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applied ai ml associate

JPMorganChase

<div><b>JOB DESCRIPTION</b><p><br>
As part of the Commercial &amp; Investment Bank, J.P. Morgan Payments enables organizations of all sizes to execute transactions efficiently and securely, transforming the movement of information, money and assets. We tackle complex challenges at every stage of the payment lifecycle and our industry-leading solutions facilitate seamless transactions across borders, industries and platforms. Operating in over 160 countries and handling more than 120 currencies, we are the largest processor of USD payments, with a daily transaction volume of $10 trillion.</p><p>
As a Associate Applied AI/ML Scientist within our Payment Solutions team, you will be instrumental in utilizing artificial intelligence and machine learning technologies to augment our payment solutions and stimulate business expansion. Your role will involve researching, experimenting, developing, and transitioning high-quality machine learning models, services, and platforms into production to streamline payment processes, bolster fraud detection, and enrich customer experience. You will also be tasked with designing and executing highly scalable and dependable data processing pipelines, conducting analysis, and deriving insights to boost and optimize business outcomes. Working in collaboration with cross-functional teams, you will identify opportunities for AI/ML applications within the payments ecosystem.</p><p><b>
Job Responsibilities:</b></p><ul><li>
Actively collaborate with Product, Technology, and other cross-functional teams to gain a deep understanding of complex business problems and formulate data-driven solutions to address these challenges in key areas of the payments&rsquo; domain.</li><li>
Design, develop, and deploy machine learning and AI solutions that meet success metrics aligned with business goals, while considering constraints such as model complexity, scalability, and latency.</li><li>
Partner with Risk and Compliance teams to ensure comprehensive model documentation, track performance metrics, and maintain adherence to regulatory compliance standards.</li><li>
Translate model outcomes into business impact metrics and communicate complex concepts to senior management and stakeholders.</li></ul><p><b>
Required qualifications, capabilities, and skills:</b></p><ul><li>
Bachelor's degree in a quantitative discipline (e.g., Computer Science, Data Science, Mathematics/Statistics, or Operations Research) with a minimum of 3 years of industry experience. Experience with Shell Scripting, Jupyter notebook/Lab, SQL, PySpark, and AWS Cloud Services is required.</li><li>
Proficient in Python with hands-on experience in Machine learning and Deep learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas). Experience with Jupyter Notebook/Lab is essential.</li><li>
3+ years of extensive experience in Natural Language Processing (NLP) or Large Language Models (LLM),or Computer Vision and other machine learning techniques, including classification, regression algorithms.</li><li>
Solid Understanding of algorithms in machine learning, AI, and neural network, including Large Language Models (LLM) and Generative AI as well as familiarity with state-of-the-art practices and advancements in these domains.</li><li>
Proficient in both basic and advanced exploratory data analysis (EDA), with an understanding of the limitations and implications of different methodologies.</li><li>
Ability to set the analytical direction for projects, transforming vague business questions into structured analytical plans. You possess strong cognitive and communication skills, characterized by clear and articulate expression. You excel at identifying core issues, bringing order to chaos, synthesizing insights, and driving decisive outcomes.</li></ul><p><b>
Preferred Qualifications, capabilities and skills</b></p><ul><li>
Experience in the financial services industry, particularly within investment banking operations.</li><li>
Cloud computing: Amazon Web Service, Azure, Docker, Kubernetes, DataBricks, Snowflakes.</li></ul>
<b>ABOUT US</b><br>
<div>
<div>JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world&rsquo;s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
</div><p></p><div><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
</p><div><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.</p>
<p>JPMorgan Chase &amp; Co. is an Equal Opportunity Employer, including Disability/Veterans</p><br>
</div></div></div><br>
<br>
<b>ABOUT THE TEAM</b><br>
<br>
J.P. Morgan&rsquo;s Commercial &amp; Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial &amp; Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.</div>

Applied AI/ML Associate

Middesk

<div>
 <strong>
  About Middesk
  <br/>
  <br/>
 </strong>
 Middesk makes it easier for businesses to work together. Since 2018, we&rsquo;ve been transforming business identity verification, replacing slow, manual processes with seamless access to complete, up-to-date data. Our platform helps companies across industries confidently verify business identities, onboard customers faster, and reduce risk at every stage of the customer lifecycle.
 <br/>
 <br/>
 Middesk came out of Y Combinator, is backed by Sequoia Capital and Accel Partners, and was recently named to Forbes Fintech 50 List.
 <br/>
 <br/>
 <strong>
  The Role:
  <br/>
  <br/>
 </strong>
 We&rsquo;re building AI-driven applications that power business onboarding, fraud prevention, and identity verification. With proprietary data assets and deep domain expertise, we&rsquo;re uniquely positioned to create a new generation of ML-powered solutions for trust and risk.
 <br/>
 <br/>
 We&rsquo;re looking for a
 <strong>
  hands-on Machine Learning Engineer with strong Data Science expertise
 </strong>
 to take end-to-end ownership of the ML lifecycle: from feature design and model development, to deployment, monitoring, and iteration in production. Unlike larger organizations where responsibilities are split, you&rsquo;ll have the opportunity to
 <strong>
  own models from concept to production
 </strong>
 while working closely with product managers, engineers, and data platform teammates who support and amplify your work.
 <br/>
 <br/>
 This is a rare chance to join an
 <strong>
  earlier-stage company
 </strong>
 where you&rsquo;ll have broad visibility and influence, and where your ML systems will have immediate and measurable impact on customers.
 <br/>
 <br/>
 What You&rsquo;ll Do:
 <br/>
 <br/>
 <ul>
  <li>
   End-to-end ML ownership: Lead the full lifecycle of ML systems &mdash; feature engineering, model design, training, evaluation, deployment, monitoring, and iteration.
  </li>
  <li>
   Collaborate with a strong team: Work alongside data engineers, platform engineers, and product teammates who ensure you have the infrastructure, data, and context to deliver.
  </li>
  <li>
   Design &amp; deploy production models: Build high-performance ML applications in risk, fraud, trust &amp; safety, and compliance domains.
  </li>
  <li>
   Keep models healthy in production: Proactively monitor, detect drift, and retrain to ensure long-term performance and reliability.
  </li>
  <li>
   Experiment &amp; learn: Drive online experiments, offline evaluation, and counterfactual analyses to prove impact.
  </li>
  <li>
   Shape ML foundations: Contribute to the feature store, model management, training/serving pipelines, and best practices that scale ML across multiple use cases.
   <br/>
   <br/>
  </li>
 </ul>
 What We&rsquo;re Looking For:
 <br/>
 <br/>
 <ul>
  <li>
   4+ years applied ML experience with proven impact in risk, fraud, trust &amp; safety, compliance, fintech, or other high-stakes domains.
  </li>
  <li>
   Track record of owning ML models end-to-end &mdash; from research and design to deployment, monitoring, and retraining in production.
  </li>
  <li>
   Strong software engineering skills (Python, ML frameworks, deployment pipelines) and ability to write reliable, production-grade code.
  </li>
  <li>
   Hands-on experience with ML infrastructure such as feature stores, model management, training/serving pipelines, and monitoring tools.
  </li>
  <li>
   Comfortable as a senior IC: you can set technical direction, establish best practices, and mentor peers while collaborating effectively across teams.
  </li>
  <li>
   Experience working cross-functionally with data engineers, platform engineers, and product stakeholders to bring ML systems to life.
  </li>
  <li>
   Deep expertise in classification challenges such as imbalanced labels, sparse signals, cold start, and production version management.
   <br/>
   <br/>
  </li>
 </ul>
 Nice to Haves:
 <br/>
 <br/>
 <ul>
  <li>
   B2B SaaS experience, ideally building ML products for enterprise customers.
  </li>
  <li>
   Familiarity with graph, LLM-based feature generation, or AI agent workflows.
  </li>
  <li>
   Experience scaling ML across multiple products or risk domains.
  </li>
 </ul>
</div>


Machine Learning Engineer

Locality

<div><p>Locality is seeking a Senior Data Engineer whom will serve as the primary internal owner of our Audience Engine, spanning data ingestion, identity resolution, automation, and AI enablement. This role emphasizes hands on platform stewardship today, with a clear path to supporting future expansion of the HH Identity Graph and applied AI use cases.
</p><ul><li>Establish and own technical management of the Audience Engine, transitioning knowledge from external partners and building a scalable, internally managed data platform</li><li>
Reduce reliance on third-party vendors while maintaining high standards for platform reliability, uptime, and data integrity across all pipelines and integrations</li>
Architect, own, and continuously optimize Databricks Bronze Silver<li> Gold pipelines to support large scale audience ingestion, enrichment, and activation workflows</li><li>
Ensure the platform is scalable and cost efficient through effective pipeline optimization and resource management</li><li>
Proactively monitor, troubleshoot, and resolve ingestion failures, schema drift, and data quality issues across batch and near real time data pipelines</li><li>
Lead identity resolution across datasets including HHID, DSID, MAID, IP, and campaign logs, improving matching logic to strengthen the Household Identity Graph</li><li>
Guarantee accurate and timely audience publication by maintaining robust downstream data delivery processes and SLAs across activation platforms and partners</li><li>
Own and maintain integrations with external data and activation partners (e.g., Experian, FreeWheel, DSPs), ensuring seamless data exchange and operational reliability</li><li>
Partner cross functionally with Product, Analytics, and Ad Operations teams to onboard new data sources and activation endpoints, enabling new use cases and revenue opportunities</li><li>
Support and operationalize applied AI/ML workflows within Databricks, including integration with tools like Akkio, and translate business needs into production-ready data features and datasets</li><li>
Design and implement scalable data models and query-ready datasets that power BI reporting, audience insights, and predictive modeling, ensuring alignment with downstream analytics requirements</li><li>
Enforce data governance, lineage, and documentation standards while ensuring compliance with privacy, consent, and data usage regulations across all audience and identity data systems</li></ul><ul><li>
6+ years of experience as a Data Engineer or Data Platform Engineer, ideally within advertising</li><li>
Strong hands on experience with Databricks, Spark, and modern cloud data platforms</li><li>
Proven experience designing and maintaining multi-layered data architectures (Bronze / Silver / Gold)</li><li>
Deep expertise in SQL and proficiency in Python or another scripting language</li><li>
Experience working with identity graphs, audience data, and complex join logic across large datasets</li><li>
Hands on experience building and managing automated data pipelines and third-party data integrations</li><li>
Familiarity with adtech, martech, or audience activation platforms, including exposure to identity providers such as Experian or similar vendors</li><li>
Practical experience supporting AI/ML workflows or AI-enabled analytics, with an understanding of data quality, schema design, and performance optimization in cloud-scale environments</li></ul><p>
</p><p><i>Compensation for the role includes a competitive base salary in the range from $75-$100/hr (dependent on years of experience, level of education obtained, as well as applicable skillset), and an excellent benefits package (including but not limited to health care benefits, retirement benefits and paid days off).
</i></p><p></p><p><i>At Locality, we believe our biggest asset is our people. We are proud to be an equal opportunity employer, hiring and developing individuals from diverse backgrounds and experiences to add to our collaborative culture. Locality values and solicits applications from all qualified candidates without regard to actual or perceived race, creed, color, religion, sex, gender, age, physical or mental disability, medical condition, sexual orientation, gender identity and/or expression, alienage or national origin, ancestry, citizenship status, marital status, military or veteran status, genetic information, or any other basis protected by federal, state, or local law.</i></p></div>

Sr. Data Engineer (Contract to Perm)

<div><b>JOB DESCRIPTION</b><div><br>
If you are looking for a game-changing career, working for one of the world's leading financial institutions, you&rsquo;ve come to the right place.</div><p>
As an Applied AIML Engineer, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm&rsquo;s portfolios.</p><p><b>
Job responsibilities</b></p><ul><li><div><div>
Co-Develop and implement LLM-based, machine learning models and algorithms to solve complex operational challenges.</div></div></li><li><div><div>
Design and deploy generative AI applications to automate and optimize business processes.</div></div></li><li><div><div>
Collaborate with stakeholders &amp; Data Scientists to understand business needs and translate them into technical solutions.</div></div></li><li><div><div>
Analyze large datasets to extract actionable insights and drive data-driven decision-making.</div></div></li><li><div><div>
Ensure the scalability and reliability of AI/ML solutions in a production environment.</div></div></li><li><div><div>
Stay up-to-date with the latest advancements in AI/ML technologies &amp; LLMs and integrate them into our operations.</div></div></li><li><div><div>
Mentor and guide junior team members in coding &amp; SDLC standards, AI/ML best practices and methodologies.</div></div></li></ul><div>
</div><div></div><p><b>Required qualifications, capabilities, and skills</b></p><ul><li><div><div>
Master&rsquo;s or Bachelors in Computer Science, Data Science, Machine Learning, or a related field, with a focus on engineering.</div></div></li><li><div><div>
Excellent API design and engineering experience with proven usage of API python frameworks Quart, Flask or FastAPI</div></div></li><li><div><div>
Proficiency in Python &amp; async programming, with a strong emphasis on writing comprehensive test cases using testing frameworks such as pytest to ensure code quality and reliability
</div></div></li><li><div><div>Expertise with Index &amp; Vector DBs such as Opensearch./ElasticSearch</div></div></li><li><div><div>
Extensive experience in deploying AI/ML applications in a production environment, with skills in deploying models on AWS platforms such as SageMaker or Bedrock.</div></div></li><li><div><div>
Champion of MLOps practices, encompassing the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models.</div></div></li><li><div><div>
Experience with generative AI models, including GANs, VAEs, or transformers. Experience with Diffusion models is a plus.</div></div></li><li><div><div>
Solid understanding of data preprocessing, prompt engineering, feature engineering, and model evaluation techniques.</div></div></li><li><div><div>
Proficiency in AI coding tools and editors such as Cursor, Windsurf or CoPilot</div></div></li><li><div><div>
Familiarity in machine learning frameworks such as TensorFlow, PyTorch, PyTorch Lightning, or Scikit-learn.</div></div></li><li><div><div>
Familiarity with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes, Amazon EKS, ECS).</div></div></li></ul><div>
</div><div><b>Preferred qualifications, capabilities, and skills</b></div><ul><li><div><div>
Expertise in cloud storage such as RDS and S3</div></div></li><li><div><div>
Excellent problem-solving skills and the ability to work independently and collaboratively.</div></div></li><li><div><div>
Strong communication skills to effectively convey complex technical concepts to non-technical stakeholders.</div></div></li><li><div><div>
Proven experience in leading projects and teams, with a track record of successful project delivery.</div></div></li></ul>
<b>ABOUT US</b><br>
<div>
<div>JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world&rsquo;s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
</div><p></p><div><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
</p><div><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.</p>
<p>JPMorgan Chase &amp; Co. is an Equal Opportunity Employer, including Disability/Veterans</p><br>
</div></div></div><br>
<br>
<b>ABOUT THE TEAM</b><br>
<br>
J.P. Morgan Asset &amp; Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.</div>

Applied AI/ML Engineer-Vice President

Lyft

<div>
 At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
 <br/>
 <br/>
 Lyft Ads is one of Lyft&rsquo;s newest and fastest-growing businesses, focused on building the world&rsquo;s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey&mdash;before, during, and after a ride&mdash;by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.
 <br/>
 <br/>
 We are seeking an
 <strong>
  Algorithms Scientist
 </strong>
 to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You&rsquo;ll collaborate closely with Engineering, Product, Data Science, and Sales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.
 <br/>
 <br/>
 This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders&mdash;pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.
 <br/>
 <br/>
 Responsibilities:
 <br/>
 <br/>
 <ul>
  <li>
   Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
  </li>
  <li>
   Own the end-to-end lifecycle of modeling projects &mdash; including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
  </li>
  <li>
   Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
  </li>
  <li>
   Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
  </li>
  <li>
   Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
  </li>
  <li>
   Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
  </li>
  <li>
   Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
  </li>
  <li>
   Investigate and resolve model behavior issues, production regressions, calibration drift, and performance anomalies in close partnership with Ads Infra teams.
  </li>
  <li>
   Drive innovation by staying current with advances in ML for ranking, recommendation, causal inference, optimization, and ads measurement &mdash; and proactively identifying opportunities to apply them.
  </li>
  <li>
   Contribute to Lyft Ads&rsquo; modeling and experimentation infrastructure, through model cards, documentation, reproducibility standards, and code quality improvements.
   <br/>
   <br/>
  </li>
 </ul>
 Experience:
 <br/>
 <br/>
 <ul>
  <li>
   Master&rsquo;s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
  </li>
  <li>
   3&ndash;5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains. Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
  </li>
  <li>
   Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
  </li>
  <li>
   Practical experience building and evaluating:
  </li>
  <ul>
   <li>
    Ranking and relevance models
   </li>
   <li>
    Optimization or pacing algorithms
   </li>
   <li>
    Predictive models for CTR, CVR, or user response
   </li>
   <li>
    Causal or experimentation-based measurement methods
   </li>
  </ul>
  <li>
   Understanding of online/offline evaluation techniques, including:
  </li>
  <ul>
   <li>
    Offline metrics (AUC, NDCG, MRR, calibration)
   </li>
   <li>
    A/B testing methodologies
   </li>
   <li>
    Bias correction and counterfactual estimation
   </li>
  </ul>
  <li>
   Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
  </li>
  <li>
   Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
  </li>
  <li>
   Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
  </li>
  <li>
   Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Benefits:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Great medical, dental, and vision insurance options with additional programs available when enrolled
  </li>
  <li>
   Mental health benefits
  </li>
  <li>
   Family building benefits
  </li>
  <li>
   Child care and pet benefits
  </li>
  <li>
   401(k) plan to help save for your future
  </li>
  <li>
   In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
  </li>
  <li>
   18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
  </li>
  <li>
   Subsidized commuter benefits
  </li>
  <li>
   Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
   <br/>
   <br/>
  </li>
 </ul>
 Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.
 <br/>
 <br/>
 Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule &mdash; Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
 <br/>
 <br/>
 The expected base pay range for this position in the New York City area is $128,000 - $160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
 <br/>
 <br/>
 Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
</div>


Data Scientist, Algorithms - Lyft Ads

Amazon Web Services

<div><h2 class="jobSectionHeader"><b>DESCRIPTION</b></h2><div>
The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon&rsquo;s custom machine learning accelerators, Inferentia and Trainium.<br>
<br>
The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance.<br>
<br>
The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration.<br>
<br>
As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology<br>
<br>
You will architect and implement business critical features, and mentor a brilliant team of experienced engineers. We operate in spaces that are very large, yet our teams remain small and agile. There is no blueprint. We're inventing. We're experimenting. It is a very unique learning culture. The team works closely with customers on their model enablement, providing direct support and optimization expertise to ensure their machine learning workloads achieve optimal performance on AWS ML accelerators. The team collaborates with open source ecosystems to provide seamless integration and bring peak performance at scale for customers and developers.<br>
<br>
This role is responsible for development, enablement and performance tuning of a wide variety of LLM model families, including massive scale large language models like the Llama family, DeepSeek and beyond. The Inference Enablement and Acceleration team works side by side with compiler engineers and runtime engineers to create, build and tune distributed inference solutions with Trainium and Inferentia. Experience optimizing inference performance for both latency and throughput on such large models across the stack from system level optimizations through to Pytorch or JAX is a must have.<br>
<br>
You can learn more about Neuron<br>
https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-cc/index.html<br>
https://aws.amazon.com/machine-learning/neuron/<br>
https://github.com/aws/aws-neuron-sdk<br>
https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success<br>
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Key job responsibilities<br>
This role will help lead the efforts in building distributed inference support for Pytorch in the Neuron SDK. This role will tune these models to ensure highest performance and maximize the efficiency of them running on the customer AWS Trainium and Inferentia silicon and servers. Strong software development using Python, System level programming and ML knowledge are both critical to this role. Our engineers collaborate across compiler, runtime, framework, and hardware teams to optimize machine learning workloads for our global customer base. Working at the intersection of software, hardware, and machine learning systems, you'll bring expertise in low-level optimization, system architecture, and ML model acceleration. In this role, you will:<br>
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<ul><li>Design, develop, and optimize machine learning models and frameworks for deployment on custom ML hardware accelerators.</li><li>Participate in all stages of the ML system development lifecycle including distributed computing based architecture design, implementation, performance profiling, hardware-specific optimizations, testing and production deployment.</li><li>Build infrastructure to systematically analyze and onboard multiple models with diverse architecture.</li><li>Design and implement high-performance kernels and features for ML operations, leveraging the Neuron architecture and programming models</li><li>Analyze and optimize system-level performance across multiple generations of Neuron hardware</li><li>Conduct detailed performance analysis using profiling tools to identify and resolve bottlenecks</li><li>Implement optimizations such as fusion, sharding, tiling, and scheduling</li><li>Conduct comprehensive testing, including unit and end-to-end model testing with continuous deployment and releases through pipelines.</li><li>Work directly with customers to enable and optimize their ML models on AWS accelerators</li><li>Collaborate across teams to develop innovative optimization techniques</li></ul><br>
A day in the life<br>
You will collaborate with a cross-functional team of applied scientists, system engineers, and product managers to deliver state-of-the-art inference capabilities for Generative AI applications. Your work will involve debugging performance issues, optimizing memory usage, and shaping the future of Neuron's inference stack across Amazon and the Open Source Community. As you design and code solutions to help our team drive efficiencies in software architecture, you&rsquo;ll create metrics, implement automation and other improvements, and resolve the root cause of software defects.<br>
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You will also build high-impact solutions to deliver to our large customer base and participate in design discussions, code review, and communicate with internal and external stakeholders. You will work cross-functionally to help drive business decisions with your technical input. You will work in a startup-like development environment, where you&rsquo;re always working on the most important initiative.<br>
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About the team<br>
The Inference Enablement and Acceleration team fosters a builder&rsquo;s culture where experimentation is encouraged, and impact is measurable. We emphasize collaboration, technical ownership, and continuous learning. Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we&rsquo;re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Join us to solve some of the most interesting and impactful infrastructure challenges in AI/ML today.</div><h2 class="jobSectionHeader"><b>
BASIC QUALIFICATIONS</b></h2><ul><li>5+ years of non-internship professional software development experience</li><li>Bachelor's degree in computer science or equivalent</li><li>5+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Fundamentals of Machine learning and LLMs, their architecture, training and inference lifecycles along with work experience on optimizations for improving the model execution.</li><li>Software development experience in C++, Python (experience in at least one language is required).</li><li>Strong understanding of system performance, memory management, and parallel computing principles.</li><li>Proficiency in debugging, profiling, and implementing best software engineering practices in large-scale systems.</li></ul><h2 class="jobSectionHeader"><b>
PREFERRED QUALIFICATIONS</b></h2><ul><li>Familiarity with PyTorch, JIT compilation, and AOT tracing.</li><li>Familiarity with CUDA kernels or equivalent ML or low-level kernels.</li><li>Candidates with performant kernel development such as CUTLASS, FlashInfer etc., would be well suited.</li><li>Familiar with syntax and tile-level semantics similar to Triton.</li><li>Experience with online/offline inference serving with vLLM, SGLang, TensorRT or similar platforms in production environments.</li><li>Deep understanding of computer architecture, operation systems level software and working knowledge of parallel computing.</li></ul><br>
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.<br>
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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.<p><br>
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.</p><br>
USA, NY, New York - 184,900.00 - 250,200.00 USD annually<br>
USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually</div><p></p>

Senior Software Development Engineer - AI/ML, AWS Neuron, Multimodal Inference

Optum

<div><p>Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start <b>Caring. Connecting. Growing together.</b></p><p></p><p><br>
Optum AI is UnitedHealth Group's enterprise AI team. We are AI/ML scientists and engineers with deep expertise in AI/ML engineering for health care. We develop AI/ML solutions for the highest impact opportunities across UnitedHealth Group businesses including UnitedHealthcare, Optum Financial, Optum Health, Optum Insight, and Optum Rx. In addition to transforming the health care journey through responsible AI/ML innovation, our charter also includes developing and supporting an enterprise AI/ML development platform.</p><p></p><p><b><br>
Optum AI team members:</b></p><ul><li>
Have impact at scale: We have the data and resources to make an impact at scale. When our solutions are deployed, they have the potential to make health care system work better for everyone</li><li>
Do ground-breaking work: Many of our current projects involve cutting edge ML, NLP and LLM techniques. Generative AI methods for working with structured and unstructured health care data are continuously being developed and improved. We are working in one of the most important frontiers of AI/ML research and development</li><li>
Partner with world-class experts on innovative solutions: Our team members are developing novel AI/ML solutions to business challenges. In some cases, this includes the opportunity to file patents and publish papers about the methods we develop. We also collaborate with AI/ML researchers at some of the world's top universities</li></ul><p></p><p><br>
You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.</p><p></p><p><b><br>
Primary Responsibilities:</b></p><ul><li>
Build and test deep learning and traditional ML models in distributed computing environments</li><li>
Develop robust machine learning pipelines and ML Ops procedures to support production processes</li><li>
Process and transform source data to be used in machine learning pipelines, leveraging distributed computing environments and parallel processing approaches</li><li>
Develop robust modules for evaluation and drift detection</li><li>
Design and plan experiments, conduct statistical analysis and create data visualizations</li><li>
Communicate findings with business stakeholders and collaborate to develop solutions that meet customer needs</li><li>
Develop code and model documentation and assist with model governance approvals</li></ul><p></p><p><br>
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.</p><p><b>
Required Qualifications:</b></p><ul><li>
BS in computer science or a related quantitative field</li><li>
2+ years of AI/ML experience</li><li>
2+ years of experience with Python, classical ML methods and deep learning, including deep learning frameworks such as PyTorch or Tensorflow</li><li>
Experience with LLM frameworks and/or NLP approaches such as text embedding and text classification</li></ul><p></p><p><b><br>
Preferred Qualifications:</b></p><ul><li>
MS or PhD in computer science or related quantitative field</li><li>
Experience with distributed computing approaches such as Spark</li><li>
Experience with Snowflake and other relational databases</li><li>
Experience building Python libraries</li><li>
Experience working on collaborative software projects using GitHub</li><li>
Experience developing simple UIs for POCs demonstrating AI/ML models</li><li>
Proven solid technical communication skills</li></ul><p></p><ul><li><br>
All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy</li></ul><p></p><p><br>
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $91,700 to $163,700 annually based on full-time employment. We comply with all minimum wage laws as applicable.</p><p></p><p><b><br>
Application Deadline: </b>This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.</p><p></p><p><i><br>
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.</i></p><p></p><p><i><br>
UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.</i></p><p></p><p><i><br>
UnitedHealth Group is a drug free workplace. Candidates are required to pass a drug test before beginning employment.</i></p></div>

Senior AI/ML Scientist - Remote

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