sr. applied scientist, ai evaluation & quality systems

aiml - applied ml engineer, data generation & verification

aiml - sr machine learning engineer, data and ml innovation

store designer, worldwide grocery real estate

associate creative director (hybrid - seattle, wa)

sr. applied scientist, ai evaluation & quality systems Jobs in Seattle

ai / software engineering intern Jobs in Seattle

ai / software engineering intern

aiml - applied ml engineer, data generation & verification Jobs in Seattle

aiml - sr machine learning engineer, data and ml innovation Jobs in Seattle

applied scientist (genai/llm), sandstone Jobs in Seattle

applied scientist (genai/llm), sandstone

sr applied scientist ai evaluation quality systems

Apple

Apple Services Engineering (ASE) powers the AI and LLM features behind experiences that hundreds of millions of users love every day. As these systems increasingly rely on human-in-the-loop evaluation, the quality of our products is directly constrained by the quality of our evaluation systems. We believe that to build exceptional AI, you need exceptional mechanisms to validate the signals used to train and evaluate them. 
 
Description 
 
The Human-centered AI, ML Data Quality Operations team is looking for a Senior Applied Scientist to join our growing team. We are building the systems and methodologies that make AI evaluation trustworthy, and scalable - directly shaping how Apple develops and validates AI across products and services. In this role, you will develop novel, scalable quality control solutions, working closely with cross-functional teams to ensure the data powering our AI/ML systems meets the highest standards of accuracy, consistency, and relevance. Your work will span the full lifecycle of quality assurance for AI and human judgments - from real-time validation and human-verified ground truth generation, to root-cause analysis that turns disagreements into corrective action. This role demands fluency across research thinking and engineering execution - you will prototype, validate, and ship. A strong point of view on when not to use a model or agent is as valued here as the ability to build one. 
 
&quot;,&quot;responsibilities&quot;:&quot;Design and implement scalable ground truth generation pipelines across varied task types, annotation modalities, and cold start conditions 
 
Build and maintain real-time monitoring systems that detect drift, distribution shifts, and quality degradation as they emerge across live evaluation and annotation pipelines. 
 
Design calibration frameworks that periodically re-anchor LLM evaluators against human-verified gold sets, correcting drift before it compounds. 
 
Build root-cause analysis tooling that surfaces disagreement patterns between automated and human judgments, and feeds findings directly into annotator training and guideline refinement. 
 
Partner closely with downstream users of these systems -ML teams, LLM-as-a-Judge (evaluator) developers, annotators - to ground design decisions in real feedback and usage patterns, not just architecture. 
 
Communicate findings and recommendations clearly to both technical and non-technical stakeholders 
 
Preferred Qualifications 
 
PhD in Computer Science, Machine Learning, Statistics, or a related field 
 
Experience in designing systems or tooling that are configurable and extensible by practitioners who did not build them 
 
Strong communication skills with the ability to influence technical direction across cross-functional teams 
 
Demonstrated passion for leveraging AI to improve work efficiency and scale 
 
Minimum Qualifications 
 
5+ years of industry experience in applied science or machine learning, with demonstrated experience building or operating production-grade evaluation, annotation, or quality-assurance pipelines. 
 
Hands-on experience designing ground truth generation pipelines across varied task types and annotation modalities, including cold-start scenarios with limited existing data. 
 
Experience building real-time monitoring or anomaly/drift detection systems for live data or ML pipelines. 
 
Working knowledge of evaluation methodology for generative AI - including LLM-as-a-judge design, meta-evaluation, failure mode analysis, and calibration/reference-guided grading techniques 
 
Strong software engineering fundamentals and proficiency in Python and relevant ML frameworks, with production experience building, deploying, and monitoring LLM-based pipelines and agents. 
 
Demonstrated ability to work directly with downstream users/stakeholders to incorporate feedback into system design, and to communicate findings clearly to both technical and non-technical audiences. 
 
MS or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical 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 $142,300 and $263,300, 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.

Sr. Applied Scientist, AI Evaluation & Quality Systems

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

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)

Expedia Group

<div>At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business. 
 
Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.
Introduction to the Product Team
 
The Product Team creates high-quality end-to-end experiences for travelers, partners, and Expedia Group. Our customer-first mindset focuses on developing products that encourage loyalty and repeat business from our travelers and partners. We partner closely with teams across Expedia Group to achieve growth and results for our customers and company.
We connect Travelers to new experiences, through trusted relationships. We use the power of Expedia Group brands to empower strategic partners to nurture and grow the existing relationship with their audience. Strategic Partnerships and Affiliates breadth of solutions which include cashback, travel offers, loyalty exchange, co-branded sites, Affiliate APIs and EG&rsquo;s Affiliate Program, bring a partners&rsquo; audience onto EG sites, thereby increasing EG&rsquo;s traffic and exposure.
Introduction to the Layla Team
Layla is an AI-native travel-planning and booking company, now part of Expedia Group&rsquo;s Product organization. Through a conversational travel assistant, Layla helps travelers move from inspiration to a complete, bookable itinerary &mdash; flights, hotels, activities, and the day-by-day detail &mdash; in minutes. As part of Expedia Group, the team combines Layla&rsquo;s planning and personalization capabilities with Expedia Group&rsquo;s global supply, first-party traveler insights, marketplace scale, and booking technology.
The team is small and senior by design and operates with startup pace: you own a surface, ship it, and see the impact on real travelers within weeks &mdash; with decisions driven by data and AI tooling used in practice every day.
As a Senior AI / ML Data Engineer, you will build the data foundations that power Layla&rsquo;s AI-native travel experiences &mdash; the reliable pipelines, high-quality datasets, and experimentation and evaluation support that let the team ship and improve AI/ML systems in production with confidence.
In this role, you will:
<ul><li>Design, build, and operate reliable, scalable data pipelines that power Layla&rsquo;s AI-native trip-planning and booking experiences across Layla and Bellboy.
</li><li>Own data quality, reliability, and observability across critical datasets, establishing well-documented, trustworthy data models that other teams can confidently extend.
</li><li>Build and support the data foundations for experimentation and model evaluation, enabling honest, timely readouts that inform product and AI/ML decisions.
</li><li>Enable the production use of AI/ML systems &mdash; supporting feature and data availability, evaluation datasets, and monitoring &mdash; while applying AI/ML concepts responsibly.
</li><li>Partner closely with Product, Analytics, and ML/engineering teams to turn ambiguous questions into dependable data and self-serve answers.
</li><li>Advocate for engineering excellence in data &mdash; testing, documentation, and reusable patterns &mdash; and mentor other engineers on best practices.
</li></ul>Minimum Qualifications:
<ul><li>Bachelor&rsquo;s degree in Computer Science, Data Engineering, or a related technical field; or equivalent related professional experience.
</li><li>8+ years of relevant professional experience.
</li><li>Strong data engineering expertise, including advanced SQL and data modeling, and experience designing and operating production data pipelines.
</li><li>Proficiency with Python for data engineering work.
</li><li>Demonstrated ability to own data quality, reliability, and observability for critical datasets in production.
</li><li>Familiarity with supporting AI/ML systems in production &mdash; such as data/feature pipelines, evaluation datasets, or experimentation &mdash; and applying AI/ML concepts to real-world products responsibly.
</li></ul>Preferred Qualifications:
<ul><li>Experience with a modern data stack (for example, a transformation framework such as dbt, a cloud warehouse such as BigQuery or Snowflake, and BI tools such as Looker or Metabase).
</li><li>Experience building and maintaining experimentation frameworks and product analytics for a consumer product with real paid acquisition.
</li><li>Experience with attribution modeling, including an understanding of its limitations.
</li><li>Experience supporting production AI/ML or generative-AI systems (feature pipelines, evaluation, or monitoring), with attention to safety and responsible use.
</li><li>Experience using AI-assisted development tools (for example, Claude or similar) to improve productivity, quality, and delivery speed.
</li><li>Experience in travel, marketplace, or e-commerce domains, or building a data/analytics function from scratch.
</li></ul>Expedia Group is proud to offer a wide range of benefits to support employees and their families, including medical/dental/vision, paid time off, and an Employee Assistance Program. To fuel each employee&rsquo;s passion for travel, we offer a wellness &amp; travel reimbursement, travel discounts, and an International Airlines Travel Agent (IATAN) membership. View our full list of benefits .
The total cash range for this position in Seattle is $184,500.00 to $258,000.00. Employees in this role have the potential to increase their pay up to $295,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.&amp;#xa;The total cash range for this position in Austin is $184,500.00 to $258,000.00. Employees in this role have the potential to increase their pay up to $295,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role. 
 
Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual&rsquo;s knowledge, skills, and experience. Pay ranges may be modified in the future. 
 
Benefits and perks
Expedia Group offers benefits and perks designed to support employees and their families, including medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and International Airlines Travel Agent Network (IATAN) membership. Learn more about life at Expedia Group at https://careers.expediagroup.com/life . 
 
Accommodation requests
Expedia Group is committed to providing an inclusive and accessible recruiting experience. If you need an accommodation or adjustment due to a disability during the application or recruiting process, please submit a request at https://expedia.service-now.com/askeg?id=job_accommodation . 
 
About Expedia Group
Expedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease. 
 
Important notice
Employment opportunities and job offers at Expedia Group will always come from Expedia Group's Talent Acquisition and hiring teams. Never share sensitive personal information unless you are confident of the recipient. Expedia Group does not extend job offers via email or messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official place to find and apply for roles is https://careers.expediagroup.com/jobs/ . 
 
Equal Opportunity
Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other characteristic protected by law. This employer participates in E-Verify. The employer will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS) with information from each new employee's I-9 to confirm work authorization.</div>

Senior AI / ML Data Engineer - Layla AI

Amazon Web Services

Job OverviewWe are seeking a dynamic and innovative Machine Learning Engineer to join our forward-thinking team. In this role, you will be at the forefront of developing, implementing, and optimizing advanced machine learning models and AI solutions. Your expertise will drive data-driven decision-making, enhance predictive analytics, and support scalable AI applications across diverse platforms. This position offers an exciting opportunity to work with cutting-edge technologies in big data systems, cloud services, and natural language processing to shape the future of intelligent solutions.Duties<ul><li>Design, develop, and deploy machine learning models utilizing frameworks such as TensorFlow and other AI tools to solve complex business problems.</li><li>Implement scalable data pipelines using ETL processes and big data systems like Hadoop and Spark to prepare data for analysis and model training.</li><li>Conduct statistical modeling and analysis to extract insights from large datasets, supporting research and strategic initiatives.</li><li>Collaborate with cross-functional teams to integrate AI models into production environments using cloud services such as AWS and machine learning cloud platforms.</li><li>Optimize model performance through rigorous evaluation, tuning, and validation techniques including model evaluation metrics and predictive modeling analysis.</li><li>Develop natural language processing applications to enhance text analysis capabilities within products or research projects.</li><li>Maintain comprehensive documentation of algorithms, processes, and system architecture while ensuring scalability and robustness of deployed models.</li></ul>Skills<ul><li>Proficiency in programming languages such as Python, Java, C, R, and Bash (Unix shell) for data analysis, model development, and automation tasks.</li><li>Extensive experience with machine learning frameworks including TensorFlow, Spark implementation, and generative AI techniques.</li><li>Strong knowledge of big data technologies like Hadoop, Spark, Talend, and SQL databases for efficient data mining and analytics.</li><li>Expertise in statistical analysis tools such as SAS or R for research-driven insights and statistical modeling.</li><li>Hands-on experience with AI implementation in cloud environments like AWS or other machine learning cloud services for scalable deployment.</li><li>Familiarity with database design principles, SQL query optimization, and data analytics to support complex data workflows.</li><li>Understanding of natural language processing (NLP), AI models development, model training strategies, evaluation methods, and deployment best practices.</li><li>Knowledge of data integration tools such as Talend for efficient ETL workflows; experience with Looker for data visualization is a plus.</li><li>Ability to work on large-scale big data systems involving scalability challenges in real-time analytics environments.</li></ul>Join us to leverage your expertise in machine learning/AI-based analysis to revolutionize how data transforms into actionable insights!Pay: $192,000.00 - $229,000.00 per yearBenefits:<ul><li>401(k)</li><li>Flexible spending account</li><li>Parental leave</li><li>Relocation assistance</li></ul>Work Location: In person

Machine Learning Engineer

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2>
As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. 
 
The Amazon Agentic WorkSpaces organization is building the future of secure, high-performance agentic WorkSpaces Applications environments &mdash; environments that span desktops and 
applications, serving knowledge workers, task workers, and automation agents at millions-of-user scale. 
 
## The role 
 
The Amazon Agentic WorkSpaces organization is seeking a Software Development Manager to lead the engineering team behind Amazon WorkSpaces Advisor, our AI-powered troubleshooting and 
optimization experience for administrators. We are building agentic solutions that analyze fleet telemetry, diagnose root causes, and recommend or apply fixes &mdash; turning problems that once 
took days of manual investigation into answers delivered in minutes. Our aim is to make virtual desktop and application environments effectively self-diagnosing, so administrators spend 
their time on strategic work instead of chasing symptoms across consoles and logs. 
 
You will lead a team of software engineers building at the intersection of applied AI and large-scale distributed systems. You will own Advisor end to end &mdash; expanding the range of problems it can solve, raising the accuracy and trustworthiness of its recommendations, and advancing it from reactive troubleshooting toward proactive detection and self-healing. In 
addition, your team owns a set of Control Plane Services through which customers provision, configure, and manage their environments, where you will be accountable for availability, latency, security posture, and operational health. This role offers the opportunity to build new agentic products from the ground up while operating production services that customers 
depend on every day. 
You will influence the technical direction of the team and work across many different technologies and disciplines. You will raise the engineering bar &mdash; design rigor, code quality, test coverage, and safe deployment practices &mdash; and champion an AI-native, automation-first approach to how software gets built and operated, using agentic tooling to increase velocity without 
trading away quality. You will identify technology gaps and opportunities and write effective communications including design documents, tenets, and PR/FAQs. Above all, you will hire, grow, and retain engineers who scale with the business. 
 
About the team 
ABOUT AWS: 
 
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.
 
 
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. 
 
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 flexible work hours and arrangements are part of our 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 and AmazeCon 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 or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field</li><li>3+ years of engineering team management experience</li><li>7+ years of working directly within engineering teams experience</li><li>3+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience</li><li>8+ years of leading the definition and development of multi tier web services experience</li><li>Knowledge of engineering practices and patterns for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations</li><li>Experience partnering with product or program management teams</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy</li><li>Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers</li><li>Experience delivering customer-facing features built on generative AI or agentic capabilities, including evaluating and improving output quality in production</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. 
 
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, WA, Seattle - 184,900.00 - 250,200.00 USD annually</div>

Software Development Manager, Agentic WorkSpaces Applications, Amazon Agentic WorkSpaces

Explore 17+ Sr applied scientist ai evaluation quality systems in Seattle this September. Find roles across top companies and apply now to boost your career.

17 sr applied scientist ai evaluation quality systems jobs In Seattle

Sr. Applied Scientist, AI Evaluation & Quality Systems

Sr. Applied Scientist, AI Evaluation & Quality Systems

Agentic AI Engineer — Healthcare AI

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

Senior AI / ML Data Engineer - Layla AI

Machine Learning Engineer

Software Development Manager, Agentic WorkSpaces Applications, Amazon Agentic WorkSpaces