ml systems engineer

LATENT

<div><h1 class="jobSectionHeader"><b>Machine Learning Engineer</b></h1><p></p><h2 class="jobSectionHeader"><b>
About Latent Health</b></h2><p>
Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family.</p><p>
For everyone else, care is fragmented and impersonal.</p><p>
Medical history is scattered across systems that don&rsquo;t communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly&mdash;but not the individual.</p><p>
We believe this can be fundamentally rebuilt.</p><p>
At Latent Health, we are building systems that understand both:</p><ul><li><p>
the <b>population</b> (clinical knowledge at scale)</p></li><li><p><br>
and the <b>individual</b> (longitudinal patient history)</p></li></ul><p><br>
Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning.</p><p>
Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth.</p><p></p><h2 class="jobSectionHeader"><b>
ML at Latent Health</b></h2><p>
The Machine Learning team is responsible for building systems that run in real clinical workflows.</p><p>
We work on:</p><ul><li><p>
Verifiable reinforcement learning at scale</p></li><li><p><br>
Mid-training and post-training of foundation models</p></li><li><p><br>
Novel objectives derived from longitudinal patient data</p></li></ul><p><br>
We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production.</p><p>
We are a small team and expect engineers to take ownership of critical systems, not components.</p><p></p><h2 class="jobSectionHeader"><b>
The Role</b></h2><p>
As a Machine Learning Engineer, you will <b>own the design, development, and operation of production-grade ML systems</b> that run in real clinical workflows.</p><p>
You will drive systems from ambiguous problem definition through to reliable production deployment, setting technical direction along the way.</p><p>
We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end.</p><p>
This role involves owning systems that directly impact real patient outcomes.</p><p></p><h2 class="jobSectionHeader"><b>
What You&rsquo;ll Do</b></h2><ul><li><p>
Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure</p></li><li><p><br>
Train and fine-tune large language models (LLMs) for:</p><ul><li><p><br>
Clinical reasoning</p></li><li><p><br>
Medical question answering</p></li><li><p><br>
Evidence-grounded generation</p></li></ul></li><li><p><br>
Make and own tradeoffs across <b>accuracy, latency, cost, and safety</b> in high-stakes production environments</p></li><li><p><br>
Develop evaluation frameworks to ensure model safety and clinical validity</p></li><li><p><br>
Integrate ML systems into product workflows and patient-facing applications</p></li><li><p><br>
Monitor system performance in production and iterate based on real-world usage and feedback</p></li><li><p><br>
Define what &ldquo;correct&rdquo; means in ambiguous clinical workflows in collaboration with engineers and clinicians</p></li></ul><p></p><h2 class="jobSectionHeader"><b><br>
What We&rsquo;re Looking For</b></h2><ul><li><p>
Strong foundation in machine learning and software engineering</p></li><li><p><br>
Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered</p></li><li><p><br>
Experience driving ambiguous ML problems from 0 1, including problem formulation, model design, and productionization</p></li><li><p><br>
Hands-on experience with PyTorch or similar frameworks</p></li><li><p><br>
Ability to operate independently in high-ambiguity environments with minimal guidance</p></li><li><p><br>
Strong product and engineering judgment &mdash; you know when to use ML, when not to, and how to scope problems accordingly</p></li><li><p><br>
Comfort working in a fast-moving, early-stage environment</p></li><li><p><br>
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)</p></li></ul><p></p><h2 class="jobSectionHeader"><b><br>
Nice to Have</b></h2><ul><li><p>
Experience deploying LLMs in production environments</p></li><li><p><br>
Experience building distributed systems or large-scale data pipelines</p></li><li><p><br>
Experience working with clinical, biomedical, or other regulated datasets</p></li></ul><p></p><h2 class="jobSectionHeader"><b><br>
Why Join Latent Health</b></h2><ul><li><p>
Work on high-stakes problems with real impact on patient care</p></li><li><p><br>
Build systems that define how AI is trusted in clinical decision-making</p></li><li><p><br>
Significant ownership in a small, high-caliber team</p></li><li><p><br>
Competitive compensation and meaningful equity</p></li></ul><p></p><h2 class="jobSectionHeader"><b><br>
Location</b></h2><p>
We are based in San Francisco and work together in person.</p><p>
We spend most of the week in the office and prioritize candidates who are excited to work this way.</p><p></p><h2 class="jobSectionHeader"><b>
Compensation</b></h2><ul><li><p>
Base salary: $225,000 &ndash; $300,000+</p></li><li><p><br>
Meaningful equity in an early-stage, Series A company</p></li></ul><p></p><h2 class="jobSectionHeader"><b><br>
Closing</b></h2><p>
If you&rsquo;re interested in building systems that bring truly personalized healthcare to millions of patients, we&rsquo;d love to talk.</p></div><p></p>

Machine Learning Engineer

Capital One

<div>Lead Machine Learning Engineer<p></p><p><b>
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You&rsquo;ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You&rsquo;ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.</b></p><p></p><p><b>
What you&rsquo;ll do in the role:</b></p><p><b>
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:</b></p><ul><li><p><b>
Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</b></p></li><li><p><b>
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</b></p></li><li><p><b>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</b></p></li><li><p><b>
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</b></p></li><li><p><b>
Retrain, maintain, and monitor models in production.</b></p></li><li><p><b>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</b></p></li><li><p><b>
Construct optimized data pipelines to feed ML models.</b></p></li><li><p><b>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</b></p></li><li><p><b>
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</b></p></li><li><p><b>
Use programming languages like Python, Scala, or Java.</b></p></li></ul><p></p><p><b>
Basic Qualifications:</b></p><ul><li><p><b>
Bachelor&rsquo;s Degree</b></p></li><li><p><b>
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</b></p></li><li><p><b>
At least 4 years of experience programming with Python, Scala, or Java</b></p></li><li><p><b>
At least 2 years of experience building, scaling, and optimizing ML systems</b></p></li></ul><p></p><p><b>
Preferred Qualifications:</b></p><ul><li><p><b>
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field</b></p></li><li><p><b>
3+ years of experience building production-ready data pipelines that feed ML models</b></p></li><li><p><b>
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow</b></p></li><li><p><b>
2+ years of experience developing performant, resilient, and maintainable code</b></p></li><li><p><b>
2+ years of experience with data gathering and preparation for ML models</b></p></li><li><p><b>
2+ years of people leader experience</b></p></li><li><p><b>
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation</b></p></li><li><p><b>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</b></p></li><li><p><b>
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance</b></p></li><li><p><b>
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents</b></p></li></ul><p><b><i>
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).</i></b></p><p></p><p>
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.</p><p></p>
McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer<p></p><p><br>
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.</p><p></p>
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.<p></p><p>
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.</p><p></p>
This role is expected to accept applications for a minimum of 5 business days.<p></p>
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.<p></p><p>
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.</p><p></p><p>
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com</p><p></p><p>
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.</p><p></p><p>
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</p></div>

Lead Machine Learning Engineer

Base-2 Solutions, LLC

<div><div><div><div><div><ul><li><b>Requisition ID:</b> 2471</li><li><b>
Standard Title:</b> Senior Systems Engineer</li><li><b>
Required Security Clearance:</b> Top Secret/SCI with Full Scope Polygraph</li><li><b>
Location:</b> Annapolis Junction, MD</li><li><b>
Work Type:</b> On-Site</li><li><b>
Shift:</b> First</li><li><b>
Referral Eligibility:</b> Eligible</li><li><b>
U.S. Citizenship Required?</b> Yes</li></ul>
</div></div></div></div><div><b>Pay Range</b>
</div><div><b>$209,047.49 - $236,920.49 Annually
</b><br>
</div><p></p><div><h2 class="jobSectionHeader"><b>Position Summary</b></h2><p>
Base-2 Solutions is seeking a Systems Engineer 4 to apply systems engineering principles across the full system life cycle, supporting technical integrity, quality, and completeness of work products and deliverables. This role supports AI-focused cloud and platform engineering efforts, with emphasis on AWS, AI/ML systems, security, and data lifecycle management.</p><h2 class="jobSectionHeader"><b>
Essential Duties and Responsibilities</b></h2><ul><li>
Apply systems engineering principles throughout the system life cycle phases of concept, development, production, utilization, support, and retirement.</li><li>
Interface with the Government regarding systems engineering technical considerations and associated problems, issues, or conflicts.</li><li>
Communicate with program personnel, government overseers, and senior executives.</li><li>
Maintain responsibility for the technical integrity, quality, and completeness of work performed and deliverables associated with one or more of the 25 ISO/IEC 15288 process areas.</li><li>
Support technical process areas including stakeholder requirements definition, requirements analysis, architectural design, implementation, integration, verification, transition, validation, operation, maintenance, and disposal.</li><li>
Support project process areas including project planning, project assessment and control, decision management, risk management, configuration management, information management, and measurement.</li><li>
Support enterprise process areas including project portfolio management, infrastructure management, lifecycle model management, human resource management, and quality management.</li><li>
Support agreement process areas including acquisition and supply.</li></ul><h2 class="jobSectionHeader"><b>
Required Qualifications</b></h2><ul><li>
Active TS/SCI with Full Scope Polygraph.</li><li>
Experience in systems engineering sufficient to meet one of the stated education/experience pathways.</li><li>
Experience applying systems engineering principles across the system life cycle.</li><li>
Ability to support technical integrity, quality, and completeness of systems engineering deliverables.</li><li>
Ability to communicate effectively with Government stakeholders, program personnel, and senior executives.</li><li>
Experience with cloud architecture, cloud engineering, or platform engineering, especially in AWS.</li><li>
Experience with AI/ML system design and implementation.</li><li>
High-level understanding of the AI lifecycle, including development, training, inference, and monitoring.</li><li>
Experience with ML pipeline development, model deployment, and DevOps/MLOps.</li><li>
Experience with system security and networking, particularly implementation of system security on classified systems.</li><li>
Experience with data engineering and data lifecycle management.</li><li>
Excellent communication and collaboration skills with varied stakeholder groups and the ability to proactively engage them to understand requirements and inform system architecture decisions.</li></ul><h2 class="jobSectionHeader"><b>
Preferred Qualifications</b></h2><ul><li>
Experience with Amazon SageMaker.</li></ul><h2 class="jobSectionHeader"><b>
Required Education and Experience Equivalency</b></h2><p><div><div><div><b>
Education</b></div><div><b> Years of Experience</b></div></div><br>
<div><div>
High School Diploma/GED</div><div> 14</div></div><br>
<div><div>
Associates Degree</div><div> 14</div></div><br>
<div><div>
Bachelors&rsquo; Degree</div><div> 10</div></div><br>
<div><div>
Masters&rsquo; Degree</div><div> 8</div></div><br>
<div><div>
PhD</div><div> 8</div></div><br>
</div></p><h2 class="jobSectionHeader"><b>
Required Certifications</b></h2><ul><li>
None specified.</li></ul><h2 class="jobSectionHeader"><b>
Required Security Clearance</b></h2><ul><li>
Active TS/SCI with Full Scope Polygraph.</li></ul>
</div><p></p><div><div><div><div><h2 class="jobSectionHeader"><b>Pay &amp; Benefit Highlights</b></h2><h2 class="jobSectionHeader"><b>
Compensation</b></h2><ul><li>
Competitive fixed salary or hourly pay (based on experience, skills, location, and internal equity).</li><li>
Employee referral bonuses up to $10,000 per hired referral.</li><li>
Additional bonus opportunities for exceptional performance and contributions to business development and company growth (role-dependent).</li></ul><h2 class="jobSectionHeader"><b>
Health</b></h2><ul><li>
100% company-paid medical premiums for employees and eligible dependents.</li><li>
Choose from multiple plan options with CareFirst, Kaiser, and UnitedHealthcare, including PPO, POS, HMO, and HSA-compatible plans.</li><li>
100% company-paid dental premiums for employees and eligible dependents.</li><li>
100% company-paid vision premiums for employees and eligible dependents.</li></ul><h2 class="jobSectionHeader"><b>
Income Protection</b></h2><ul><li>
100% company-paid premiums for short-term disability.</li><li>
100% company-paid premiums for long-term disability.</li><li>
100% company-paid premiums for accidental death &amp; dismemberment (AD&amp;D).</li><li>
100% company-paid premiums for life insurance up to $200,000.</li></ul><h2 class="jobSectionHeader"><b>
Retirement</b></h2><ul><li>
401(k) with immediate vesting: 4% company match plus a 4% non-elective company contribution (8% total).</li><li>
401(k) pre-tax and Roth options.</li></ul><h2 class="jobSectionHeader"><b>
Leave</b></h2><ul><li>
Up to 20 days of flexible paid time off (PTO).</li><li>
11 paid floating holidays.</li></ul><h2 class="jobSectionHeader"><b>
Work-Life Balance</b></h2><ul><li>
Flexible work schedules, including flex time and compressed work periods (contract and project-dependent).</li></ul></div></div></div></div></div><p></p>

Systems Engineer 4 (AI-focused)

Encora Inc.

<div>
 We are seeking an
 <strong>
  AI/ML Engineer &ndash; Agents
 </strong>
 to help build the core intelligence layer of our AI-powered MarTech and AdTech platform. This role will focus on designing, developing, and deploying
 <strong>
  agentic AI systems
 </strong>
 ,
 <strong>
  LLM-powered applications
 </strong>
 , and
 <strong>
  real-time inference pipelines
 </strong>
 that drive intelligent automation, personalization, and decision-making at scale.
 <br/>
 <br/>
 This is a high-impact, hands-on engineering role for someone who thrives in fast-moving environments and wants to help shape next-generation AI products from the ground up.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Design and deploy agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or similar.
  </li>
  <li>
   Build and scale LLM/GenAI applications for MarTech and AdTech use cases, including content generation, campaign optimization, audience segmentation, personalization, and workflow automation.
  </li>
  <li>
   Fine-tune and optimize LLMs/SLMs for domain-specific tasks such as classification, extraction, recommendation, and intent understanding.
  </li>
  <li>
   Develop and manage training and inference pipelines for production-scale ML systems.
  </li>
  <li>
   Build scalable infrastructure for real-time and batch inference, experimentation, and deployment.
  </li>
  <li>
   Implement best practices for evaluation, A/B testing, model monitoring, and continuous improvement.
  </li>
  <li>
   Partner closely with product, data, and engineering teams to turn prototypes and research into production-ready AI services.
  </li>
  <li>
   Rapidly prototype and apply emerging research to practical product use cases.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   <strong>
    3+ years
   </strong>
   of experience in AI/ML engineering, machine learning, or applied NLP roles.
  </li>
  <li>
   Master&rsquo;s degree in Computer Science, Engineering, or a related field required; PhD preferred.
  </li>
  <li>
   Hands-on experience with agent-based AI frameworks such as LangGraph, AutoGen, CrewAI, or similar.
  </li>
  <li>
   Strong expertise in deep learning and natural language processing using frameworks such as PyTorch or TensorFlow.
  </li>
  <li>
   Experience fine-tuning and deploying foundation models such as LLaMA, Mistral, or similar.
  </li>
  <li>
   Strong experience with Hugging Face, LoRA, PEFT, and modern fine-tuning workflows.
  </li>
  <li>
   Experience with RAG pipelines, vector databases, semantic search, and embedding optimization tools such as FAISS, Pinecone, or PGVector.
  </li>
  <li>
   Experience deploying ML systems in cloud environments such as AWS, GCP, or Azure.
  </li>
  <li>
   Strong software engineering skills in Python, APIs, microservices, and containerized environments such as Docker and Kubernetes.
  </li>
  <li>
   Solid understanding of GPU optimization, distributed training, and inference performance tuning.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience in MarTech, AdTech, personalization, targeting, or attribution systems.
  </li>
  <li>
   Experience with real-time decisioning or ad-serving systems.
  </li>
  <li>
   Contributions to open-source AI/LLM frameworks or published research.
  </li>
  <li>
   Prior experience in a startup or high-growth environment.
  </li>
 </ul>
</div>


AI/ML Engineer

Arta Finance

<div><h2 class="jobSectionHeader"><b>The Company</b></h2><p>
Arta is on an audacious and incredibly rewarding mission: to pave the way for people everywhere to lead more successful financial lives. Arta leverages AI and sophisticated digital tools&mdash;once reserved for ultra-high-net-worth individuals&mdash;and makes them accessible to a broader global audience. Think of it as your own digital family office, combining intelligent investment strategies, alternative assets, private market access, and smart automation to help you grow and protect your wealth effortlessly. We value trust, teamwork, and adaptability. Think: intelligent investing, personalized portfolios, and real-time trading, all backed by robust data infrastructure.</p><p></p><h2 class="jobSectionHeader"><b>
The Role</b></h2><p>
Arta is building the AI infrastructure for the next generation of wealth management.</p><p>
We partner with leading financial institutions to power strategic initiatives that create real competitive advantage, particularly in making high-quality, personalised advice scalable.</p><p><b>
Our platform enables intelligent agents to operate across core advisory workflows, from client servicing and suitability to portfolio research and analysis.</b> These systems run in live, regulated environments and are embedded into how institutions serve their clients day to day.</p><p></p><h2 class="jobSectionHeader"><b>
What You Will Do</b></h2><p>
You will design and build <b>production-ready agent systems</b> that sit at the core of how financial decisions are supported and delivered.</p><p><b>
Build the AI Agent Platform</b></p><ul><li><p>
Design and implement <b>agent architectures</b> (tool use, planning, memory, orchestration)</p></li><li><p>
Build systems for <b>LLM orchestration</b>, prompt management, and workflow execution</p></li><li><p>
Develop <b>evaluation frameworks</b> <b>for agent quality, reliability, and safety</b></p></li><li><p>
Create benchmarking pipelines to measure model and system performance over time</p></li></ul><p><b>
Enable Enterprise Deployment</b></p><ul><li><p>
Build infrastructure for <b>self-hosted and multi-tenant deployments</b></p></li><li><p>
Design systems that operate under enterprise constraints (security, latency, cost)</p></li><li><p>
Develop APIs and platform abstractions for external partners</p></li></ul><p><b>
Bridge Research Production</b></p><ul><li><p>
Translate rapidly evolving LLM capabilities into stable, production-ready systems</p></li><li><p>
Partner with ML and product teams to integrate agents into real financial workflows</p></li><li><p>
Improve reliability, observability, and failure handling of agent systems</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
Who You Are</b></h2><ul><li><p>
5+ years building <b>production ML systems or backend systems for ML-powered products</b></p></li><li><p>
Hands-on experience with <b>LLMs, agent frameworks, or applied ML systems</b></p></li><li><p>
Strong <b>Python </b>skills and experience with modern ML tooling</p></li></ul><ul><li><p>
Experience with <b>agent systems, tool use, or LLM orchestration frameworks</b></p></li><li><p>
Experience building <b>evaluation / benchmarking systems for ML or LLMs</b></p></li><li><p>
Experience designing systems beyond notebooks &mdash; APIs, services, pipelines</p></li><li><p>
Strong systems thinking: latency, reliability, failure modes, tradeoffs</p></li><li><p><b>
Location:</b> You are located in or have a plan to relocate to<b> the Bay area</b>.
</p></li></ul><h3 class="jobSectionHeader"><b>Strong Plus</b></h3><ul><li><p>
Experience with <b>self-hosted models or enterprise AI deployments</b></p></li><li><p>
Background in distributed systems or data infrastructure</p></li><li><p>
Exposure to financial systems or high-stakes domains</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
What Makes This Role Different</b></h2><ul><li><p>
This is not a research or prototype-focused role.</p></li><li><p>
You will be responsible for <b>shipping systems that operate in live financial environments.</b> Your work directly supports institutional clients and real end users at some of the largest and fastest-growing financial institutions, not internal demos.</p></li><li><p><b>
If you&rsquo;re motivated by making agent systems work reliably at scale, in complex and regulated settings, this role will be a strong fit.</b></p></li></ul><p></p><h2 class="jobSectionHeader"><b>
Interview Process</b></h2><ul><li><p>
Introduction with Head of Talent, 30m</p></li><li><p>
Domain Knowledge Interview, 60m
</p><ul><li><p>Machine Learning &amp; Evaluation
</p></li></ul></li><li><p>Technical Interview 1: Coding/Algorithm/Data Structures, 60m
</p></li><li><p>Technical Interview 2: System Design &amp; Domain Knowledge, 60m
</p></li><li><p>Co-founder Interview with Head of AI, 30m
</p></li><li><ul><li>We may request you to complete a take-home assignment if necessary.</li></ul></li></ul><h3 class="jobSectionHeader"><b><i>
Note: We require at least one in-person interview before making our offer decision. For remotely located candidates, we may request you to visit the Mountain View HQ to meet the team. Depending on your location, you will meet our team member based in NY/New Jersey.</i></b></h3><p></p><h3 class="jobSectionHeader"><b><i>
Interview Integrity Notice</i></b><br>
<b><i>To ensure a fair and accurate assessment, candidates are expected to complete all interview exercises independently, without the use of external assistance or AI tools. Arta may, with your consent, request that you share your full screen during technical portions of the interview to verify your work environment. Interviewers may also, with your consent, ask you to temporarily disable virtual backgrounds or filters to confirm your identity and maintain interview integrity. These steps are voluntary, used only for real-time verification, and do not involve recording, storing, or accessing any information beyond what you choose to display during the session.</i></b></h3><p></p><h2 class="jobSectionHeader"><b>
What We Offer</b></h2><ul><li><p>
A competitive salary and benefits package, with ample opportunities for growth and advancement</p></li><li><p>
A vibrant and dynamic work environment where innovation, collaboration, and continuous learning are highly valued</p></li><li><p>
The opportunity to work with a diverse and talented team of industry experts, passionate about shaping the future of finance</p></li><li><p>
Robust health insurance offering for you and your family</p></li><li><p>
High deductible health plan available with health savings account contribution</p></li><li><p>
20 weeks of parental leave</p></li><li><p>
17 days PTO annually</p></li></ul><p></p><p>
Arta's Compensation Philosophy</p><p>
We determine your salary based on factors including your interview performance, job-related skills, experience, and relevant education or training. Our offers are based on salary bands that are updated periodically using market benchmarks and consider geographic location as well (for example, higher cost regions like San Francisco or New York). If you are presented with an offer, we will review the base salary, benefits, number of options, notional option value and strike price. We would like to know if you accept our offer within 7 days.<br>
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Please keep in mind that the equity portion of your offer is not included in these numbers and represents a significant part of your total compensation.<br>
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IC I: $110,000-$180,000<br>
IC II: $160,000-$230,000<br>
IC III: $180,000-$300,000</p></div>

Machine Learning Engineer, AI Agent Platform

Amazon.com

<div><h2 class="jobSectionHeader"><b>DESCRIPTION</b></h2><div>
We&rsquo;re building foundational large language model capabilities for Amazon Stores that combine general world knowledge with Amazon&rsquo;s e-commerce domain expertise to create more intuitive, conversational, and personalized shopping experiences for our customers. We&rsquo;re looking for pioneers who are passionate about technology, innovation, and customer experience, and who want to make a lasting impact in a rapidly evolving space. You&rsquo;ll work alongside talented scientists and engineers to invent on behalf of customers and unlock the next generation of LLM-powered shopping experiences.<br>
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If you&rsquo;re excited about working at the intersection of large-scale ML systems, post-training and inference optimization, and customer-facing innovation, this is a unique opportunity to join a dynamic team shaping the future of AI at Amazon.<br>
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Key job responsibilities<br>
In this role, you will leverage your engineering expertise to develop and optimize generative AI systems for shopping. On a day-to-day basis, you will:<br>
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<ul><li>Design and optimize high-performance kernels, custom operators, and low-level acceleration techniques that maximize hardware utilization and reduce computational overhead for LLM training and inference.</li><li>Drive improvements in memory management, parallel computing, kernel fusion, attention optimization, and matrix multiplication efficiency to reduce latency and increase throughput at scale.</li><li>Partner closely with applied scientists, engineering teams and product managers to define requirements, support experimentation, and deliver production-ready systems.</li><li>Move quickly in ambiguous environments, make thoughtful short- and long-term trade-offs, and deliver incrementally across a wide range of technologies, from distributed data processing to ML infrastructure and kernel-level optimization.</li><li>Develop tooling to accelerate experimentation, improve observability, and generate insights across model quality, latency, throughput, and efficiency metrics.</li></ul></div><h2 class="jobSectionHeader"><b>
BASIC QUALIFICATIONS</b></h2><ul><li>5+ years of non-internship professional software development experience</li><li>5+ years of programming with at least one software programming language experience</li><li>5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Experience as a mentor, tech lead or leading an engineering team</li><li>Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications</li></ul><h2 class="jobSectionHeader"><b>
PREFERRED QUALIFICATIONS</b></h2><ul><li>5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience</li><li>Bachelor's degree in computer science or equivalent</li><li>Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in computer architecture</li><li>Experience with CUDA kernels or ML/low-level kernels</li><li>Experience with vLLM, SGLang, TensorRT or similar platforms in production environments, or experience working with PyTorch or JAX software</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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Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.<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, CA, Palo Alto - 193,300.00 - 261,500.00 USD annually<br>
USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually</div><p></p>

Sr. Software Dev Engineer, Stores Foundational AI -SFAI

Apple

<p>The Apple Ray team is seeking a Senior / Staff Software Engineer with strong distributed systems expertise and a solid background in machine learning. In this hybrid role, you will design and build core components of Apple&rsquo;s unified data+ML platform powered by open-source Ray, while also partnering with ML teams to ensure the platform meets the needs of large-scale training and inference workloads.<br>
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You will contribute to the distributed runtime, orchestration layer, and system APIs that power Apple&rsquo;s intelligent features across products and services. This role is ideal for a software engineer who enjoys low-level systems work but is also fluent in ML workflows and models at scale.<br>
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<b>Description</b><br>
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Apple Ray integrates deeply with Apple&rsquo;s data and ML ecosystem to provide a unified platform for building, orchestrating, and scaling complex ML and data pipelines. As a Software Engineer with ML background, you will design distributed systems that support large-scale model training, tuning, and inference across heterogeneous compute environments-from bare-metal GPU clusters to cloud-native infrastructure.<br>
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You will build features that enhance developer productivity for ML engineers, improve resource efficiency, and advance the performance and reliability of Apple&rsquo;s ML workloads. You&rsquo;ll collaborate closely with ML practitioners to translate model and pipeline needs into robust platform capabilities, while also improving the underlying distributed runtime and control plane.<br>
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This role requires strong engineering fundamentals, hands-on experience with ML systems, and a passion for building scalable infrastructure.&quot;,&quot;responsibilities&quot;:&quot;Build scalable distributed systems and platform components using Ray that power Apple&rsquo;s data+ML workflows.<br>
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Develop APIs, libraries, and services that improve the efficiency and usability of large-scale ML training and inference pipelines.<br>
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Optimize performance and resource utilization across GPU/CPU clusters for ML workloads running at Apple scale.<br>
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Collaborate with ML teams to understand model and pipeline needs and translate them into robust platform features.<br>
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Design fault-tolerant orchestration mechanisms, autoscaling strategies, and runtime improvements for distributed ML jobs.<br>
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Diagnose complex issues across distributed systems and ML pipelines to ensure reliability and availability.<br>
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Improve observability, monitoring, and debugging capabilities targeted at ML-centric distributed workloads.<br>
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Contribute to architectural decisions and, where appropriate, upstream enhancements to Ray and related tools.<br>
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<b>Preferred Qualifications</b><br>
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Experience with distributed training frameworks (DeepSpeed, Horovod, FSDP, ZeRO).<br>
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Background in optimizing GPU workloads or performance benchmarking.<br>
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Experience with model orchestration systems or ML platforms.<br>
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Contributions to open-source ML or distributed systems projects.<br>
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Familiarity with large-scale data systems such as Spark, Flink, or similar.<br>
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<b>Minimum Qualifications</b><br>
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6+ years building distributed systems, high-scale backend services, or compute runtimes.<br>
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Solid background in ML workflows, model training, model serving, or data pipeline development.<br>
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Proficiency in Python, plus strong experience in a systems-level language (C++, Rust, Go, or Java).<br>
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Experience with ML frameworks such as PyTorch or TensorFlow and familiarity with GPU-based training.<br>
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Understanding of parallelism strategies, model scaling, or distributed training concepts.<br>
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Experience with cluster orchestration (Kubernetes, EKS, GKE) or large-scale compute systems.<br>
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Strong debugging skills across distributed and ML-centric runtime environments.<br>
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Ability to work cross-functionally with ML engineers, data engineers, and infrastructure teams.<br>
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B.S., M.S., or Ph.D. in Computer Science, Machine Learning, or related technical fields - or comparable software engineering experience.<br>
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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 .<br>
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<b>Pay &amp; Benefits</b><br>
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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.<br>
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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.<br>
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Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.</p>

Senior ML Engineer, Apple Ray, Apple Data Platform

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