hacking and how to defend against it</li>
<li>Agent systems: harness, context engineering, MCP / Skills, sub-agents, tool search</li>
<li>Evaluation & experimentation: LLM-as-judge and judge calibration, pass^k, regression suites, A/B and non-inferiority testing, off-policy evaluation</li>
<li>Self-improving / evolutionary systems: evolutionary program search, candidate archives and parent sampling, automatic prompt / context optimization, multi-objective (Pareto) selection and credit assignment</li>
<li>Inference & serving: vLLM / TensorRT-LLM, MoE, KV / prompt caching, and the cost engineering that comes with it</li>
<li>Publications (for PhD), strong competition results (ACM-ICPC / Kaggle / ML competitions), or notable open-source contributions</li>
</ul>
Job Information
 
【For Pay Transparency】Compensation Description (Annually)
 
The base salary range for this position in the selected city is $153900 - $300960 annually.
 
​
 
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
 
​
 
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
 
​
 
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
 
​
 
For Los Angeles County (unincorporated) Candidates: 
​
 
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
 
​
 
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
 
​
 
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
 
​
 
3. Exercising sound judgment.
 
​
 
About TikTok
 
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
 
​
 
Why Join Us
 
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
 
​
 
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
 
​
 
​
 
Diversity & Inclusion
 
​
 
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
 
​
 
TikTok Accommodation
 
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at
 
https://tinyurl.com/RA-request

Research Scientist Graduate (Conversational AI)- 2027 Start (PhD)

Deloitte

<div>
 Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.
 
 
 This is an early, well-funded build. You will own agent systems end to end - from architecture through production - and your work ships into live clinical and operational settings within your first months, not into a lab.
 
 
 As an Agentic AI Engineer, you will design, build, and operationalize the LLM- and SLM-powered systems behind real healthcare decisioning - the reasoning, orchestration, retrieval, memory, and control layers that let intelligent agents operate reliably across the hardest decisions in the industry: clinical reasoning, prior authorization and claims integrity, care navigation, and the operational workflows that run across payers, providers, and life sciences. This is not a prompt-only role. We are looking for builders who think deeply about system behavior, grounding, and reliability where a wrong action has real consequences for patients and the clinicians who serve them.
 
 
 You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the agentic engineering depth.
 
 
 We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.
 
 
 Work you'll do
 
 
 Agent architecture &amp; orchestration
 
 
 <ul>
 <li>
 Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution against complex, regulated operational processes.
 </li>
 <li>
 Build stateful workflows using frameworks such as LangGraph and LangChain - including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.
 </li>
 <li>
 Engineer for long-horizon reliability - multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail.
 </li>
 <li>
 Build the reasoning behind regulated decisions - policy- and criteria-grounded outputs, structured proposer/critic/judge-style review, and auditable rationales for high-stakes decisions across the industry, from clinical review and prior authorization to claims integrity and care management.
 
 
 </li>
 </ul>
 Retrieval, grounding &amp; context engineering
 
 
 <ul>
 <li>
 Develop end-to-end Retrieval-Augmented Generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
 </li>
 <li>
 Engineer memory and context management - conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.
 </li>
 <li>
 Apply modern context-delivery patterns (e.g., MCP-style tool/context interfaces) so agents access the right information at the right time.
 
 
 </li>
 </ul>
 Reliability, evaluation &amp; safety
 
 
 <ul>
 <li>
 Implement observability and tracing for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behavior.
 </li>
 <li>
 Apply guardrails, safety controls, and failure-handling to reduce hallucinations and unsafe actions.
 </li>
 <li>
 Evaluate agents at the trajectory and task level - multi-step task success, failure-mode and regression analysis, and sandboxed test environments - alongside retrieval- and generation-quality metrics, automated checks, and human review.
 </li>
 <li>
 Engineer healthcare-grade safety - deployment eval gates, human-oversight and escalation models, auditability and traceability for regulated decisions, and PHI/HIPAA-aware data handling.
 
 
 </li>
 </ul>
 Integration &amp; production craft
 
 
 <ul>
 <li>
 Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers so agents operate safely within real business workflows.
 </li>
 <li>
 Deliver production-quality code with strong practices in testing, CI/CD, logging, versioning, and documentation; make architecture decisions that balance quality, safety, latency, cost, and model risk.
 </li>
 <li>
 Partner with our modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning - through evaluation-driven feedback and, where it helps, fine-tuned or reasoning-optimized models.
 </li>
 <li>
 Translate ambiguous, high-complexity operational processes into robust system logic and reusable AI patterns; stay current with advances in agentic systems and translate research into practical engineering decisions.
 
 
 </li>
 </ul>
 The team
 
 
 Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
 
 
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
 </li>
 <li>
 Demonstrated depth building and shipping production agentic systems - this is your primary craft, not a recent exploration. We weigh shipped systems, research, model releases, and open source over years in a title; expect strong software/ML fundamentals plus substantial, recent hands-on agentic work.
 </li>
 <li>
 Strong, hands-on experience building production agent systems with modern orchestration - LangGraph/LangChain or equivalent, including custom orchestration.
 </li>
 <li>
 Experience designing and optimizing end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation.
 </li>
 <li>
 Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
 </li>
 <li>
 Deep, practical understanding of LLM behavior - strengths, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs - and the evaluation methods used to measure them.
 </li>
 <li>
 Experience evaluating and debugging agent behavior - task-success and trajectory analysis, not just output quality.
 </li>
 <li>
 Strong Python engineering skills and modern software practices: testing, CI/CD, version control, and API integration; experience implementing observability, tracing, and debugging for LLM-based systems in production.
 </li>
 <li>
 Hands-on experience with at least one frontier model platform (e.g., Anthropic, Google, OpenAI) and/or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.
 </li>
 <li>
 Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
 </li>
 <li>
 Limited immigration sponsorship may be available.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with multi-agent systems and agent collaboration patterns.
 </li>
 <li>
 Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus.
 </li>
 <li>
 Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA.
 </li>
 <li>
 Understanding of traditional NLP concepts: tokenization, semantic similarity, entity extraction, summarization, and transformer fundamentals.
 </li>
 <li>
 Experience operating in highly regulated, high-stakes, or operationally complex environments; healthcare exposure - clinical, payer, or life-sciences workflows, or standards such as FHIR - is a plus, not a requirement.
 </li>
 <li>
 Demonstrated habit of staying current with AI research, benchmarks, and emerging engineering patterns.
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $110,700-$372,900 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.
</div>

Agentic AI Engineer — Healthcare AI

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2 patient access representative temporary jobs In Seattle

Research Scientist Graduate (Conversational AI)- 2027 Start (PhD)

Agentic AI Engineer — Healthcare AI