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clinical research coordinator i - ascend program Jobs in Seattle

clinical research coordinator ii Jobs in Seattle

clinical research coordinator ii

clinical project coordinator

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

Apple

At Apple, great ideas quickly become phenomenal products, services, and customer experiences. Join us in powering the global media experiences loved by millions! Our Media Processing Infrastructure team designs, builds, and operates the scalable, resilient, and high-performance workflow platforms that ingest, process, and transcode rich audio and video at massive scale. 
 
Our portfolio spans from lightweight App Store previews to multi-terabyte feature film sources - powering flagship Apple services including Apple TV, Apple TV Channels, Apple Fitness+, Apple Original Films &amp; TV, Apple Music Video, hosted Apple Podcasts, and live linear streaming orchestration. We build robust, distributed systems that process media flawlessly, focusing on reliability, extreme performance, and disruptive engineering excellence. 
 
Description 
 
In this role, you will lead a high-caliber team responsible for the core workflow and distributed transcoding infrastructure that power Apple&rsquo;s media ecosystem. You will own the strategy, technical roadmap, and architectural direction for systems managing exponentially growing asset volumes and workflow complexity across both VOD (including sports VOD and clips) and live linear streaming services. 
 
As a hands-on technical manager, you will remain deeply credible in the underlying architecture and distributed Java backend systems while fostering a culture of ownership, operational rigor, and continuous growth. You will also champion modern engineering velocity by cultivating your team's fluency with agentic and generative AI tooling-driving adoption to accelerate delivery and set a new standard for engineering excellence.&quot;,&quot;responsibilities&quot;:&quot;Lead hiring, onboarding, mentoring, and performance management to build, retain, and inspire a world-class, inclusive engineering team. 
 
Define and communicate the team's technical vision, architectural roadmap, and execution strategy aligned with broader Apple Media Services objectives. 
 
Maintain deep technical credibility across distributed Java backend services, media pipelines, and storage/caching layers-guiding critical architecture decisions and high-scale design reviews. 
 
Drive the evolution of our high-performance asset cache solution and media orchestration engines, expanding them as foundational platform offerings adopted across Apple. 
 
Scale and harden infrastructure to handle rapid growth in both asset volume and workflow complexity across VOD, sports highlights, and live linear orchestration. 
 
Drive your team&rsquo;s adoption and sustained fluency with generative and agentic AI tooling to elevate engineering velocity, code quality, and testing practices. 
 
Actively shape AI-assisted developer workflows within the team and champion best practices across the broader organization. 
 
Partner closely with cross-functional stakeholders across engineering, Product, Project Management, Operations, and SRE to ensure seamless end-to-end delivery and high system availability. 
 
Champion operational excellence and reliability, ensuring mission-critical production pipelines maintain world-class resilience, low latency, and fault tolerance. 
 
Preferred Qualifications 
 
Prior experience in direct people management leading teams of 5+ engineers in a fast-paced platform or infrastructure domain. 
 
Hands-on experience with agentic AI frameworks, generative AI tooling, or LLM-integrated developer workflows applied to production software development. 
 
Skilled in fostering organizational AI fluency-evaluating developer tooling, establishing modern workflows, and driving sustained adoption across an engineering team. 
 
Experience with large-scale media processing pipelines, video transcoding technologies (e.g., HEVC, HDR, stereoscopic video), or live streaming linear orchestration. 
 
Proven background building and scaling distributed caching tiers or high-throughput asset storage solutions adopted broadly across an enterprise organization. 
 
Demonstrated ability to translate ambiguous, high-scale media requirements into robust technical roadmaps and durable platform architectures. 
 
Minimum Qualifications 
 
5+ years of software engineering experience designing, building, and operating mission-critical, highly scalable distributed systems at production scale. 
 
Proven track record of building, leading, and mentoring high-performing software engineering teams with direct people management responsibility. 
 
Deep technical expertise in Java and modern backend distributed system architectures (concurrency, distributed caching, asynchronous messaging, and workflow orchestration). 
 
Demonstrated ability to remain technically credible across complex systems-engaging in architectural reviews and deep-dive technical discussions that earn the trust of senior engineers. 
 
Strong track record of partnering cross-functionally and driving alignment across engineering, project management, operations, and SRE organizations. 
 
Proven experience with platform-oriented engineering-designing reusable, scalable infrastructure and APIs that serve as dependable foundations for other teams. 
 
BS Degree in Computer Science or equivalent work experience. 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $195,700 and $338,400, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Software Engineering Manager - ASE Media Processing Infrastructure

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2>
Do you want to build the backbone of Generative AI cloud at AWS? Do you want to build the future of the cloud for AI training and inference? Want to do industry leading work delivering continuous price performance improvements in the cloud for AI model training for multi billion variable LLMs? Come Join us in designing, delivering and operating AWS cloud offerings that enable high performance and scalability in AI/ML and HPC workloads. 
 
Utility Computing (UC) 
AWS Utility Computing (UC) provides product innovations &mdash; from foundational services such as Amazon&rsquo;s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS&rsquo;s services and features apart in the industry. As a member of the UC organization, you&rsquo;ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. 
 
You&rsquo;ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You&rsquo;ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you&rsquo;ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. 
 
Key job responsibilities 
As a Cloud Hardware Development Engineer on the AWS Generative AI &amp; ML Servers team, you will partner with EC2 to define server architectures based on workload demand and customer requirements. You will translate these architectures into detailed supporting designs and component specifications that enable high-performance AI training and inference at scale. 
You will define and drive validation strategies from PCBA bring-up through server and rack integration, ensuring designs meet performance, thermal, mechanical, power, and signal integrity requirements. Working with interdisciplinary teams of component, firmware, test, qualification, and integration engineers, you will lead design and manufacturing partners through development and production. 
You will be responsible for triaging hardware issues at both ODM facilities and datacenters, conducting root cause analysis, and implementing corrective actions. After launch, you will oversee the fleet of servers you develop, monitoring quality metrics and ensuring they meet customer requirements while identifying opportunities for continuous improvement. 
 
A day in the life 
Your day to day responsibilities will include interfacing with internal customers to understand project requirements and facilitate system development. You will work directly with EC2 teams to align on architecture decisions and translate workload requirements into technical specifications. You will be responsible for learning operational challenges in the existing fleet with the goal of improving customer experience and developing improved systems for future designs. You will work directly with vendors and ODM/JDM design teams to develop and manufacture your product at scale, driving design reviews, resolving technical issues, and ensuring quality throughout the development lifecycle. 
 
About the team 
The team is comprise of both Hardware Design Engineers, System Design Engineers, Software Development Engineers and Technical Program Managers, all with the common goal of delivering the best Accelerated Server fleet possible to our customers. 
 
*Why AWS* 
Amazon Web Services (AWS) is the world&rsquo;s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating &mdash; that&rsquo;s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. 
 
*Diverse Experiences* 
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn&rsquo;t followed a traditional path, or includes alternative experiences, don&rsquo;t let it stop you from applying. 
 
*Work/Life Balance* 
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there&rsquo;s nothing we can&rsquo;t achieve in the cloud. 
 
*Inclusive Team Culture* 
Here at AWS, it&rsquo;s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity) conferences, inspire us to never stop embracing our uniqueness. 
 
*Mentorship and Career Growth* 
We&rsquo;re continuously raising our performance bar as we strive to become Earth&rsquo;s Best Employer. That&rsquo;s why you&rsquo;ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.<h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>Bachelor's degree in Electrical Engineering, Computer Engineering, or equivalent</li><li>2+ years of hardware design, development and validation experience for server or compute platforms</li><li>Experience in one or more server technologies: thermal/mechanical design, power delivery, high-speed signal integrity, or accelerator subsystems</li><li>Experience developing functional specifications, design verification plans, and validation test procedures</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Master's degree in Electrical Engineering, Computer Engineering, or a related field</li><li>2+ years of experience working with ODMs through product development and manufacturing lifecycle</li><li>Experience in high-speed bus design, signal integrity, or power delivery for server/compute platforms</li><li>2+ years experience working with hardware bring-up, debug, or validation of GPU/accelerator platforms</li><li>Familiarity with PCIe topology, NVMe, and accelerator interconnects</li><li>Experience developing and executing test procedures for electrical or mechanical systems</li><li>Experience working in large-scale datacenter or cloud environments</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, CA, Cupertino - 157,300.00 - 212,800.00 USD annually 
USA, TX, Austin - 136,000.00 - 184,000.00 USD annually 
USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually</div>

Cloud Hardware Dev Engineer (AWS Generative AI & ML Servers), AWS Generative AI & ML Servers

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)

Explore 4+ Clinical project coordinator in Seattle this September. Find roles across top companies and apply now to boost your career.

4 clinical project coordinator jobs In Seattle

Agentic AI Engineer — Healthcare AI

Software Engineering Manager - ASE Media Processing Infrastructure

Cloud Hardware Dev Engineer (AWS Generative AI & ML Servers), AWS Generative AI & ML Servers

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