ai engineer

American Express

<div><p>Joining Amex Tech means discovering and shaping your contribution to something big. Here, you can work alongside talented tech teams and build a unique career with the Powerful Backing of American Express. With a range of opportunities to work with the latest technologies, and a commitment to back the broader engineering community through open source, our mission is to power your success. Because Amex Tech is powered by our technology, our culture, and our colleagues.
</p><p>The Technology organization enables and accelerates the company&rsquo;s growth strategies, delivering global capabilities and services in support of Amex&rsquo;s customers and colleagues, while maintaining 24/7 servicing and availability to ensure an uninterrupted, high-quality customer experience. Technology provides the foundation for everything we do in the company while driving differentiation through building and leveraging innovative technology and data insights.
</p><p>American Express Technologies is seeking an Engineer with advanced skills in Generative AI and Data Engineering to join our Production Management tooling team. Our mission is to transform how critical platforms are supported by embedding AI-first solutions into event management and system reliability. We are looking for highly motivated, creative problem solvers who are excited to design and implement innovative AI tools that address complex business challenges at scale.</p></div>
<br><div><ul><li>Demonstrated passion for Generative AI and its application to real-world business problems.
</li><li>Hands-on experience with LLM frameworks (LangChain, LangGraph, or similar) and RAG architectures.
</li><li>Strong programming skills in Python (preferred) and/or Go, with proven ability to build both prototypes and production applications.
</li><li>Solid understanding of data engineering principles, including ETL pipelines, structured/unstructured data management, and vector databases.
</li><li>Experience with machine learning frameworks such as PyTorch, TensorFlow, and Hugging Face.
</li><li>Proficiency in deploying AI-enabled applications using cloud platforms and containerized environments (Kubernetes, Docker, OpenShift).
</li></ul><p><b>Preferred Qualifications
</b></p><ul><li>Experience developing agentic AI applications and integrating multi-step reasoning into enterprise workflows.
</li><li>Strong grounding in data science techniques, including experimentation, model evaluation, and applied statistical methods.
</li><li>Exposure to observability and monitoring tools such as Splunk, Grafana, Dynatrace, and ELK.
</li><li>Familiarity with APIs, microservices, and web technologies (React, NodeJS, SQL) to deliver end-to-end solutions.
</li><li>3&ndash;5 years of relevant work experience with a Bachelor&rsquo;s degree in Computer Science, Data Science, or related field.
</li><li>Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.</li></ul></div>
<br><div><p>At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.
</p><p>As part of Team Amex, you&rsquo;ll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.</p></div>
<br><div><p>We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:
</p><p></p><ul><li>Competitive base salaries
</li><li>Bonus incentives
</li><li>6% Company Match on retirement savings plan
</li><li>Free financial coaching and financial well-being support
</li><li>Comprehensive medical, dental, vision, life insurance, and disability benefits
</li><li>Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
</li><li>20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
</li><li>Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
</li><li>Free and confidential counseling support through our Healthy Minds program
</li><li>Career development and training opportunities
</li></ul><p>For a full list of Team Amex benefits, visit our Colleague Benefits Site .
</p><div>American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. American Express will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable state and local laws, including the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance for Employers, and the City of Los Angeles&rsquo; Fair Chance Initiative for Hiring Ordinance. For positions covered by federal and/or state banking regulations, American Express will comply with such regulations as it relates to the consideration of applicants with criminal convictions.<br>
</div><div></div><br>
<div>We back our colleagues with the support they need to thrive, professionally and personally. That's why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.<br>
</div><div></div><br>
<div>US Job Seekers - Click to view the &ldquo; Know Your Rights &rdquo; poster. If the link does not work, you may access the poster by copying and pasting the following URL in a new browser window: https://www.eeoc.gov/poster.</div></div><div></div>

AI Engineer II

Mercor

<div>
 <strong>
  About The Job
  <br/>
  <br/>
 </strong>
 <strong>
  Mercor
 </strong>
 connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include
 <strong>
  Benchmark
 </strong>
 ,
 <strong>
  General Catalyst
 </strong>
 ,
 <strong>
  Peter Thiel
 </strong>
 ,
 <strong>
  Adam D'Angelo
 </strong>
 ,
 <strong>
  Larry Summers
 </strong>
 , and
 <strong>
  Jack Dorsey
 </strong>
 .
 <br/>
 <br/>
 <strong>
  Position:
 </strong>
 ML Engineer (Coding Agent Experience)
 <br/>
 <br/>
 <strong>
  Type:
 </strong>
 <strong>
  Contract
  <br/>
  <br/>
 </strong>
 <strong>
  Compensation:
 </strong>
 <strong>
  $85/hour
  <br/>
  <br/>
 </strong>
 <strong>
  Location:
 </strong>
 <strong>
  Remote
  <br/>
  <br/>
 </strong>
 <strong>
  Role Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
  </li>
  <li>
   Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
  </li>
  <li>
   Identify bugs, edge cases, performance issues, and failure modes in AI models.
  </li>
  <li>
   Compare outputs from multiple frontier models and assess their strengths and weaknesses.
  </li>
  <li>
   Apply professional engineering judgment to realistic ML engineering scenarios.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <strong>
  Must-Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   2+ years of professional machine learning engineering experience.
  </li>
  <li>
   Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
  </li>
  <li>
   Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
  </li>
  <li>
   Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience deploying ML systems to production.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Compensation &amp; Legal
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   $400 per accepted task
  </li>
  <li>
   Compensation is tied to accepted work.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Application Process (Takes 20&ndash;30 mins to complete)
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Upload resume
  </li>
  <li>
   AI interview based on your resume
  </li>
  <li>
   Submit form
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Resources &amp; Support
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  </li>
  <li>
   For any help or support, reach out to: support@mercor.com
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
 </em>
</div>


AI/ML Engineer - Fully Remote | Upto $85/hr

NYU Langone Health

<div><p>NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.
<br>
<br>
<i>For more information, go to</i> <i>med.nyu.edu</i>, and interact with us on <i>LinkedIn</i>, <i>Glassdoor</i>, <i>Indeed</i>, <i>Facebook</i>, <i>Twitter</i> <i>and</i> <i>Instagram</i>.</p>
<p><b>
Position Summary:</b><br>
We have an exciting opportunity to join our team as a Engineer II - Applied AI.
</p><p>As a ML Engineer, you will support the implementation of diverse Generative AI and Machine Learning initiatives across the health system. You will be responsible for driving specific projects forward in collaboration with the key customers - data scientists, business units and IT partners across the various verticals (corporate services, office of science and research, clinical, and pathology).</p>
<p><b>
Job Responsibilities:</b></p>
<div>
<ul><li>Design and implement AI/ML applications using tools and frameworks from our teams technology stack and following the best practices in software development</li>
</ul></div><div><ul><li>Collaborate with data scientists to build and / or fine-tune high performing AI models and implement modern techniques from relevant published papers</li>
</ul></div><div><ul><li>Develop scalable and robust pipelines to support large data processing tasks</li>
</ul></div><div><ul><li>Implement our internal security and data protection standards in AI applications; assist the team with maintaining emerging AI system risk and security standards</li>
</ul></div><div><ul><li>Support the team in preparation for technical reviews and internal documentation (architecture, IT Security, AI)</li>
</ul></div><div><ul><li>Collaborate with other team members and stakeholders to meet team objectives; develop understanding of business workflows shadow / work with clients to gather feature requirements, identify and document AI opportunities, create appropriate tickets, participate in backlog refinement, execute tickets, participate in code-review activities</li>
</ul></div><div><ul><li>Stay updated with the latest industry trends and advancements in AI and relevant cloud technologies; routinely share and demonstrate learnings with the team</li>
</ul></div><div><ul><li>Provide technical guidance and coaching to less experienced team members</li>
</ul></div><div><ul><li>Participate in all phases of AI software development life cycle, including functional analysis, prototyping, development, evaluation, testing, deployment, refactoring and technical support</li></ul></div><br>
<p>
</p><p><b>Minimum Qualifications:</b><br>
To qualify you must have a 1. Bachelor's degree in computer science, software engineering, or a related field.<br>
2. At least 1-3 years of hands-on experience in AI Solution development<br>
3. Strong programming skills in Python, or other languages commonly used in AI development.<br>
4. Substantial knowledge of AI, machine learning, and deep learning<br>
5. Experience with AI platforms like PyTorch or TensorFlow<br>
6. Experience with building large-scale and/or compute-intensive applications on clusters for data engineering, model training and evaluation (HPC, Spark, Kubernetes)<br>
7. Understanding of software development principles and methodologies, including data structures, data modeling and software architecture.<br>
8. Excellent problem-solving skills and ability to work in a team environment.<br>
9. Excellent communication skills, both verbal and written.</p>
<p><b>
Preferred Qualifications:</b><br>
1. Masters degree in computer science, data science, software engineering, or a related quantitative discipline<br>
2. 3-5 years of hands-on experience in AI Solution development<br>
3. Basic understanding of at least one cloud (Azure, AWS, GCP) and cloud native development tools, and experience using their AI / ML toolchain</p>
<p><i>
Qualified candidates must be able to effectively communicate with all levels of the organization.</i>
<br>
<br>
NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you'll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it's developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family.
<br>
<br>
NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. We require applications to be completed online.
<br>
<br>
View Know Your Rights: Workplace discrimination is illegal.</p>
<p>
NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $84,577.92 - $126,000.00 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.</p>
<p>
To view the Pay Transparency Notice, please click here</p></div>

Machine Learning Engineer

Fusemachines

<div>
 <strong>
  About Fusemachines
  <br/>
  <br/>
 </strong>
 Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 <br/>
 <br/>
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 <br/>
 <br/>
 <strong>
  Type: Full-time, Remote
  <br/>
  <br/>
 </strong>
 <strong>
  Role Overview
  <br/>
  <br/>
 </strong>
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;often in partnership with client stakeholders and internal delivery teams.
 <br/>
 <br/>
 You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Problem Framing &amp; Stakeholder Partnership
  </li>
  <ul>
   <li>
    Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
   </li>
   <li>
    Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
   </li>
  </ul>
  <li>
   Data Analysis &amp; Feature Engineering
  </li>
  <ul>
   <li>
    Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
   </li>
   <li>
    Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
   </li>
   <li>
    Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
   </li>
  </ul>
  <li>
   Model Development (Core ML)
  </li>
  <ul>
   <li>
    Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)
   </li>
   <li>
    Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
   </li>
   <li>
    Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
   </li>
   <li>
    Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
   </li>
   <li>
    Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
   </li>
  </ul>
  <li>
   Deep Learning
  </li>
  <ul>
   <li>
    Build and train deep learning models using PyTorch or TensorFlow/Keras
   </li>
   <li>
    Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
   </li>
  </ul>
  <li>
   Evaluation, Explainability, and Iteration
  </li>
  <ul>
   <li>
    Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
   </li>
   <li>
    Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
   </li>
  </ul>
  <li>
   Productionization &amp; MLOps (Project-Dependent)
  </li>
  <ul>
   <li>
    Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
   </li>
   <li>
    Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
   </li>
  </ul>
  <li>
   Documentation &amp; Communication
  </li>
  <ul>
   <li>
    Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
   </li>
   <li>
    Create documentation and lightweight demos that make results actionable
    <br/>
   </li>
  </ul>
 </ul>
 Success in This Role Looks Like
 <br/>
 <br/>
 <ul>
  <li>
   You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
  </li>
  <li>
   Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
  </li>
  <li>
   Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
  </li>
  <li>
   Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
  </li>
  <li>
   Strong SQL skills (joins, window functions, aggregation, performance awareness)
  </li>
  <li>
   Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
  </li>
  <li>
   Hands-on experience across multiple model types, including:
  </li>
  <ul>
   <li>
    Classification &amp; regression
   </li>
   <li>
    Time series forecasting
   </li>
   <li>
    Clustering/segmentation
   </li>
  </ul>
  <li>
   Experience with deep learning in PyTorch or TensorFlow/Keras
  </li>
  <li>
   Strong problem-solving skills: ability to work with ambiguous goals and messy data
  </li>
  <li>
   Clear communication skills and ability to translate analysis into decisions
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
  </li>
  <li>
   Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
  </li>
  <li>
   Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
  </li>
  <li>
   Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
  </li>
  <li>
   Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
  </li>
  <li>
   Consulting or client-facing delivery experience
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Certifications (Strong Plus)
  <br/>
  <br/>
 </strong>
 Candidates with at least one relevant certification are especially encouraged to apply:
 <br/>
 <br/>
 <ul>
  <li>
   Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
  </li>
  <li>
   Databricks certifications (Data Scientist, Data Engineer, or related)
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice-to-Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
  </li>
  <li>
   Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
  </li>
  <li>
   Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  <em>
   Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
   <br/>
   <br/>
  </em>
 </em>
 Important: Immigration Sponsorship Policy
 <br/>
 <br/>
 Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
 <br/>
 <br/>
 <ul>
  <li>
   Direct Company Sponsorship: Such as H-1B, J-1, or TN visas
  </li>
  <li>
   Employer of Record: Listing Fusemachines as the immigration employer on any government documentation
  </li>
  <li>
   Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT)
   <br/>
   <br/>
  </li>
 </ul>
 Powered by JazzHR
 <br/>
 <br/>
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</div>


Machine Learning Engineer / Data Scientist

Giga

<div>
 <strong>
  About Giga
  <br/>
  <br/>
 </strong>
 Giga has recently raised a $61M Series A and has several paying customers, including DoorDash. We&rsquo;re building the next generation of customer experience &mdash; real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world&rsquo;s largest enterprises.
 <br/>
 <br/>
 It&rsquo;s an exciting inflection point for the company. While we have been successful, we have larger ambitions. Our goal is to become the go-to AI platform for all enterprise automation, powered by our voice superintelligence. To achieve this, we need more great engineers.
 <br/>
 <br/>
 The work affects millions of people every day and our engineers have autonomy and make true impact. This opportunity is unique because we have brilliant founders, have found commercial success, and see a clear path to becoming a generational company. Some further info about us:
 <br/>
 <br/>
 <ul>
  <li>
   Voice AI startup Giga raises $61M Series A
  </li>
  <li>
   DoorDash and Giga Partnership
   <br/>
   <br/>
  </li>
 </ul>
 Giga builds AI agents trusted by the largest B2C companies in the world. Industry leaders like DoorDash trust Giga with their most complex support and operations workflows across voice, chat, and email. If being a part of this resonates with you, please apply!
 <br/>
 <br/>
 <strong>
  The Role
  <br/>
  <br/>
 </strong>
 We're looking for software engineers to help build the systems that power our AI agents. You'll work across the backend, from data pipelines and integrations to agent infrastructure, shipping features alongside experienced engineers who will help you grow.
 <br/>
 <br/>
 This is a role where you'll contribute to real problems from day one. We expect you to ramp up quickly, take ownership of your work, and operate with increasing independence as you learn the codebase.
 <br/>
 <br/>
 <strong>
  What You'll Work On
  <br/>
  <br/>
 </strong>
 You'll contribute to projects across our stack. Some examples:
 <br/>
 <br/>
 <ul>
  <li>
   Atlas: Building features for our AI assistant: charts and alerts in Slack, natural language queries, and expanding Atlas to manage platform resources
  </li>
  <li>
   Activity Stream: Log visualization with filters, timestamps, and frequency charts to give visibility into agent behavior
  </li>
  <li>
   Dynamic knowledge: Adding time-based knowledge (like ongoing incidents) that auto-updates from sources like status pages
  </li>
  <li>
   Agent memory: Conversation awareness and recent interaction lookups so agents remember context across sessions
   <br/>
   <br/>
  </li>
 </ul>
 As an early-career engineer, you'll be paired with senior engineers on larger initiatives while also owning smaller projects end-to-end as you ramp up.
 <br/>
 <br/>
 <strong>
  You Might Be a Fit If You
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Have 1-3 years of professional software engineering experience
  </li>
  <li>
   Are curious and ask good questions&mdash;you want to understand why things work, not just how
  </li>
  <li>
   Can take feedback well and iterate quickly
  </li>
  <li>
   Prefer shipping over perfection but still care about quality
  </li>
  <li>
   Want to grow at a startup where you'll have real responsibility early
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  Perks &amp; Benefits
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   Competitive base + bonus + equity
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   Catered lunch daily
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   Dinner stipend
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   $500/month wellness &amp; commuter benefit (gym, fitness classes, mental health)
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   401(k) plan
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   Medical, dental, and vision coverage
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 </ul>
 Giga is an equal opportunity employer. We're committed to providing equal employment opportunities regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law.
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 Compensation Range: $160K - $250K
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</div>


Software Engineer I / II

Traba

<div><p>Traba is the AI operating layer for the industrial supply chain.</p><div></div><p><br>
We started in workforce&mdash;temp staffing, the biggest operational pain point for the manufacturing and logistics customers we serve&mdash;and used it to embed ourselves inside their daily operations and create a far better customer experience through technology.</p><div></div><p><br>
Now those same customers are pulling us beyond staffing into the broader operational workflows that run their facilities. That foundation gave us proprietary data from millions of shifts and deep enterprise relationships.</p><div></div><p><br>
But our edge is more than data: by connecting to the systems running across every facility and activating the workers already on our platform to execute against them, we are building applied AI that drives real productivity gains and transforms how the global supply chain operates at scale.<br>
<br>
We are backed by Founders Fund, Khosla Ventures, and General Catalyst.</p><div></div><p><b><br>
You'll build the AI agents themselves&mdash;the harnesses, evals, orchestration, and model strategy the rest of Traba's product runs on.</b> We're seeking an entrepreneurial AI Agent Engineer to join as a founding member of the Agents team and build the next layer of Traba's product&mdash;an agentic platform that synthesizes the data flowing through our marketplace and operates autonomously inside our customers' supply chain workflows. (Our Applied AI team puts these agents to work across the product; you own the agents and infrastructure they depend on.) You'll embed deeply in our customers' operations, design and ship production agent harnesses on top of frontier models, and turn what you learn in the field into durable product. You will partner closely with our CTO, product, and our operators to take 0-to-1 ideas to scaled deployments.</p><div><br>
</div><h2 class="jobSectionHeader"><b>
About You</b></h2><ul><li><p><b>
Builder with an AI operator's instinct.</b> You've shipped real product on top of LLMs&mdash;not just chat wrappers. You've designed agent harnesses, structured tools, written evals, and tuned prompts against production traces. You think in capability, reliability, and unit economics&mdash;not just whether the model says the right thing.</p></li><li><p><b>
Domain-immersed.</b> You enjoy time with the people who do the work&mdash;learning an industry's vocabulary, edge cases, and operational tempo&mdash;and let that shape what you build.</p></li><li><p><b>
High-agency in ambiguity.</b> Dropped into a fuzzy customer problem with a half-formed hypothesis and a deadline, you scope, build, evaluate, and ship without waiting for a spec.</p></li><li><p><b>
Sweat both ends of the stack.</b> You move between prompt iteration, eval design, backend services, and customer-facing UI in the same week, and you care about evals that catch regressions and traces that are easy to debug.</p></li></ul><div></div><div><br>
</div><h2 class="jobSectionHeader"><b>
You Will</b></h2><ul><li><p>
Embed with customers and operators to understand how supply chains run today&mdash;then design and ship agents that take meaningful work off their plate.</p></li><li><p>
Build production agent systems on frontier LLMs: tool use, sub-agents, retrieval, structured outputs, MCP servers, and the orchestration that ties them together.</p></li><li><p>
Own evaluation as a first-class discipline&mdash;datasets from real traces, rubrics and graders, experiments, and improvements you can prove move the needle.</p></li><li><p>
Architect the data, services, and APIs the agent layer depends on&mdash;integrating our internal systems with customers' WMS, TMS, and ERP environments.</p></li><li><p>
Codify repeatable deployment patterns so each new customer rollout is faster than the last.</p></li></ul><div></div><div><br>
</div><h2 class="jobSectionHeader"><b>
You Have</b></h2><ul><li><p>
1+ years of software engineering experience, with a track record of shipping LLM- or agent-based features to production.</p></li><li><p>
Strong in Python and/or TypeScript/Node.js, and comfortable designing APIs, distributed systems, and data models in PostgreSQL.</p></li><li><p>
Hands-on with the modern agent stack: production-scale prompt engineering, evaluation frameworks, orchestration patterns, and frontier model APIs.</p></li><li><p>
A track record of building in fast, ambiguous environments&mdash;ideally at a vertical AI, AI-agent, forward-deployed, or data-product company.</p></li><li><p>
Excellent written and verbal communication&mdash;you can run a customer workshop in the morning and write a clean design doc in the afternoon.</p></li></ul><div></div><div><br>
</div><h2 class="jobSectionHeader"><b>
Benefits</b></h2><ul><li><p>
Start-up equity</p></li><li><p>
Competitive Salary</p></li><li><p>
100% Paid health, dental &amp; vision coverage</p></li><li><p>
Dinner Provided via DoorDash, free DashPass &amp; stocked kitchen for NY employees</p></li><li><p>
Commuter benefit</p></li><li><p>
Gympass Benefit</p></li><li><p>
✚✚ Additional: One Medical Membership, Gympass, HSA via Optum, Talkspace, HealthAdvocate, Teledoc Health</p></li></ul><div><br>
</div><h2 class="jobSectionHeader"><b>
Salary Range Details</b></h2><p>
The compensation range for this position is set between $140,000 and $200,000, reflecting our market analysis and other relevant considerations. However, exceptions may be made for candidates with qualifications that significantly differ from those outlined in the job description.</p><div><br>
</div><h2 class="jobSectionHeader"><b>
Equal Opportunity Employer</b></h2><p>
Traba is dedicated to promoting Equal Opportunity employment practices. We evaluate all applicants without discrimination based on race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other characteristics that are legally protected by local, state, or federal regulations. We encourage applicants from a variety of backgrounds, experiences, and skill sets.</p><div><br>
</div><h2 class="jobSectionHeader"><b>
Our Values</b></h2><p><b>
Dream Big -</b> We are on a path to change the world for the better. We create and communicate a bold direction that inspires a life-changing vision. We don&rsquo;t sacrifice long-term value for short-term results.</p><p><b>
Olympian&rsquo;s Work Ethic -</b> Changing the world never comes easy. We work harder, longer, and smarter, not just two out of three. We put everything we have on the field.</p><p><b>
Growth Mindset -</b> We confront the toughest challenges head-on and persevere. Sometimes we fail, but we brush ourselves off, adapt, learn, and push forward with resilience.</p><p><b>
Customer Obsession -</b> We go the extra mile for our workers and businesses. We remain focused on delivering high-quality products and services that solve this massive and overlooked industries&rsquo; problems</p><div></div><p><br>
Job Applicant Privacy Notice</p><p>
Compensation Range: $140K - $200K</p></div>

Software Engineer (AI Agents)

Databricks

<div><h1 class="jobSectionHeader"><b>Staff Machine Learning Engineer, CustomerLake (ML/LLM)</b></h1>
<p><i>RDQ427R109</i></p><p><br>
</p><p>At Databricks, we are passionate about enabling data teams to solve the world's toughest problems &mdash; from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best Data Intelligence Platform so our customers can use deep data insights to improve their business. Founded by engineers &mdash; and customer obsessed &mdash; we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.</p><p><br>
</p><p>As one of the first engineers in the NYC Engineering office, you'll join a small, nimble team building new products from the ground up. We're building CustomerLake, the Customer Data Platform on Databricks, to bring enterprise-grade ML and AI personalization to every company whose data already lives on Databricks. The best B2C and B2B brands have historically relied on in-house ML/AI teams to power personalization, recommendations, churn and lifetime-value modeling, and audience targeting. Our goal is to deliver that same capability to companies that don't have an in-house team but already have their data in order on Databricks. This is a true 0-to-1 environment, combining the excitement of a startup with the resources of a tech leader like Databricks.</p><p><br>
</p><p><b>The impact you'll have:</b></p>
<ul><li>Evaluate ML and LLM approaches for CustomerLake's personalization use cases, push the models and algorithms forward, and continuously improve quality over time</li>
<li>Go deep on how models behave in production: inspect individual traces, understand how the models reason, and tune and improve from there</li>
<li>Build the platform and evaluation framework that let CustomerLake customers optimize for real business value such as purchases, retention, and product usage, not vanity metrics like email opens and clicks</li>
<li>Push the team toward new directions and novel methods worth tackling, not just optimizing what already exists</li>
<li>Partner closely with product management, engineering, and design to turn ambiguous customer problems into scalable, trustworthy solutions</li>
<li>Set the technical foundation and best practices for our ML/AI personalization work as we grow this into several roles across our products over the next 1-2 years</li></ul><br>
<p></p><p><b>What we look for:</b></p>
<ul><li>10+ years of engineering experience, with a strong foundation across the full loop of shipping and improving ML/AI products</li>
<li>Hands-on experience building and evaluating ML models and/or LLM systems for real product or business use cases; your understanding is practical, not purely academic, and you can make models work well inside a product</li>
<li>Experience with personalization based on customer behavior (ideal) or transactions (acceptable), such as recommendations, targeting, churn, or lifetime-value modeling</li>
<li>Proficiency in Python and modern ML frameworks (e.g., PyTorch), with hands-on experience in model evaluation and monitoring AI quality in production</li>
<li>Familiarity with LLMs and generative AI, including techniques like retrieval-augmented generation (RAG), prompt design, fine-tuning, and evaluation</li>
<li>A demonstrated product mindset, with the ability to translate ambiguous customer problems into scrappy MVPs and iterate quickly based on data and user feedback</li>
<li>High ownership and bias for action in 0-to-1 environments: comfortable making pragmatic trade-offs, operating with incomplete information, and driving projects from idea through launch and adoption</li></ul><br>
<p></p><p><b>Nice to have:</b></p>
<ul><li>Experience in martech, ideally a go-to-market or business use case with an analytical (rather than purely transactional) angle</li>
<li>An academic or research background that can help us innovate and develop novel methods</li></ul><br>
<p></p><div><div><div><p><b>Pay Range Transparency</b></p>
<p>Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.<br>
<br>
</p></div><div>Local Pay Range</div>
<div>$192,000&mdash;$260,000 USD
</div></div></div><div><p><b>About Databricks</b></p>
<p>Databricks is the data and AI company. More than 10,000 organizations worldwide &mdash; including Comcast, Cond&eacute; Nast, Grammarly, and over 50% of the Fortune 500 &mdash; rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark&trade;, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
<br>
<br>
<b>Benefits
</b><br>
<br>
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.<br>
<br>
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</p><p><b>Our Commitment to Diversity and Inclusion</b></p>
<p>At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.</p>
<p><b>Compliance</b></p>
<p><b>If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.</b></p></div></div>

Staff Software Engineer - AI/ML

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