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research coordinator 1 ne s uaw research

turing

<div><div><h1 class="jobSectionHeader">About Turing</h1>

Turing's mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com.</div><div></div> 
Turing is looking for a Senior Technical Recruiter to join our embedded Research Engineering Recruitment Pod. In this role, you will work side-by-side with our Research Scientists, Research Engineers, and AI leadership to source, engage, and close the talent building at the frontier of AI.

This is not a high-volume, transactional recruiting role. The talent pool for elite Research Engineers and Forward-Deployed Engineers is highly concentrated, hyper-competitive, and continuously targeted by frontier labs. You will act as an embedded strategic partner, mapping passive talent ecosystems across academia, quantitative research, and open-source networks, developing technically informed candidate pipelines, and orchestrating bespoke closing strategies for candidates who do not move through standard corporate hiring funnels.
<h3 class="jobSectionHeader">
The Anti-Sell</h3>

This is a high-urgency recruiting role. You will compete head-to-head with frontier AI labs and hyperscalers for candidates who are often managing multiple highly competitive offers. Winning in this market requires deep technical literacy, exceptional speed, and continuous market intelligence.

This isn't for everyone. But if you want to work on an elite team to hire the most sought-after talent in frontier AI and help build the teams defining what comes next in the field, this role may be for you!
<h3 class="jobSectionHeader">
What You'll Do</h3>
<h4 class="jobSectionHeader">Sourcing &amp; Pipeline Building</h4>
<ul><li>Look beyond standard keyword searches by continuously tracking arXiv preprints, GitHub activity, OpenReview submissions, and open-source contributions.</li>
<li>Map and cultivate passive talent ecosystems across top PhD programs (Stanford, MIT, CMU), frontier research labs and high-growth AI startups.</li>
<li>Build proactive candidate pipelines long before formal requisitions open, using emerging signals across research, open-source, and professional networks to identify and engage exceptional talent early.</li>
</ul><h4 class="jobSectionHeader">Engaging Incredibly Hard-to-Reach Talent</h4>
<ul><li>Reach passive researchers, Research Engineers, and Forward-Deployed Engineers who do not respond to generic recruiter outreach.</li>
<li>Demonstrate the technical fluency to assess candidate relevance, understand research contributions, and partner with technical leaders on candidate calibration across relevant technical domains.</li>
</ul><h4 class="jobSectionHeader">Closing Highly Sought-After Talent</h4>
<ul><li>Partner directly with the CEO, CTO, and research leaders to design and execute individualized closing playbooks for highly sought-after talent.</li>
<li>Win contested offers by understanding each candidate's motivations and leveraging the aspects of the opportunity that matter most to them.</li>
</ul><h3 class="jobSectionHeader">What We're Looking For</h3>
<ul>
<li>Technical Recruiting Track Record: 4+ years of full-lifecycle technical recruiting experience, with proven depth in AI/ML, Research Engineering, Distributed Systems, or Forward-Deployed Engineering.
</li>
<li>Domain Literacy: Ability to speak credibly with top researchers and technical leaders.
</li>
<li>Proven Closer: Demonstrated track record of personally sourcing and closing highly sought-after candidates against competing offers from frontier labs or hyperscalers.
</li>
<li>Compensation &amp; Equity Fluency: Strong understanding of private-company equity, technical compensation, and competitive market benchmarking.
</li>
<li>High Ownership &amp; Startup Speed: Ability to operate with high urgency in a fast-moving AI research environment.
</li>
</ul><h3 class="jobSectionHeader">What Success Looks Like</h3>
<ul><li>Compounding Talent Density: Continuously raising the technical caliber and execution velocity of Turing's Research Engineering and Forward-Deployed AI teams.</li>
<li>High Win Rates: Consistently converting highly sought-after candidates and maintaining strong offer acceptance rates in contested searches.</li>
<li>Trusted Technical Partnership: Earning deep trust with research directors and technical leads, acting as an embedded advisor who understands the research roadmap.</li></ul><div><h2 class="jobSectionHeader"> 
Values</h2>
<ul><li>We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. 
</li>
<li>We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection 
</li>
<li>We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.</li>
</ul><h2 class="jobSectionHeader">Advantages of joining Turing</h2>
<ul><li>Work at the frontier of AI, helping the world's leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. 
</li>
<li>Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. 
</li>
<li>Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges. 
</li>
<li>Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. 
</li>
<li>Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.</li></ul> 


Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
<div>
For applicants from the European Union, please review Turing's GDPR notice here.</div></div></div>

Senior Technical Recruiter - Research Engineering Pod

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

TikTok

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

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

Deloitte

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

Agentic AI Engineer — Healthcare AI

Curi Bio

<div><div><div><div>To apply, please email your CV and any other relevant documents to careers@curibio.com.
</div></div></div><div><h3 class="jobSectionHeader">SUMMARY</h3>
Curi Bio is a leading developer of human stem cell-based platforms for drug discovery based in Seattle, WA. Curi integrates human stem cell models, tissue specific biosystems, and AI/machine learning-based phenotypic screening into its preclinical discovery platform to accelerate discovery of next generation medicines. With Curi Bio&rsquo;s suite of human stem cell-based products and services, scientists are able to build more mature and predictive human iPSC-derived tissues, with a focus on cardiac, musculoskeletal, and neuromuscular models. Curi Bio is closing the gap between preclinical data and human results, accelerating the discovery and development of safer, more effective medicines.
Curi Bio is a startup company seeking a Research Associate to join our team whose primary responsibility will be cellular media production and quality control, with additional support for skeletal muscle and motor neuron differentiation, cell expansion, and shipping/logistics. Prioritized candidates will have hands-on experience formulating and producing cell culture media, aseptic cell culture experience, prior biotech experience, and experience with standard cell biology assays. Experience in quality control/assurance functions and/or logistics in a biotech atmosphere is highly desired.
<h3 class="jobSectionHeader">ESSENTIAL FUNCTIONS &amp; RESPONSIBILITIES</h3>
<ul><li>Formulate, produce, and quality-control cellular media supporting 2D and 3D tissues utilizing iPSC-derived cardiomyocyte, skeletal muscle, and motor neurons
</li><li>Own media production scheduling, lot documentation, and specifications; troubleshoot and improve formulations and production workflows
</li><li>Cell production and quality control
</li><li>Production of iPSC-derived motor neuron and skeletal muscle differentiations
</li><li>Basic iPSC cell culture and 3D muscle tissue engineering
</li><li>Packaging and coordinating shipping of Curi Bio products
</li><li>Communicate effectively with team to ensure proper Go/ No-Go Decisions and adherence to cell differentiation protocols
</li><li>Complete appropriate documentation, audit, and release of products. Maintain detailed, up-to-date, and well-documented Electronic Lab Notebook (ELN)
</li><li>Update and communicate with scientific staff in person, though typically flexible work schedule
</li><li>Perform other job-related duties as required. Weekend work will be required
</li></ul><h3 class="jobSectionHeader">MINIMUM QUALIFICATIONS</h3>
<ul><li>Bachelor&rsquo;s degree in a scientific field is required
</li><li>Experience preparing, filtering, and QC-testing cell culture media, buffers, and reagents
</li><li>Cell culture and/or tissue culture experience is required
</li><li>Excellent sterile technique and ideally some microscopy experience
</li><li>Ability to manage timelines and goals, work in a matrixed team environment
</li><li>Proficient in standard Office programs like Word, PowerPoint, and Excel
</li><li>Proven ability to troubleshoot technical and scientific issues, both individually and as part of a team
</li></ul><h3 class="jobSectionHeader">PREFERRED QUALIFICATIONS</h3>
<ul><li>Strong preference for local candidates
</li><li>Experience producing media at scale or working to defined media specifications and lot release criteria
</li><li>Familiarity or experience with GMP practices and record-keeping
</li><li>Biotech/Pharmaceutical company or CRO experience
</li></ul><h3 class="jobSectionHeader">WORKING CONDITIONS / PHYSICAL DEMANDS</h3>
<ul><li>Fine motor skills needed for aseptic work.
</li></ul><h3 class="jobSectionHeader">BENEFITS</h3>
<ul><li>Full-Time Salary Range: $68,000-75,000 + benefits. Actual pay will be determined based on education, certifications, experience, and other job-related factors permitted by law.
</li><li>Medical, Dental, Vision, Life
</li><li>401k plan
</li><li>Commuter assistance program
</li><li>Career advancement opportunities within Curi Bio
</li><li>Relocation assistance will not be provided
</li></ul><h3 class="jobSectionHeader">HOW TO APPLY</h3>
Please email your CV and any other relevant documents to careers@curibio.com.
Visit www.curibio.com/careers for more information on Curi Bio and other open positions.
Curi Bio is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.</div></div>

Research Associate II, Media and Cell Production

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