Machine Learning Engineer – Inspire11

Elevens, as we call ourselves here, are curiously smart, purposefully scrappy, and dedicated to the highest standard of quality. By being true to ourselves and others, we create a respectful environment that enhances collaboration, drives growth through high expectations, and ensures alignment with our core values. Inspire11 is more than a modern business and technology consulting firm; we're a revolution. We work differently — and it shows. Every move we make is tied to measurable value, and our success aligns with our clients’ success. We partner to simplify the complex, solve sustainably, and stay ahead by continuously pushing our own thinking forward. Value creation isn't something we talk about — it's something we live, every day. 

At Inspire11, we're not just offering a job; we're providing a platform for you to transform the future. You'll feel right at home here if you think differently, build boldly, and lead with impact. We're a place where your unique perspective is valued, your boldness is celebrated, and your potential is unleashed. 

• Growth Through Challenge:  We hold ourselves to the highest standards of excellence and accountability. At Inspire11, we are committed to delivering exceptional results and empowering our employees to take ownership of their work and drive meaningful impact. You'll work alongside elite consultants who challenge you to expand your skills and think even more creatively. 

• The Autonomy to Do Your Best Work: Here, outcomes matter more than outputs. It's about the value we create. You'll have the autonomy to shape your work, drive decisions, and make an impact—not just for our clients but for your growth. You'll be challenged to think strategically, anticipate needs, and solve problems. Bold thinking, creative approaches, and challenging conventional wisdom are part of our DNA. Our clients rely on us to lead them into what's next. 

• Diverse Perspectives Driving Innovative Solutions: We champion inclusivity and authenticity, knowing that diverse perspectives drive better solutions. You'll be encouraged to bring your whole self to work, knowing that's where true innovation happens. We also believe that doing great work should be fun. Our culture thrives on energy, camaraderie, and appreciating the unique contributions that make Inspire11 an amazing place. Our teams get energy from being the best they can be. 

 What you can expect in this role: 

• Designing scalable machine learning infrastructure, including implementing CI/CD pipelines for model deployment
• Monitoring models in production
• Developing Generative AI solutions
• Ensuring security and compliance
• Preprocessing and analyzing datasets
• Developing and fine-tuning ML models to optimize for performance
• Communicating results to stakeholders 

Tools for success: 

• Exhibit strong software engineering principles
• Proficiency in cloud architecture (AWS, Azure, GCP)
• Familiarity with CI/CD and DevOps
• Expertise in data preprocessing and ML techniques
• Experience with model evaluation and libraries like TensorFlow, PyTorch
• Proven experience in ML engineering
• Strong programming skills (Python, R)
• Cloud services and infrastructure knowledge
• Agile methodologies understanding 

Inspire11 is unable to sponsor employment visas at this time. Applicants must be legally authorized to work in the U.S. at the time of application and throughout the duration of employment. 

Inspire11’s Privacy Policy: https://www.inspire11.com/privacy-policy/  

U.S. Compensation & Benefits  

Compensation at Inspire11 
Inspire11 follows Title VII of the Civil Rights Act of 1964, the Equal Pay Act of 1963, the Illinois Human Rights Act, the Illinois Equal Wage Act, and the Illinois Equal Pay Act of 2003. We believe that people performing the same role at the same level, with similar levels of ability or experience, should be similarly compensated. We also believe that the most critical factors driving compensation should be an Eleven’s skill, experience, performance, responsibility, and effort. We may also consider an employee’s length of service, relevant education, training, or certification, and the requirements of a specific job. We may also consider whether the cost of living or competition for talent in specific markets requires adjustments in compensation for employees in those markets. Our goal is to fairly compensate all our Elevens based on these factors within the Company’s financial ability.   

Compensation may be increased or decreased at Inspire11’s discretion based on an employee’s performance reviews, market conditions, and the Company’s financial performance. Inspire11 will always notify an employee in writing before any change is made to their compensation.  

Bonuses 
We may allow employees in Sales, Recruiting, or Leadership to earn periodic bonuses based on sales, revenue, client-specific or practice-specific targets. These targets are communicated to each eligible employee via offer letter or annual update. All such target-based bonuses are calculated based on the Company and an individual’s periodic performance and will only be awarded if: (a) the employee is meeting performance expectations and is an employee in good standing of the Company; (b) the target is met; and (c) the Company’s current financial performance and market conditions permit the bonus to be paid out. All periodic bonuses are discretionary and are earned only when paid out.   

Each year, the Company will decide whether the Company’s financial performance, market performance, and business realities allow it to create an annual bonus pool. If the Company decides that annual bonuses may be awarded, then the Leadership Team will grant annual bonuses to employees in good standing based on the same factors discussed in the previous paragraph while also considering employees’ diligence, performance, expertise, skill, judgment, and experience. To be eligible to participate in this annual bonus pool, an employee must be hired by no later than October 1st of the preceding year. Annual bonuses and merit increases may be prorated based on hire date. Employees eligible for target-based periodic bonuses are not eligible to participate in the annual bonus pool.   

Options or Other Equity in Inspire11 
Inspire11 has a legacy program that allowed certain high-performing employees to purchase shares of the Company through an affiliate. The Company also has a newer program that grants options annually to high-performing employees based on their performance reviews, their managers’ recommendation, and leadership approval. These options have a vesting period associated with them.  

Benefits 
Full-time employees are eligible for the following contributory benefits, where premiums are partially paid by the Company and partially by the employee:   

• Medical insurance plans, 
• Dental insurance plans, and  
• Vision insurance plans.  

Full-time employees are eligible for the following benefits provided wholly by the Company:  

• Short-term disability, providing for a benefit of 60% of an employee’s salary up to a maximum of $2,000 per week for up to 11 weeks after a 7-day waiting period; 
• Long-term disability, providing for 60% of an employee’s salary up to a maximum of $10,000 per month for the time period from the 91st day after a qualifying event through the earlier of the end of the disability or age 67; 
• Life insurance, providing for a benefit of two times the employee’s annual salary, up to a maximum payout of $400,000;   
• A connectivity stipend of $325 annually; 
• Employee Assistance Program, providing for confidential counseling sessions, 24-hour crisis support, online peer support groups, skills training, behavioral health coaching, initial free legal consultations (and discounted rates on referred lawyers), online legal forms, assistance with eldercare and parenting services, among other things.  

Full-time employees are also eligible to participate in the Company’s 401(k) plan, which allows employees to contribute to a standard or Roth IRA. According to a vesting schedule, the Company matches 100% of employee contributions up to 3% of an employee’s salary.  

Depending on eligibility and their elections, full-time employees may also have access to certain tax-advantaged savings accounts set up through the Company, into which employees contribute withheld funds:   

• Flexible savings accounts
• Limited-purpose flexible savings accounts  
• Healthcare savings accounts
• Dependent care savings accounts  
• Commuter benefit accounts  

Paid Time Off and Leave 
Inspire11 grants all eligible employees any statutory leave required by federal, Illinois, or other applicable law, such as FMLA, bereavement, sick leave, emergency leave, jury duty or witness leave, and VESSA leave.   

In addition, Inspire11 has a self-managed paid time off policy (PTO) that does not set predetermined upper limits on the amount of PTO an employee may take. All PTO must be requested in advance and approved by the appropriate supervisor to ensure client work can continue. All full-time employees are expected to take a minimum of one week of PTO each year.   

Inspire11 offers paid parental leave to full-time employees who have been with the company for at least one year. Birthing parents are eligible for up to 12 weeks of paid parental leave, while non-birthing parents are eligible for up to 6 weeks. Short-term disability (STD) benefits are also provided following childbirth. When combined with PTO and STD, birthing parents may receive a total of 18–20 weeks of paid leave. Lastly, Inspire11 has eight paid holidays.  

Other Benefits 
Inspire11 also maintains other programs that provide additional benefits to employees, such as:   

• Impact to 11: a charitable giving matching program where, after being employed by Inspire11 for a year, employees may request that a qualifying charity receive up to $500 in matching donations each year. 
• Professional development benefits for qualifying employees: a program allowing for reimbursement of certain course materials and costs on a discretionary basis for employees who have been at Inspire11 for at least 6 months, with supervisor’s approval. 
• Regular gifts of Inspire11 clothes, office items, and swag.  

Disclaimer 
This document applies only to the United States and to Inspire11 LLC, not our worldwide partners.  

Inspire11 reserves the right to change the benefits offered to employees in accordance with applicable law based on business needs, market conditions, and financial stability. Before changing any benefits, Company will notify all employees of the change and will update this document.  

EEOC 
Inspire11 is an Equal Opportunity Workplace and an Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. If you need assistance or a reasonable accommodation during the application process, please contact us at hr@inspire11.com, or inform your recruiting partner.  

Hudson River Trading (HRT) is looking for a Senior Software Engineer to focus on our Treasury Infrastructure. Coders at HRT work on small, highly productive, and efficient teams that design, improve, and maintain the technology that powers worldwide trading — at HRT, the code you write is our business. You’ll have an opportunity to work closely with Treasury Quantitative Researchers and our Funding Team to design and develop HRT's Treasury Optimization and Research Platform.

We are looking for highly skilled programmers who love to code and solve complex problems, appreciate a culture of collaboration, and thrive in a performance-driven environment. You can always expect to be challenged by the ever-changing financial markets, and find yourself working on critical software in an extremely fast-paced, real-time environment.

Responsibilities

• Contribute to the engineering design and development of HRT’s Treasury Optimization Platform.
• Collaborate with Treasury Researchers on HRT’s Treasury Research Infrastructure.
• Collaborate with Quantitative Developers as well as our Funding and Finance teams on managing cash and collateral requirements.

Qualifications

• Completed a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. If you are currently pursuing your Bachelor's degree, please apply to our Campus/New Grad opportunities.
• Working experience in Python and good CS fundamentals.
• Proficiency with numerical computing libraries (Numpy, PyTorch, TensorFlow) is a plus.
• Knowledge of global portfolio financing and funding is a plus.
• Strong problem-solving skills and ability to work in a fast-paced environment.
• Excellent communication skills and ability to collaborate with engineers, researchers, and non-technical teams.

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

Key Responsibilities:

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.

Qualifications:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.

The annual base salary range for this position is $175,000 to $300,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

#LI-BL1

Polar Operations LLC (dba IMC Markets) has multiple openings for Python Software Engineer (REQ#4461661101) in Chicago, IL and Brooklyn, NY.

Your Core Responsibilities

• Implement software features and components for IMC's trading systems (25%).
• Design software solutions independently and make architectural decisions (10%).
• Debug and fix software issues in trading systems (5%).
• Identify complex multi-component issues, perform root cause analysis, and implement preventative solutions (5%).
• Ensure code quality and system reliability through comprehensive automated testing (10%).
• Contribute to the automated testing framework, own critical trading system components, and ensure reliability, performance, and scalability across regions (5%).
• Participate in code reviews (10%).
• Monitor system performance and trading operations (5%).
• Collaborate with traders and other teams to understand requirements and deliver solutions (10%).
• Participate in planning, estimate complexity of tasks, and help balance technical debt with feature development (5%).
• Mentor and guide junior developers and interns, helping them understand IMC's systems and development practices (10%).

Partial telecommuting permitted. Multiple positions available. Requisition #: 4461661101

Your Skills and Experience

This position requires a Bachelor’s degree, or foreign equivalent, in Computer science, Computer Engineering, or a similarly quantitative and technical field and one (1) year of experience in the job offered or a related occupation.

Must have at least one (1) year of experience in each of the following skill(s):

• Applying analytical and engineering techniques to solve complex problems programmatically;
• Programming with Python;
• Developing and implementing algorithms and data structures;
• Software development lifecycle and best practices;
• Designing and developing software applications in a collaborative, cross-functional environment;
• Experience with DevOps, databases, big data, or frontend frameworks;
• Experience with Docker, Kubernetes, and Linux environments;
• Experience working with financial data;
• Foundational knowledge of quantitative trading concepts and equity/index options;
• Exposure to web/API frameworks such as FastAPI and React;
• Experience with automated testing and CI/CD pipelines;
• Experience collaborating with trading desks and developing software for use in a quantitative trading environment;
• Experience with the following tools: Team City, JIRA ticket management, Gerrit code review tool, GitLab, uv, Gitlab CI, py-spy, memray, and Python Packaging; and
• Experience with the following libraries: Pandas, Polars, NumPy, Tensorflow, Scikit, scikit-learn, cvxpy, and Pytest.

#LI-DNI

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

We are deploying machine learning directly onto custom hardware – and we want you to help drive it from the ground up. This is an initiative where you'll have the rare opportunity to architect solutions from scratch, influence technical research direction, and see your work drive real impact in one of the most demanding computing environments in the world.

We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can fix it - there's no vendor to wait on and no abstraction layer you're not allowed to touch. If you've ever wanted to push the boundaries of what's computationally possible, this role is for you. We're looking for researchers and experienced engineers from any background. Trading experience is a bonus, not a prerequisite.

Your Core Responsibilities

• Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs
• Shape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions
• Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production
• Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems

Your Skills and Experience

• Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
• Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
• Understanding of machine learning fundamentals – neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
• Proficiency in Python, C++, or similar languages for tooling, testing, and simulation
• Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

Nice to Have

• Exposure to ML compiler infrastructure such as MLIR, TVM, XLA, or similar tools for lowering and optimizing models for hardware targets
• Background in latency-sensitive or resource-constrained systems including high-frequency trading, particle physics data acquisition, real-time signal processing, or similar domains
• Familiarity with functional verification methodologies (for example SystemVerilog, UVM, Cocotb)
• Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through industry or research experience

Lantern is looking for a Director of AI Engineering to lead the strategy, development, and delivery of AI and machine learning capabilities across the organization. This is a senior leadership role responsible for building and scaling a high-performing AI engineering team, setting the technical vision for ML/LLM systems, and ensuring that AI initiatives are tightly aligned with Lantern’s product strategy, care workflows, and business objectives. You will report to the VP of Engineering and work closely with executive leadership to shape how AI transforms care delivery and operations at Lantern.

In this role, you will own the end-to-end AI engineering function—from research and prototyping through production deployment and continuous improvement. You will be expected to move fast, driving rapid proof-of-concept development and delivering production-grade AI solutions on aggressive timelines. You will establish the technical roadmap, define engineering standards, build team culture, and drive execution across multiple concurrent AI initiatives. Your work will directly impact how Lantern leverages intelligent systems to improve patient outcomes, operational efficiency, and scalable growth.

Location: Hybrid - at least 3 days/wk in one of our offices:  Dallas, TX / Chicago/Evanston / New York / Washington, DC

Responsibilities:

• Define and execute the AI engineering strategy and technical roadmap, ensuring alignment with Lantern’s product, clinical, and business priorities.
• Build, lead, and scale a high-performing AI engineering team, including hiring, mentoring, performance management, and creating a culture of technical excellence and innovation.
• Drive rapid AI proof-of-concept development, moving from problem identification to working prototypes on aggressive timelines, and shepherding successful POCs into production-grade systems.
• Own the end-to-end lifecycle of ML/LLM systems—from research and experimentation through production deployment, monitoring, and continuous improvement.
• Establish and maintain best-in-class MLOps practices: experiment tracking, reproducibility, CI/CD for models, automated testing, drift detection, observability, and scalable infrastructure on Azure.
• Partner with product, clinical operations, marketing, data, and engineering leadership to identify high-impact AI opportunities and translate them into well-scoped, executable initiatives.
• Lead architecture decisions for ML/LLM systems, ensuring scalability, reliability, security, and compliance with healthcare regulations.
• Establish clear KPIs and success metrics for the AI engineering function, including model performance, delivery velocity, team health, and business impact.
• Champion responsible AI practices across the organization, including fairness, transparency, explainability, and regulatory compliance in healthcare contexts.
• Represent the AI engineering function to executive leadership, providing regular updates on progress, risks, and strategic recommendations.
• Build external visibility for Lantern’s AI capabilities through thought leadership, community engagement, and talent brand building.

Experience & Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience; Master’s preferred.
• 10+ years of experience in software engineering or machine learning, with at least 4 years in a leadership role managing ML/AI teams.
• Proven track record of building and shipping production ML/LLM systems at scale, including end-to-end ownership from research through deployment.
• Deep technical expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), LLM technologies (prompt engineering, RAG, embeddings, fine-tuning, agents), and modern MLOps practices.
• Strong experience with cloud-native ML infrastructure on Azure, including Azure ML, Databricks, and Azure DevOps pipelines.
• Demonstrated ability to rapidly prototype AI solutions and drive them to production on aggressive timelines.
• Experience with big data ecosystems (Spark, Databricks, Delta Lake) and streaming technologies.
• Excellent leadership skills: hiring and developing high-performing teams, managing cross-functional relationships, and influencing at the executive level.
• Strong strategic thinking with the ability to balance long-term vision with near-term execution and business impact.
• Experience operating in regulated environments, ideally healthcare, with an understanding of compliance, data governance, and responsible AI principles.
• Outstanding communication skills with the ability to translate complex technical concepts for non-technical stakeholders and executive audiences.

Strong Candidates will have:

• Experience applying AI/ML capabilities in healthcare, health tech, or other regulated industries.
• Background working with clinical data, claims data, or healthcare operational workflows.
• Experience building and scaling AI teams from the ground up, including defining team structure, processes, and culture.
• Track record of delivering AI-powered products that drove measurable business outcomes.
• Experience with LLM agents, agentic workflows, tool-use orchestration, or multi-step reasoning systems.

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave

Lead Data Scientist, Relevancy Sciences Data Science & Research – Personalization & Loyalty Strategy (P3764)

Relevancy Sciences Team is responsible for powering relevant, personalized, and scalable customer experiences across Kroger’s e-commerce ecosystem. We build and evolve the science behind search and recommendations that serve millions of customers and support high-scale digital experiences.

We are seeking a Lead Data Scientist to provide technical leadership across search and recommender systems, with a strong focus on modern model architectures, and production-ready machine learning. This role is ideal for someone who combines depth in applied machine learning with strong systems thinking, cross-functional influence, and contributes towards agentic capabilities.

What does the role entail? (Responsibilities)

Technical Leadership, Ownership, and Influence. Own and drive technical initiatives across search & recommender systems. Define and evolve the science strategy for improving content discovery, relevance, personalization, and decision support across digital experiences. Identify high-impact opportunities, make clear technical tradeoffs, and guide the team towards scalable, practical solutions. Rapidly prototype and validate new ideas to accelerate adoption and demonstrate measurable value.

Develop innovative search & recommender systems. Design and build ML solutions tailored to the unique needs of grocery retail domain. Lead the development of systems that improve product discovery and personalization across customer journeys. Bring strong technical and thought leadership on next generation personalization, including the use of Generative AI and agent-based approaches.

Evaluate and improve ML performance. Establish rigorous evaluation methodologies to assess the performance of ML systems across key metrics. Define robust online evaluation frameworks, and guide experimentation strategies that connect model improvements to customer and business outcomes.

Model serving and deployment. Partner closely with Engineering to build and deploy production-ready ML systems. Influence design decisions related to real-time inference, feature access, system integration, monitoring, and reliability. Ensure solutions meet latency, scalability, and operational requirements. Contribute to the evolution of serving and deployment strategies.

Cross-functional leadership. Work closely with Product, Engineering, and business stakeholders to translate needs into clear problem statements, hypotheses, and execution plans. Drive alignment across teams and influence decisions through clear communication of tradeoffs, risks, and expected outcomes.

Mentoring and knowledge sharing. Mentor data scientists and lead technical reviews to improve model quality, experimentation rigor, and systems thinking. Promote best practices in reproducibility and evaluation. Contribute to building a strong, learning-oriented team culture.

What skills and experience do you need? (Requirements)

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 6+ years of experience applying machine learning to real-world problems with strong experience in search and recommender systems. Strong understanding of approaches such as embeddings and multi-stage decision systems.
• Strong proficiency in Python and SQL, with experience working on large-scale data using tools such as Spark.
• Experience with modern machine learning and deep learning frameworks such as PyTorch or TensorFlow.
• Strong foundation in statistics, experimentation, and data analysis, including design of experiments and A/B testing.
• Familiarity with large language models, foundation models, and emerging AI capabilities, including where they are applicable and where they are not.
• Experience evaluating or prototyping GenAI-based solutions is preferred.
• Experience partnering with engineering teams to deploy and maintain machine learning systems in production.
• Understanding of real-time systems, model serving, feature pipelines, and monitoring.
• Ability to make practical tradeoffs between model complexity, performance, latency, and scalability.
• Demonstrated ability to lead technical work across projects and influence direction across data science, engineering, and product teams.
• Experience mentoring or guiding other data scientists and contributing to a strong technical culture.
• Experience working with cloud platforms such as GCP or Azure.
• Experience in retail, e-commerce, or high-scale consumer domains is a plus.

Why join our team? (Rewards)

Impact millions of people. As a member of our data science team, you will have the opportunity to make a tangible difference in the lives of millions of customers by delivering relevant and personalized recommendations that enhance their grocery shopping experience. Your work will directly contribute to increasing customer satisfaction and loyalty, driving business outcomes for our company.

Continuous learning and development. Challenge yourself. We are committed to fostering a culture of continuous learning and development. You will have access to resources and support for expanding your knowledge and skills in cutting-edge technologies, including recommender systems, machine learning and artificial intelligence. Our team encourages exploration and experimentation, providing opportunities to stay at the forefront of industry advancements.

Work on new developments in recommender systems. Join a team at the forefront of innovation in recommender systems and AI. You will have the chance to contribute to pushing the boundaries of what’s possible in personalized recommendation technology. You will have the chance to work on exciting projects that leverage the latest developments in deep learning architectures and data science methodologies.

#LI-SSS

We are deploying machine learning directly onto custom hardware – and we want you to help drive it forward. This PhD internship is an opportunity to work on research that has direct impact on IMC's work tackling open problems at the frontier of low-latency ML inference and hardware acceleration.

You'll work alongside IMC engineers in one of the most demanding low-latency computing environments in the world. You'll own a focused research project from start to finish, present your findings to the team, and leave behind a prototype or benchmark that we can build on.

Your Core Responsibilities

• Architect and develop an ML focused research project based on a real-world trading environment
• Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions
• Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems
• Present your project to the team, deepening our collective understanding of an area of ML acceleration
• Gain hardware design fundamentals from skilled RTL developers and learn how they apply to our industry
• Build skills to evaluate research not only from an academic perspective, but through real-world performance constraints, engineering costs, and industry impact

Your Skills and Experience

• Currently enrolled in a PhD program in Electrical Engineering, Computer Science, Physics, or a related field
• Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
• Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
• Understanding of machine learning fundamentals – neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
• Proficiency in Python or similar languages for tooling, testing, and simulation
• Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

You may submit one application per role each year.  We strongly encourage you to focus on applying to a single role that best matches your skills and interests.  Though you may apply to multiple roles, please note that each application will be evaluated based on the specific criteria established for that particular role. If you have already applied for this position during the current recruitment season and were not selected, you may reapply when the next recruitment season begins in 2027.

The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.

Base Salary: $225,000

Job Description

Waltz Health is looking for a skilled Engineering Lead to drive the architecture, development, and delivery of core healthcare technology solutions across the organization. This role involves leading teams, mentoring engineers, and leveraging Microsoft Azure to accelerate our platform roadmap and improve healthcare outcomes through technology.

You will be reporting to the Sr. Director, Enterprise Architecture & Data.  You will be responsible for:

• Own architecture and delivery of marketplace platform applications across Microsoft Azure, including App Services, Azure Functions, AKS, and supporting platform services.
• Manage and mentor engineering teams; set goals, coach career growth, and foster a high-performance, inclusive culture.
• Partner with Product, Architecture, and Business stakeholders to define API-first roadmaps and translate requirements into executable plans.
• Oversee SDLC from design to deployment and support, review customer feedback and operational metrics for continuous improvement.
• Collaborate with clients on custom requirements and integration architectures that are simple, secure, and cost-effective.
• Design, develop, and maintain CRM/workflow automation solutions; enforce data integrity, security, and compliance.
• Lead API-led integrations using an enterprise integration platform (iPaaS); define integration patterns and SLAs.
• Architect, implement, and maintain scalable, secure, and highly available Azure cloud infrastructure and platform services, including CI/CD pipelines, infrastructure-as-code (e.g., Terraform), compute (AKS), storage, data services, and integration capabilities.
• Architect and implement microservices using Spring Framework and Azure-native services.
• Establish standards for REST/GraphQL API design, versioning, documentation, and governance (policies, rate limiting, security, observability) using tools such as Azure API Management and Azure Monitor/Application Insights.
• Work with Product to define objectives, success metrics, and release plans for platform features; drive agile, iterative delivery.
• Mentor and guide engineering teams, establish best practices, and foster a technology-agnostic mindset.
• Deliver simple, cost-effective solutions for complex integration and business challenges.

In the first 90 days, you will:

• Ramp up on the Waltz platform, enterprise application landscape, integration architecture, and Microsoft Azure cloud infrastructure.
• Establish API-first practices and governance; prioritize integration backlogs.
• Deliver initial microservices and integration use-cases focused on data ingestion, transformation, pricing, and platform interoperability.

You will be successful because you:

• Clear communicator, strong collaborator across levels.
• Enthusiastic, adaptable, and outcome-oriented in a fast-moving environment.
• Passion for improving healthcare through technology.

You have these qualifications and skills:

• Bachelors/Degree in Computer Science or other technology field.
• Deep hands-on experience building scalable, secure applications on Microsoft Azure using .NET (C#, ASP.NET) and/or Java (Spring Framework).
• Microservices using Spring Framework and an enterprise integration platform (API-led connectivity, reusable assets, policy management).
• API-first designs and API Governance (standards, security, versioning, documentation, monitoring).
• Product development (backlog shaping, MVP definition, outcome-based metrics).
• Integration architecture across CRM/enterprise applications, cloud services, and data platforms; experience delivering simple, cost-effective solutions for complex problems.
• Technical leadership and mentoring teams; proven experience leading engineers and establishing best practices.
• Technology agnostic mindset—able to select the right tool for the problem and balance build vs. buy decisions.
• 5+ years in software engineering, 2+ years in technical leadership/people management.
• Bachelors in CS/IT/Engineering; Master’s preferred.
• Experience with CRM development/customization plus integration experience (API design, OAS/OpenAPI, connectors, workflows).
• Proficient in one or more modern programming languages; strong experience with React or Angular preferred.
• DevOps/CI-CD and infrastructure-as-code experience (e.g., Terraform); hands-on with data integration and ETL/ELT pipelines (e.g., Azure Data Factory, dbt, Talend).
• Deep SQL knowledge; experience with relational databases (e.g., Postgres) and cloud data warehouses (e.g., Snowflake).
• Experience building and operating cloud applications; exposure to Azure Cognitive Services, Azure OpenAI, and/or TensorFlow is a plus.

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC.  The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism.  This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models.

Your Core Responsibilities:

• Design and deploy machine learning models to enhance trading performance across various asset classes
• Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
• Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models
• Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources

Your Skills and Experience:

• PhD or Master’s in Engineering, Math, Statistics, Computer Science, or related quantitative field
• 2+ years of experience building applied ML models; previous experience in trading environment preferred
• Proven expertise in developing and deploying predictive models
• Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
• Strong understanding of theoretical foundations of state-of-the-art ML models
• Strong publication track record at ICML, ICLR, NeurIPS, or equivalent
• Ability and desire to work in a collaborative team environment
• Excellent written and verbal communication skills

At IMC, we believe technology is the foundation of our competitive edge — and machine learning is increasingly central to how we trade. Over the past few years, we've been steadily building our machine learning capabilities: developing infrastructure, growing our in-house GPU cluster, deploying models into production, and partnering closely with quant researchers and traders to generate real impact. Now we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows.  We're looking for a Principal Machine Learning Engineer to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll work alongside researchers and technologists to design the systems that power experimentation, training, and deployment of ML models — and help set the direction for how machine learning is done at IMC as we scale. If you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you.

Your Core Responsibilities: 

• Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster
• Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines
• Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading
• Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge
• Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment
• Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work
• Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling

Your Skills and Experience: 

• 8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm
• A track record of designing and owning large-scale training and inference systems — not just contributing, but architecting
• Deep proficiency in Python, with strong experience in either CUDA or C++
• Hands-on expertise with modern deep learning frameworks (PyTorch, TensorFlow, or JAX) and practical experience implementing architectures like transformers, attention mechanisms, or sequence models
• Strong foundation in deep learning fundamentals: optimization, regularization, loss design, and the trade-offs that matter when training at scale
• Experience with distributed training at scale (Horovod, NCCL) and GPU optimization (cuDNN, TensorRT)
• History of deploying models to production with strong observability, reproducibility, and monitoring practices
• Comfort working across the ML stack from data pipelines to training infrastructure to serving systems

Why This Role: 

• Build, don't inherit — You'll make foundational technology choices in a platform that's still being defined, not maintain someone else's legacy.
• Real investment, real backing — This is a strategic priority with resources behind it, not a side experiment.
• Direct impact on trading — Your infrastructure will power models that make real trading decisions in competitive global markets.
• Global scope — Work with teams across New York, Chicago, Amsterdam, London, Sydney, Hong Kong and beyond; define practices that can scale worldwide.
• Ideas over titles — IMC's culture values clarity, rigor, and collaboration. The best ideas win, regardless of where they come from.
• Tight coupling with research — You won't be building in isolation. Researchers and engineers work side-by-side, iterating together.

#LI-DNP

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC’s technology and trading capabilities.

Your Core Responsibilities: 

• Develop large-scale distributed training pipelines to manage datasets and complex models
• Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
• Develop libraries to improve the performance of machine learning frameworks
• Maximize performance in training and inference using GPU hardware and acceleration libraries
• Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
• Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
• Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
• Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
• Dig into the internals of open-source ML tools to extend their capabilities and improve performance

Your Skills and Experience: 

• 5+ years of experience in machine learning with a focus on training or inference systems
• Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
• Strong engineering skills, including Python, CUDA, or C++
• Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
• Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
• Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
• Exposure to cloud platforms and orchestration tools
• A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus

#LI-DNP

IMC Trading is seeking a Machine Learning Research Lead with proven experience applying state-of-the-art machine learning to solve challenging trading problems.  This role will drive the development of a centralized ML environment to be used across all areas of trading at IMC.  The ideal candidate will have experience working with other researchers, traders, and engineers to build and continuously improve a research platform to drive innovation via ML. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics and trading intuition as well as a problem-solving mindset and pragmatism.  This is an opportunity to dive deep into feature engineering and focus on applying a wide range of ML models as well as to perform research on building custom models.

Your Core Responsibilities:

• Lead the design, development, and deployment of machine learning models to enhance trading performance across various asset classes
• Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
• Collaborate with quantitative traders, researchers and developers to translate market insights into data-driven features and models
• Oversee data acquisition, preprocessing, and feature engineering for structured and unstructured data sources
• Mentor junior researchers and contribute to a culture of research excellence and experimentation
• Drive strategic decisions on model architecture, experimentation pipelines, and infrastructure for scalable research

Your Skills and Experience:

• PhD or Master’s in Engineering, Math, Statistics, Computer Science, or related quantitative field
• 4+ years of experience building applied ML models; previous experience in trading environment preferred
• Proven expertise in developing and deploying predictive models in low-latency environments
• Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
• Strong understanding of theoretical foundations of state-of-the-art ML models
• Prior people management experience preferred
• Ability and desire to work in a collaborative team environment
• Excellent written and verbal communication skills

#LI-DNP

Who Are We?

Prolaio believes that continuous learning and collaboration can make a significant difference in how heart care is administered. We are creating smarter ways to address heart disease and heart risks by uniting patients, care teams, and researchers on a secure, technology-enabled platform that drives clinical innovation and offers a path towards better patient outcomes.

This is precision cardiology, and we know it’s within reach.

What Will You Do?

The Overview

As a Data Science Intern on the Biomarkers Team, you will develop and validate advanced machine learning pipelines focused on long term time series analysis. Your primary objective is to leverage Foundational Time Series Models and Transfer Learning - such as adapting models from cardiac arrhythmia ECG detection to extract novel physiological insights from wearable and clinical sensor data. This role is essential for advancing our understanding of patient health by translating raw signal data into validated digital biomarkers.

The Specifics

• Model Development & Transfer Learning: Test existing and fine-tune deep learning architectures for time series data, specifically utilizing transfer learning techniques to adapt pre-trained ECG-based cardiac models for new physiological signal tasks.
• Signal Processing Pipeline: Build and optimize Python-based signal processing workflows to handle noisy, real-world sensor data, including filtering, feature extraction, and artifact removal.
• Foundational Model Implementation: Research and implement emerging foundational time series models to evaluate their zero-shot or few-shot performance on proprietary longitudinal cardiac datasets.
• Validation & Benchmarking: Design rigorous validation frameworks comparing model outputs against a clinician-verified "ground truth" to establish metrics like Mean Absolute Error (MAE) and Intraclass Correlation (ICC).
• Codebase Delivery: Maintain a clean, documented, and reproducible code repository that transforms raw high-frequency signals into structured, analysis-ready biomarker datasets.

Why Prolaio?

• Impactful Work: You will join in the fight against heart failure (HF) and hypertrophic cardiomyopathy (HCM) with the goal of extending and saving the lives of our patients while also being at the forefront of changing the healthcare industry through technology.
• Innovative Environment: You will be part of an organization doing something that’s never been done before.
• Professional Growth: You will join a growing team and have a substantial impact on our daily and future operations with the opportunity to continuously learn and grow.
• Collaborative Team: You will be part of a team of collaborative, curious, and committed individuals focused on the collective good, inclusiveness, scientific excellence, and advancing digital health for cardiology.

Who You Are?

• Academic Background: Currently enrolled in a Master’s or PhD program in Data Science, Electrical Engineering, Biomedical Engineering, Computer Science, or a related quantitative field.
• Technical Proficiency: Strong proficiency in Python and deep learning frameworks (e.g., PyTorch or TensorFlow) with a specific focus on time series or signal data.
• Signal Processing Expertise: Familiarity with digital signal processing (DSP) techniques, such as Fourier transforms, wavelet analysis, and windowing methods for physiological data.
• Machine Learning Knowledge: Solid understanding of modern ML architectures (CNNs, RNNs, Transformers) and experience with Transfer Learning or fine-tuning large-scale models.
• Data Handling: Experience working with real-world sensor data, handling irregular sampling rates, and managing large-scale longitudinal datasets.

Compensation: The expected hourly rate for this internship is $50/hour.

**At this time, relocation and housing stipends are not offered for this internship.**

Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance.

Your Role:

You will be instrumental in building out our GPU-accelerated compute platform that powers distributed AI training and inference, large-scale simulations, and computational research workloads. Working closely with product, your platform team members, and infrastructure specialists, you’ll design and implement the compute orchestration layer that manages GPU clusters, bare-metal provisioning, and resource scheduling-enabling researchers and engineers to programmatically access high-performance compute resources with cloud-like simplicity.

Job Responsibilities

• Compute Orchestration Systems: Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments.  
• Resource Management & Scheduling: Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads.
• Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads.
• GPU Platform Engineering: Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems.
• Bare-Metal Lifecycle Management: Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation.
• Performance-Critical Systems: Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments.
• Platform APIs & Integration: Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure.
• Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health.
• Multi-Tenancy and Isolation: Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations. 

Requirements

• Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration systems, or distributed compute infrastructure for production environments.
• Compute Platform Engineering: Strong background in building compute orchestration, resource scheduling, or workload management systems at scale.
• Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations.
• Programming Skills: Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued. 
• Linux & Systems Programming: Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management.
• Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms. 
• GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly.
• Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers.
• Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. 
• Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation.
• Commitment to Growth: Growth mindset with continuous focus on learning and professional development.

Preferred Qualifications

• Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters)
• Experience with GPU virtualization technologies (SR-IOV, NVIDIA vGPU) and multi-tenant GPU sharing
• Background in container orchestration platforms with custom scheduling or resource management
• Knowledge of high-performance networking for GPU communication (InfiniBand, RDMA, NVLink, NVSwitch)
• Familiarity with AI/ML training frameworks (PyTorch, TensorFlow) and their infrastructure requirements
• Understanding of distributed training patterns and multi-node GPU coordination
• Experience building infrastructure for research institutions,labs, or technical computing environments
• Background in financial services or other regulated industry infrastructure is a plus

Key Technologies

• Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming

Why Moonlite

• Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure.
• Hands-On Ownership: As an early engineer, you’ll have end-to-end ownership of projects and the autonomy to influence our product and technology direction.
• Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments.
• Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving.
• Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space.

We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together.

#li-remote