applied ml engineer ai ml evaluation simulation

Apple

We&rsquo;re building the next generation of AI evaluation systems - and we&rsquo;re looking for a 
 
motivated early-career engineer who&rsquo;s excited to work at the intersection of ML, 
 
software, and product. You&rsquo;ll join a team focused on making AI systems - including 
 
LLMs and agentic AI - more measurable, testable, and trustworthy in real-world 
 
scenarios. 
 
This is a hands-on, collaborative role ideal for someone with a strong foundation in 
 
software engineering and machine learning, and an eagerness to grow by building tools 
 
and systems that help evaluate advanced AI behavior at scale. 
 
Description 
 
As an Applied ML Engineer on our team, you&rsquo;ll help develop simulation systems, 
 
support data tooling, and contribute to evaluation workflows that improve the reliability 
 
of modern AI. You&rsquo;ll collaborate closely with experienced engineers and researchers, 
 
learning how to instrument, monitor, and analyze model behavior - especially for 
 
language models and agent-style systems. 
 
This is a great opportunity for someone early in their career to work with cutting-edge AI 
 
technologies in a high-impact, supportive environment. You&rsquo;ll gain experience working 
 
with large-scale ML systems, learn best practices in applied AI, and grow your skills 
 
across engineering, product, and research.&quot;,&quot;responsibilities&quot;:&quot;Contribute to systems that simulate interactive behaviors (including LLM-driven 
 
agents) 
 
Help build tools to support dataset generation and evaluation workflows 
 
Assist in developing pipelines for structured insights from model behavior 
 
Collaborate with teammates to debug and improve evaluation systems 
 
Write clean, testable code to support scalable and reliable infrastructure 
 
Learn how to define metrics that connect model behavior to real-world outcomes 
 
Preferred Qualifications 
 
Coursework or internship experience in ML, AI systems, or applied data science 
 
Familiarity with training or evaluating models (even via coursework or personal 
 
projects) 
 
Exposure to tools like PyTorch, TensorFlow, or Hugging Face 
 
Interest in AI observability, behavior simulation, or synthetic data 
 
Passion for working cross-functionally in fast-moving, exploratory teams 
 
Minimum Qualifications 
 
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Machine Learning, or related field 
 
Strong programming skills in Python or another modern language (e.g., Java, Swift, 
 
Go) 
 
Basic understanding of machine learning principles 
 
Interest in LLMs, generative AI, or agent-based systems 
 
Curiosity about how to evaluate and improve real-world AI performance 
 
Strong collaboration and communication skills 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
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 $120,300 and $210,100, 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.

Applied ML Engineer - AI/ML Evaluation & Simulation

Join Apple&rsquo;s Information Security Machine Learning (ISML) team, where we are redefining cybersecurity through data-driven intelligence. Our mission is to transform traditional reactive security measures into autonomous systems that proactively detect and defend against threats. We achieve this through cutting-edge research, applied science, and robust infrastructure development. We are seeking a highly motivated and talented Machine Learning Engineer to join our dynamic and growing team. You will play a pivotal role in designing, developing, and deploying machine learning models that power our advanced security products and services. This is an incredible opportunity to make a real world impact by building intelligent systems that detect and prevent advanced threats, enhance critical security processes, and protect Apple and our customers. 
 
Description 
 
The Security ML Engineer will bring their expertise in machine learning to the problems and opportunities facing Information Security at Apple. You will contribute to the Autonomous Security program by developing production ready AI/ML systems using Apple&rsquo;s internal platforms, cloud services, and local compute environments. 
 
You will translate research to design, building and deploying machine learning models for security use cases, leveraging generative AI, statistical modeling, reinforcement learning, and data science to address complex security challenges. You will collaborate with cross-functional teams including security teams, software engineers, and researchers to prototype and scale AI/ML driven security solutions. You will own end-to-end ML workflows: data exploration, model development, evaluation metrics design, deployment, and monitoring. 
 
Preferred Qualifications 
 
Ph.D. in a technical field such as Computer Science, Engineering, Statistics, or related disciplines. 
 
In-depth knowledge of ML algorithms, including supervised/unsupervised learning, deep learning (CNNs, RNNs, LSTMs), and large language models. 
 
Industry experience in deploying ML and generative AI solutions in cybersecurity contexts. 
 
Familiarity with cloud platforms (e.g., AWS, GCP) and their security offerings is a plus. 
 
Experience with large scale data processing and analysis using tools such as Apache Spark. 
 
Experience working in a key security process, such as Incident Response, Threat Intelligence, or Vulnerability Management. 
 
Experience with specific security tools and technologies (e.g., SIEM, IDS/IPS, endpoint security solutions). 
 
Contributions to open-source security or machine learning projects. 
 
Publications or talks at top-tier ML or security conferences. 
 
Additional proficiency in C++ or Swift is a plus 
 
Minimum Qualifications 
 
BSc or Masters degree in Machine Learning, Data Science, Computer Science, Information Security, Mathematics, Statistics, or related field. 
 
Strong programming skills in Python and Scala; experience with ML libraries such as TensorFlow, PyTorch, HuggingFace, and Scikit-learn. 
 
Hands-on experience with full ML model lifecycle: from experimentation to deployment and monitoring. 
 
Solid grasp of security fundamentals including network security, incident response, threat modeling, and vulnerability management. 
 
Excellent written and verbal communication skills, with the ability to present technical concepts clearly to varied audiences. 
 
Familiarity with CI/CD workflows and ML pipelines . 
 
Experience operating, and scaling production services in cloud native environments. 
 
Experience deploying models on CUDA devices using tools like TensorFlow or Torch. 
 
Proven experience building generative AI applications for real-world use cases. 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
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 $139,500 and $258,100, 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.

Machine Learning Engineer, Information Security

At Apple, we believe that technology should enrich people's lives in meaningful ways. Apple Services-including the App Store, Apple Music, Apple TV+, and Podcasts-connect millions of users worldwide to the content and experiences they love. Behind these services are sophisticated AI and machine learning systems that power personalized recommendations, intelligent search, and content discovery. The quality of these experiences depends fundamentally on the integrity of the data that fuels them. 
 
Join the Human-centered AI (HAI) organization within Apple Services Engineering as a Senior Applied Scientist on our Data Quality Operations team. This is your opportunity to shape the future of data quality at Apple, working at the intersection of cutting-edge machine learning, large language models, and rigorous scientific methodology. You'll be at the forefront of designing and implementing novel, scalable quality control solutions which ensure our AI-powered services meet Apple's exacting standards for a high-quality user experience. If you're passionate about applying scientific rigor to real-world problems, thrive on innovation, and want your work to impact hundreds of millions of users, this role offers an exceptional opportunity to make a lasting contribution to products people use every day. 
 
Description 
 
We are seeking a Senior Applied Scientist to lead the design and implementation of novel, scalable quality control solutions for Apple Services. In this role, you will develop AI or ML solutions for programatically generating gold standard datasets, create anomaly detection systems, and pioneer use of Large Language Models for automated quality control. You will work closely with cross-functional teams to ensure the data powering our AI/ML systems meets the highest standards of accuracy, consistency, and relevance. 
 
&quot;,&quot;responsibilities&quot;:&quot;Develop methodologies and systems to programatically generating gold standard datasets to measure and improve data quality across diverse content types. 
 
Build and validate LLM-based grading systems that align with human judgment for tasks including content classification, quality assessment, and metadata validation. 
 
Create and deploy anomaly detection systems to proactively identify data inconsistencies, biases, and errors across production pipelines. 
 
Collaborate with AI/ML engineering, product, and data science teams to identify quality challenges and integrate solutions into production workflows. 
 
Stay abreast of the latest advancements in machine learning, AI, data quality, and LLM research, applying relevant techniques to enhance our capabilities. 
 
Mentor and guide junior team members, fostering technical excellence and innovation in data quality operations. 
 
Communicate findings and recommendations clearly to technical and non-technical stakeholders, including senior leadership. 
 
Preferred Qualifications 
 
PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, NLP or a related quantitative field with 3+ years of relevant industry experience, 
 
Experience with data labeling operations, annotation quality frameworks, or human-in-the-loop systems. 
 
Familiarity with prompt optimization frameworks or automated prompt engineering. 
 
Experience in natural language processing (NLP) or natural language understanding (NLU). 
 
Experience with MLOps practices and tools for deploying, monitoring, and managing ML models in production. 
 
Familiarity with cloud platforms (e.g., AWS, GCP, Azure) or large-scale distributed infrastructure systems. 
 
Prior experience contributing to open-source projects or publishing research in relevant academic conferences or journals. 
 
Minimum Qualifications 
 
3+ years of industry experience in an Applied Scientist, Machine Learning Engineer, or Data Scientist role with focus on data quality, evaluation, or related areas. 
 
Proven experience designing, building, and deploying scalable data quality or evaluation systems in production environments. 
 
Strong hands-on experience with Large Language Models (LLMs) including prompt engineering, fine-tuning, and applications such as grading, validation, or classification. 
 
Proficiency in Python, data science libraries (pandas, NumPy, scikit-learn, PyTorch, TensorFlow), SQL, and distributed computing environments (Spark, Hadoop). 
 
MS in Computer Science, Machine Learning, Statistics, Applied Mathematics, NLP or a related quantitative field with 3+ years of relevant industry experience, OR BS degree in a related quantitative field with 8+ years of relevant industry experience. 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
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 $171,600 and $258,100, 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/ML Evaluation Quality, Apple Services Engineering

Cerebro

<div>
 
 Are you passionate about advancing the frontier of AI? Do you thrive at the intersection of cutting-edge research and real-world impact? A fast-growing AI startup is looking for exceptional AI researchers and engineers with expertise in
 
 multimodal models
 
 to join their world-class team in
 
 Seattle, WA
 
 .
 
 
 
 About Us
 
 
 
 We are a well-funded startup backed by top-tier investors, with a mission to bring real-time AI avatars to life. Our team includes experts with advanced degrees from leading institutions and extensive experience at top tech companies. With significant funding and a clear vision, we are scaling rapidly and looking for top talent to help operationalize cutting-edge research into transformative products.
 
 
 
 What You&rsquo;ll Do
 
 
 
 As an
 
 AI Research Engineer
 
 , you&rsquo;ll play a pivotal role in bridging the gap between research and production. Your responsibilities will include:
 
 <ul>
 <li>
 
 Operationalizing Research
 
 : Collaborate with researchers to transition models from experimental checkpoints to production-ready systems. Establish scalable patterns for large-scale training, rapid experimentation, and deployment of new architectures.
 </li>
 <li>
 
 Optimizing Model Performance
 
 : Profile and enhance model inference for latency and throughput using techniques like quantization, pruning, distillation, and architectural refinements to ensure cost-effective scalability.
 </li>
 <li>
 
 Model Acceleration
 
 : Apply optimization techniques (e.g., TensorRT, ONNX, vLLM) to accelerate multimodal models, including video diffusion, large language models (LLMs), and speech models.
 </li>
 <li>
 
 Designing Data Pipelines
 
 : Build efficient pipelines for video data ingestion, preprocessing, and training at
 
 petabyte scale
 
 using tools like Dagster and Ray.
 </li>
 <li>
 
 Evaluating and Iterating
 
 : Develop evaluation frameworks to measure model quality, establish benchmarks, and guide continuous improvement of model capabilities.
 </li>
 </ul>
 
 
 What We&rsquo;re Looking For
 
 
 
 We&rsquo;re seeking candidates with a strong foundation in AI research and production engineering. The ideal candidate will have:
 
 <ul>
 <li>
 
 Production ML Experience
 
 : Proven track record of deploying ML models to production, with a deep understanding of common failure modes (e.g., resource contention, OOMs, batch optimization) and how to address them.
 </li>
 <li>
 
 Deep Learning Expertise
 
 : Strong knowledge of PyTorch and modern ML architectures, with experience training and optimizing large models (e.g., transformers, diffusion models).
 </li>
 <li>
 
 Systems Proficiency
 
 : Comfort working with GPUs, debugging CUDA issues, and profiling model workloads to identify compute or memory bottlenecks.
 </li>
 <li>
 
 Data Engineering Skills
 
 : Experience building scalable data pipelines for high-bandwidth media processing and training workflows.
 </li>
 </ul>
 
 
 Preferred Qualifications
 
 
 <ul>
 <li>
 Experience with
 
 video or audio models
 
 in research or production settings.
 </li>
 <li>
 Familiarity with
 
 low-level optimization
 
 (e.g., CUDA kernels, Triton, custom operators).
 </li>
 <li>
 Knowledge of
 
 real-time ML systems
 
 and latency-critical inference.
 </li>
 <li>
 Prior work with
 
 model compression techniques
 
 (quantization, distillation, pruning).
 </li>
 </ul>
 
 
 Why Join Us?
 
 
 <ul>
 <li>
 
 Impactful Work
 
 : Your contributions will directly shape the future of real-time AI avatars, advancing cutting-edge research into products that touch millions of lives.
 </li>
 <li>
 
 World-Class Team
 
 : Collaborate with some of the brightest minds in AI, including researchers and engineers from top institutions and companies.
 </li>
 <li>
 
 Growth Opportunities
 
 : Be part of a fast-growing startup with significant funding and a clear vision for the future.
 </li>
 <li>
 
 In-Person Collaboration
 
 : Work alongside your teammates in our vibrant
 
 Seattle HQ
 
 , fostering creativity and innovation through daily collaboration.
 </li>
 </ul>
 
 
 How to Apply
 
 
 
 If you&rsquo;re excited about the opportunity to work on
 
 multimodal AI models
 
 and help bring groundbreaking research to life, we&rsquo;d love to hear from you! Apply now to join a team shaping the future of AI.
 
</div>

Artificial Intelligence Researcher

<div>
 
 Summary
 
 
 
 Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!
 
 
 As part of Apple's AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges.
 
 
 
 Description
 
 
 
 As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state of the art research in ML to tackle complex data problems. The solutions you develop will significantly impact Apple Intelligence, future Apple products and the broader ML development ecosystem.
 
 
 You will work with a multidisciplinary team to actively participate in Apple Intelligence&rsquo;s data-model co-design and co-develop practice. Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple. You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models.
 
 
 Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.
 
 
 Your work may span a variety of directions, including but not limited to:
 
 
 Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data.
 
 
 Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets.
 
 
 Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models.
 
 
 Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data.
 
 
 Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments.
 
 
 Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering.
 
 
 
 Minimum Qualifications
 
 
 
 <ul>
 <li>
 Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning.
 </li>
 <li>
 Deep understanding in multi-modal foundation models.
 </li>
 <li>
 Staying on top of emerging trends in generative AI and multi-modal LLM.
 </li>
 <li>
 Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 3+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality.
 </li>
 <li>
 Strong publication record in relevant conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, , etc)
 </li>
 <li>
 Strong problem-solving and communication skills.
 </li>
 <li>
 Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas
 
 
 </li>
 </ul>
 
 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 $147,400 and $272,100, 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&rsquo;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&rsquo;s Employee Stock Purchase Plan. You&rsquo;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 &mdash; 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.
 
 
 Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
 
 
 Apple accepts applications to this posting on an ongoing basis.
 
 
</div>

AIML - Machine Learning Engineer, Data and ML Innovation

GEICO

<div>At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities.
Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers&rsquo; expectations while making a real impact for our company through our shared purpose.
When you join our company, we want you to feel valued, supported and proud to work here. That&rsquo;s why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers.
At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers' expectations while making a real impact for our company through our shared purpose. When you join our company, we want you to feel valued, supported and proud to work here. That's why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers
GEICO is seeking a Senior Staff Machine Learning Engineer to help shape how Generative AI enhances customer and associate experiences across the enterprise. This is a hands-on technical role who will be leading the strategy, architecture, and delivery of ML systems for the Claims organization&mdash;designing predictive models, robust data/feature pipelines, and production-grade MLOps to drive measurable business outcomes. 
You will work alongside engineering teams, data scientists, and product leaders to design, build, and integrate AI-powered capabilities that automate workflows, improve decision-making, and elevate user experience. You will contribute to a culture of learning, curiosity, and innovation while growing your expertise in cutting-edge AI technologies
About the role<ul><li>
Staff+ individual contributor role focused on end-to-end ML: data and feature engineering, modeling, deployment, monitoring, and continuous improvement.</li><li>
Partner with Claims Operations, Product, and Engineering to deliver ML capabilities such as severity/triage predictions, claim outcome forecasting, and automation accelerators.</li><li>
GenAI (e.g., LLMs and agentic workflows) may be leveraged where it augments ML systems; strong ML depth is primary.</li></ul>
What you&rsquo;ll do<ul><li>
Own ML platform architecture: data/feature pipelines, experiment tracking, model registries, serving layers, offline/online evaluation, and observability.</li><li>
Define standards for reliability, performance, cost efficiency, security, governance, and model risk management across ML services.</li><li>
Lead design and implementation of models across classical ML and deep learning (e.g., gradient boosted trees, sequence models, Transformers for tabular/time-series/NLP where relevant).</li><li>
Translate business goals into measurable ML objectives and experiment plans; ensure robust offline metrics and real-world impact.</li><li>
Build scalable training and inference pipelines; establish CI/CD for ML, automated evaluations, canary releases, and rollback strategies.</li><li>
Implement monitoring for data quality, drift, fairness, latency, reliability, and cost; lead incident response and postmortems.</li><li>
Partner with Claims, Product, Data Science, Platform/SRE, Security, and Legal/Compliance to gather requirements, define scope, and prioritize backlogs.</li><li>
Maintain pragmatic technical roadmaps balancing business outcomes, release timelines, and engineering excellence.</li><li>
Own build-vs-buy decisions and tooling/service selection (speed to market, extensibility, TCO); guide platform evolution with clear architectural principles.</li><li>
Lead experienced engineers through complex platform implementations; drive system-wide architectural improvements and reliability practices.</li><li>
Mentor engineers and junior tech leads; codify best practices; contribute to internal documentation and promote enterprise-wide ML standards.</li><li>
Where appropriate, collaborate on retrieval-augmented workflows, prompt/context management, and LLM evaluation and safety guardrails to complement ML systems.</li></ul>
Minimum qualifications<ul><li>
Bachelor&rsquo;s degree or above in Computer Science, Engineering, Statistics, or related field.</li><li>
10+ years of professional software development experience using at least two general-purpose languages (e.g., Java, C++, Python, C#).</li><li>
10+ years architecting, designing, and building multi-component ML platforms leveraging open-source/cloud-agnostic components:<ul><li>
Search/vector: ElasticSearch, Qdrant (as applicable to ML features and retrieval)</li><li>
Data warehouse/lakehouse: Snowflake; familiarity with Parquet/Delta/Iceberg</li><li>
Streaming: Kafka; plus Flink/Spark Streaming experience</li><li>
Datastores: PostgreSQL; NoSQL (MongoDB, Cassandra)</li><li>
Distributed compute: Spark, Ray</li><li>
Workflow orchestration: Airflow, Temporal</li></ul></li><li>
6+ years managing end-to-end SDLC for ML systems: version control, CI/CD, Kubernetes, testing (unit/integration/data/ML eval), monitoring/alerting, production support.</li><li>
6+ years working with cloud providers (Azure and/or AWS) in production ML contexts.</li></ul>
Preferred qualifications (GenAI as a plus)<ul><li>
Experience leveraging or fine-tuning LLMs (e.g., GPT, Llama, Mistral, Claude) to augment ML workflows, retrieval, or claims-facing tooling.</li><li>
Hands-on with MLOps tooling: MLflow/Kubeflow, model registries, feature stores (e.g., Feast), experiment tracking, A/B testing and online evaluation frameworks.</li><li>
Observability: Prometheus/Grafana, OpenTelemetry; SLO-driven operations and incident management.</li><li>
Model safety, fairness, explainability (e.g., SHAP/LIME), and regulatory compliance; familiarity with model risk management practices.</li><li>
Insurance/financial services domain experience: claims automation, fraud detection, risk modeling, subrogation, severity/triage, and regulatory stewardship.</li><li>
Experience with high-throughput, low-latency inference and real-time feature pipelines.</li></ul>
#LI-JK1 
Annual Salary
$150,000.00 - $300,000.00
The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate&rsquo;s work experience, education and training, the work location as well as market and business considerations. 

GEICO will consider sponsoring a new qualified applicant for employment authorization for this position. 
The GEICO Pledge:
Great Company: At GEICO, we help our customers through life&rsquo;s twists and turns. Our mission is to protect people when they need it most and we&rsquo;re constantly evolving to stay ahead of their needs.
We&rsquo;re an iconic brand that thrives on innovation, exceeding our customers&rsquo; expectations and enabling our collective success. From day one, you&rsquo;ll take on exciting challenges that help you grow and collaborate with dynamic teams who want to make a positive impact on people&rsquo;s lives.
Great Careers: We offer a career where you can learn, grow, and thrive through personalized development programs, created with your career &ndash; and your potential &ndash; in mind. You&rsquo;ll have access to industry leading training, certification assistance, career mentorship and coaching with supportive leaders at all levels.
Great Culture: We foster an inclusive culture of shared success, rooted in integrity, a bias for action and a winning mindset. Grounded by our core values, we have an an established culture of caring, inclusion, and belonging, that values different perspectives. Our teams are led by dynamic, multi-faceted teams led by supportive leaders, driven by performance excellence and unified under a shared purpose.
As part of our culture, we also offer employee engagement and recognition programs that reward the positive impact our work makes on the lives of our customers.
Great Rewards: We offer compensation and benefits built to enhance your physical well-being, mental and emotional health and financial future.<ul><li>
Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family&rsquo;s overall well-being.</li><li>
Financial benefits including market-competitive compensation; a 401K savings plan vested from day one that offers a 6% match; performance and recognition-based incentives; and tuition assistance.</li><li>
Access to additional benefits like mental healthcare as well as fertility and adoption assistance.</li><li>
Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year.</li></ul>
The equal employment opportunity policy of the GEICO Companies provides for a fair and equal employment opportunity for all associates and job applicants regardless of race, color, religious creed, national origin, ancestry, age, gender, pregnancy, sexual orientation, gender identity, marital status, familial status, disability or genetic information, in compliance with applicable federal, state and local law. GEICO hires and promotes individuals solely on the basis of their qualifications for the job to be filled.
GEICO reasonably accommodates qualified individuals with disabilities to enable them to receive equal employment opportunity and/or perform the essential functions of the job, unless the accommodation would impose an undue hardship to the Company. This applies to all applicants and associates. GEICO also provides a work environment in which each associate is able to be productive and work to the best of their ability. We do not condone or tolerate an atmosphere of intimidation or harassment. We expect and require the cooperation of all associates in maintaining an atmosphere free from discrimination and harassment with mutual respect by and for all associates and applicants.</div>

Senior Staff Machine Learning Engineer

The Applied Machine Learning team builds production multimodal systems that understand and transform large-scale image, audio, and video content. Our work spans diffusion-based image generation, transcription and diarization, face and object detection, OCR and image description for search, and automated quality control of media pipelines. 
 
We are looking for a Staff Machine Learning Engineer to strengthen our diffusion and video models, adapt small and mid-sized LLMs, and turn our uniquely large corpus of weakly labeled media into a durable product advantage. This is an opportunity to shape the next generation of multimodal experiences end-to-end, from data and models to evaluation and user impact. 
 
Description 
 
The Staff Machine Learning Engineer - Multimodal Generation &amp; Post-Training will be a senior individual contributor on a small, applied ML team focused on production multimodal systems. The role will lead fine-tuning and adaptation of diffusion and emerging video models, as well as post-training of small and medium LLMs for captioning, moderation, and retrieval-friendly descriptions. 
 
The engineer will design data and evaluation workflows that use our large archive of weakly labeled music, podcast, film, TV, and short-form content to drive measurable quality and efficiency improvements. The role includes close collaboration with partner infra teams for model serving and with adjacent product and research groups to bring new capabilities into production. 
 
Preferred Qualifications 
 
PhD in Computer Science, Machine Learning, or a related technical field. 
 
8+ years of industry experience with production multimodal systems spanning image, audio, and/or video. 
 
Deep expertise with diffusion and video generation techniques (e.g., ControlNet/IP-Adapter, temporal consistency methods, sampling and latency optimization). 
 
Experience with PEFT/QLoRA and post-training approaches such as DPO or related preference-based methods for small and mid-sized LLMs. 
 
Background in ASR/VAD/diarization, OCR, multimodal retrieval, or face recognition with fine-grained temporal alignment. 
 
Familiarity collaborating with infra/platform teams on model serving (e.g., batching strategies, quantization, observability) and translating requirements into reliable production deployments. 
 
Demonstrated ability to define metrics, build evaluation harnesses, and communicate results clearly to cross-functional partners. 
 
Track record of publications, patents, or open-source contributions in relevant areas of machine learning or multimodal modeling. 
 
Minimum Qualifications 
 
Master&rsquo;s degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience. 
 
5+ years of hands-on industry experience building and shipping machine learning systems to production. 
 
Proven experience training and fine-tuning diffusion or other image/video generative models, including adapter-based methods such as LoRA. 
 
Proficiency in Python and at least one major deep learning framework such as PyTorch. 
 
Experience designing and operating ML pipelines for noisy or weakly labeled data, including offline evaluation and monitoring in production. 
 
Strong software engineering skills, including code quality, experimentation discipline, and debugging/profiling of model performance. 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
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 $201,300 and $302,200, 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.

Staff Machine Learning Engineer, Apple Services Engineering

Explore 140+ Applied ml engineer ai ml evaluation simulation in Seattle this March. Find roles across top companies and apply now to boost your career.