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nlp engineer

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

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)

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2>
AWS Security SecDevOps is a team of world-class software development engineers responsible for shaping the future of internal security tooling within AWS Security. We build technologies at scale to help keep AWS and our customers secure. As a software development engineer on this team you will leverage modern technologies and AWS services to empower AWS security response engineers to be more productive. 
 
You are a software developer who likes to lead and deliver software solutions that solve business problems and delight your customers with efficiency and productivity gains. You&rsquo;ve got development experience with technologies like AWS, Java, TypeScript, React, Python, and SQL, though you don't need experience with all of those to start. You might be a front end developer, a back end developer, or a full stack engineer. If you have some background in security that is helpful, but not required. 
 
We are the AWS SecDevOps team. We build APIs, websites, and tools for AWS Security Operations including a Security Orchestration, Automation, and Response (SOAR) platform. Our team tackles the most challenging internal development efforts within AWS Security - the ones which require scale, availability, and integration with numerous other services and tools. To help delight our customers we incorporate real-time collaboration features and use purpose-built databases, while maintaining close contact with our customers throughout the development process. 
 
Key job responsibilities 
Own team-level architecture and design guidance for security response systems, ensuring solutions are reliable, secure, extensible, and simple. 
 
Lead projects requiring the coordinated work of multiple engineers, taking accountability for the design even when you don't author every component, maintaining a high security bar. 
 
Take business problems where the technology strategy is undefined and drive clarity, making sound trade-offs between short-term delivery and long-term maintainability. 
 
A day in the life 
You review the operational health of large-scale detection and response systems, ensuring security coverage across all AWS accounts and addressing any service degradation rapidly. 
 
You make architectural decisions and review technical designs, ensuring your systems meet the reliability and scalability bar required for security-critical infrastructure. 
 
You meet with customers and other team members to work backwards and understand the pain points so that you can solve for rapid, efficient response issues. 
You mentor others, helping to grow their skills, achieve successes through delivery and hold a high operational bar.
 
 
About the team 
Diverse Experiences 
Amazon Security values diverse experiences. Even if you do not meet all of the 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 Amazon Security? 
At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon&rsquo;s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. 
 
Inclusive Team Culture 
In Amazon Security, it&rsquo;s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. 
 
Training &amp; 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, training, and other career-advancing resources here to help you develop into a better-rounded professional. 
 
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.<h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>5+ years of non-internship professional software development experience</li><li>5+ years of programming with at least one software programming language experience</li><li>5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Experience as a mentor, tech lead or leading an engineering team</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience</li><li>Bachelor's degree in computer science or equivalent</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 building highly available software with AWS services and distributed architecture.</li><li>Experience working with React, TypeScript, building services with Java, Python and integrating with multiple systems.</li><li>Experience or familiarity with building scalable systems that integrate Generative AI technologies</li><li>Experience in security operations or related domains</li><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></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 - 168,100.00 - 227,400.00 USD annually</div>

Senior Software Development Engineer, AWS SecDevOps

Software Engineer

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
Come shape the future with us! As a major cloud provider, our technology powers many of the world's largest organizations. We are building an exciting Generative AI application that will transform how enterprises operate. 
 
You will have the exciting opportunity to be on the ground floor of developing this exciting product. We are seeking creative problem-solvers who are energized by tackling complex technical challenges to push the boundaries of what's possible with AI. Our team collaborates closely across roles to rapidly build, test and iterate as we create an AI solution that delivers real business value. Join our innovative team to leave your mark on the next generation of AI. This is an exciting role, with high visibility and high impact, leading several customer facing initiatives under the Amazon Quick organization. The ideal candidate will have a strong technical background in front-end development, excellent leadership skills, and a passion for delivering high-quality user experiences.
 
 
Key job responsibilities 
As a lead software development engineer on the Amazon Quick team, you will design and develop features for the Amazon Quick product. You will work with senior scientists and engineers within Amazon AI and develop high quality software that is robust and reliable. 
 
You will be responsible for 
<ul><li>Deciding what features and performance improvements to build</li><li>Driving software engineering best practices</li><li>Designing distributed and scalable systems</li><li>Providing technical leadership and contribute to the definition, development, integration, test, documentation, and support new AI Services</li><li>Instill best practices for software development and documentation, making sure designs meet requirements, and delivering high quality software on tight schedules</li></ul> 
About the team 
Diverse Experiences 
AWS 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. 
 
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 conferences, inspire us to never stop embracing our uniqueness. 
 
Mentorship &amp; 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.
 
 
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.
</div><h2 class="jobSectionHeader">BASIC QUALIFICATIONS</h2><ul><li>5+ years of non-internship professional software development experience</li><li>5+ years of programming with at least one software programming language experience</li><li>5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Experience as a mentor, tech lead or leading an engineering team</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience</li><li>Bachelor's degree in computer science or equivalent</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 - 168,100.00 - 227,400.00 USD annually</div>

Senior Software Development Engineer, Amazon Quick

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

Explore 9+ Nlp engineer in Seattle this September. Find roles across top companies and apply now to boost your career.