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 a quantitative trading firm at the forefront of technological innovation. We build and deploy cutting-edge systems within one of the world’s most advanced computing environments to power our global trading operations. Our non-siloed, collaborative coding environment empowers talented engineers to make significant contributions and see their impact daily. At HRT, you'll be challenged to solve the most complex problems in trading alongside many of the brightest minds in the field.

We are seeking highly motivated and skilled Research Engineers who will work very closely with our Algo Developer (Quant Research) trading teams. These roles require exceptional programming fundamentals, a strong user focus, and the ability to thrive in a fast-paced environment with constantly evolving requirements. Multitasking and context switching are inherent aspects of this role, through which you will gain insight into the ideation and execution of some of the world's most sophisticated trading strategies.  

Research Engineers at HRT work on a wide variety of projects. Examples include:

• Designing highly customized quantitative systems that enable Algo Developers to easily express and backtest new predictive signals.
• Building world-class research tooling to speed up Algo Developers’ research iteration.
• Aiding the development and deployment of very large deep learning models for HRT’s live trading.
• Improving HRT’s monetization systems which turn our predictions into profitable trades.
• Evaluating sim vs. live differences to improve the fidelity of our simulations.
• Building systems to monitor and manage live trading.
• Specializing in specific time horizons, from high frequency trading to mid-to-low frequency hedge fund-style strategies.
• Working across a very wide variety of asset classes (from equities to fixed income to options) and regions (from North America to Asia Pacific) – you’ll interact with markets across the world!

Responsibilities

• Design, develop, and maintain robust, performant, and scalable software systems.
• Optimize existing codebases for performance, reliability, and efficiency.
• Debug complex issues across distributed systems in a time-sensitive environment.
• Optimize both the mechanics and ergonomics of Algo Developer research workflows; this requires developing a deep intuition for research methodologies.
• Balance the short term goals of individual trading teams with the longer term goals of HRT’s generalized trading platforms across trading teams. Because we don’t work in a pod environment, REs on different trading teams are expected to share technology and ideas.

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.
• Previous experience at a top-tier finance or technology company with an excellent track record of rapid advancement and superlative reviews.
• Experience with supporting internal users is preferred; bonus if you have real hands-on experience with quant research and/or ML/Deep Learning platforms.
• Experience with python numerical, ML and data-oriented libraries is a big plus (e.g. pandas, scikit, pytorch, etc.)
• Candidates should be prepared for technical interviews in either Python or C++. While there is no hard language requirement for RE roles, proficiency in one of these languages will be assessed during the interview process.
• Kindness and empathy is a real requirement. No “smart jerks” at HRT. 
• Strong understanding of data structures, algorithms, and design principles.
• Excellent communication skills, both written and verbal.
• HRT ships every day. The ability to thoughtfully, methodically, and reliably deploy new software and features is a critical skill. 
• If you have management experience, that’s always a bonus – but even so, expect to code!

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.

For more info about interviewing at HRT, see this blog!

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

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

Job Title: Research Professional – Oeindrila Dube - AI and Machine Learning for Social Impact  (Full-Time, Benefits Eligible)

Location: Chicago, IL 

Salary Ranges: $55,000–62,000 annual salary, additional $2,000 professional development fund. The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Terms: Seeking a Research Professional for a period of at least one but ideally two years 

Expected Start Date: July 1, 2026 

Department: Becker Friedman Institute  

Benefits Eligible: Yes. The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

About the program

The Becker Friedman Institute for Economics (BFI) is seeking to hire a full-time Research Professional to work with Professor Oeindrila Dube. Applicants must have completed a Bachelors degree by June 2026 and available to begin work in summer 2026. The Research Professional’s responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses, running simulations, and preparing manuscripts and presentations. 

The program is intended to serve as a bridge between college and graduate school for students interested in empirical economics. Applicants must have strong quantitative and programming skills. Candidates with research experience are strongly preferred, especially those with experience in Stata, R, Python or Matlab. The ideal candidate would work for BFI for one or two years before applying to graduate school in Economics or another quantitative social science. BFI offers competitive salary and employee benefits. 

Job Summary 

Can cutting-edge AI make policing more equitable?  Can we infer what is in a police officer’s mind from observing their behavior? These are the kinds of question driving our research program, and we're looking for someone to help answer them. 

The Becker Friedman Institute for Economics (BFI) is seeking a full-time Research Professional to work with Oeindrila Dube, Philip K. Pearson Professor at the University of Chicago's Harris School of Public Policy. This is a hands-on research role at the frontier of computer science and economics, with real stakes: the projects you'll work on aim to change how police interact with the public.  

We are applying NLP and machine learning to terabytes of body-worn camera footage to build tools that improve policing outcomes. We will evaluate the effectiveness of these tools using experimental methods. This work builds on "A Cognitive View of Policing" (QJE 2025). You will be working at real scale, using data that matters.  

The ideal candidate has strong training in computer science and economics or a related social science field. While you don’t need to have majored in economics, you should be excited to learn how economists think about leveraging NLP/AI tools and generate causal evidence. 

Why this, instead of a software job? 

If you're a CS student weighing this against a position in industry: the work here is harder in different ways, more collaborative, and the output is knowledge that gets published and used to inform real policy decisions. Pre-doctoral research positions like this one are a well-established pathway into PhD programs in economics, computer science, public policy, and related fields — and a growing number of CS researchers have found that combining their technical skills with training in causal inference opens up research questions that neither field can tackle alone. 

Unit-preferred Competencies

• Strong programming skills in Python, including experience with at least some machine learning libraries (e.g., scikit-learn, PyTorch, Hugging Face) 
• Has experience working with large structured/unstructured datasets in a research or internship setting
• Ability to work independently and manage multiple projects simultaneously 
• Excellent organizational skills 
• Attention to detail 
• Strong academic writing/communication skills

Minimum Qualifications:

Education

• Bachelor's degree in CS, economics, statistics, math, or a related quantitative discipline

Experience

• At least one year of relevant research experience (coursework and school projects count)

Technical Knowledge/Skills

• Knowledge of R and/or Python preferred 
• Knowledge of STATA preferred 

Preferred Qualifications:

• Has experience in NLP, speech/ASR, or computer vision. 
• Has experience with R and Stata are a plus (as Stata is used in the field experiment work) 
• Has had exposure to causal inference or program evaluation — or is eager to develop these skills quickly.  

Application Documents

• Resume/CV (required)
• Cover Letter (required)
• Writing Sample (required)
• Transcripts (unofficial is acceptable) (required)
• Two Professional References (required)

About the Department  

The Becker Friedman Institute for Economics aims to foster frontier research and apply those findings to achieve global impact. BFI brings together researchers from the Booth School of Business, the Kenneth C. Griffin Department of Economics, the Harris School of Public Policy, the Law School, and the entire University of Chicago Economics Community to leverage robust data, apply cutting-edge analytical tools, and uncover novel insights on the world’s most difficult economic problems. BFI then bridges the divide between academic researchers and relevant policy makers by translating and packaging rigorous research into accessible formats and proactively sharing those findings with decision-makers and thought leaders in business, government and nonprofit institutions. In this way, BFI has the potential to play a significant role in meeting the most difficult challenges facing the global community. 

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University’s Notice of Nondiscrimination.

Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.

All offers of employment are contingent upon a background check that includes a review of conviction history.  A conviction does not automatically preclude University employment.  Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

The University of Chicago’s Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu. Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.

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

Director, Software Engineering – Agentic Foundations (P4474)

PREFERRED LOCATION: Cincinnati / Chicago

SUMMARY:

The AI Enablement team at 84.51° is seeking a Director of Software Engineering for its Agentic Foundations team. This team is responsible for designing, developing, and maintaining the agentic backbone of the Kroger enterprise, including platforms, patterns, and tools to support the best practice development of AI agents.

The Director of Engineering requires a unique mix of software engineering and AI skills necessary to lead the technical design of platforms, patterns, and tools to support the enterprise agentic ecosystem. This role has a focus on providing the technical leadership needed for engineering and data science development teams implementing solutions like agent/MCP/scorer/prompt registries, agent-related tools to aid in the development and monitoring of best practice agents and tools, and managing low-code and no-code agent platforms that integrate with agentic support.

RESPONSIBILITIES:

• Partner with the Director of Product Management for Agentic Foundations to jointly lead the space for the Kroger enterprise
• Lead and manage a team of engineers, guiding them in the design and development of their product/platform areas and in the management of their own reports
• Lead and manage hiring efforts for engineers on the Agentic Foundations team, including finding interview panelists, writing interview guides and questions, and assisting with coding screening questions
• Own and manage offshore reliability engineering teams
• Provide in-depth technical leadership in the scoping and implementation of enterprise-grade solutions to support agents across the Kroger enterprise
• Provide operational leadership across agentic platforms, ensuring enterprise-grade reliability, observability, and incident management
• Maintain an up-to-date expertise on agentic workflows, frameworks, platforms, and best practices to identify and inform new capabilities for implementation
• Foster a collaborative and innovative team environment, encouraging professional growth and development among team members
• Reduce the development time of the team by leveraging agentic SDLC tooling provided by the enterprise
• Lead the abstraction of tooling and services to user-friendly interfaces like APIs, Python packages, or UIs
• Understand business requirements and trade-off scale, risk, and accuracy to maximize value and translate research into consumable products or services
• Guide the development teams to follow software development standards, including version control, documentation, testing, and support
• Make time-sensitive decisions and solve urgent problems without escalation

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• 7+ years professional experience with cloud-based software architecture focused on scalability, performance, and reliability
• 7+ years professional experience with end-to-end SDLC leveraging CI/CD and AIOps/MLOps, including experience with agentic SDLC tooling
• 7+ years professional experience with large-scale AI/ML systems, including 2+ years with GenAI and deep learning methods
• Professional experience with production-grade agentic systems, including concepts like design and development, evals, deployment, and monitoring
• Professional experience with AI/ML frameworks and tooling (PyTorch, OpenAI SDK, LangChain, autogen)
• Professional experience with AIOps/MLOps tooling and concepts (MLflow, Model Registry, Model Monitoring)
• Professional experience with scaling and deploying AI/ML systems using tooling in Microsoft Azure and Google Cloud, including CI/CD, containerization, orchestration, and API development (in particular FastAPI)
• Professional experience with Kubernetes (AKS) and Helm
• Professional experience with Python
• Professional experience with infrastructure-as-code using Terraform
• Professional experience leading technical teams and consulting with customers, including managing senior engineers, communicating complex technical topics, and driving outcomes in ambiguous environments
• Skilled in partnering with product management peers to jointly define product and team direction and priorities
• Education: Bachelor’s degree or higher in Software Engineering, Computer Science, Machine Learning, or a related field

#LI-SSS

#LI-Remote

Lead Software Engineer – Agentic Foundations (P4475, P4477)

LOCATION: Cincinnati / Chicago

SUMMARY:

The AI Enablement team at 84.51° is seeking a Lead Software Engineer for its Agentic Foundations team. This team is responsible for designing, developing, and maintaining the agentic backbone of the Kroger enterprise, including platforms, patterns, and tools to support the best practice development of AI agents.

The Lead Software Engineer will provide hands-on technical leadership across architecture, implementation, and operations of core agentic capabilities, including agent/MCP/scorer/prompt registries, evaluation and monitoring tooling, and low-code/no-code agent platforms that abstract complexity for developers and business users. You will partner closely with product management, data science, and engineering peers and leaders to deliver enterprise-grade capabilities that accelerate time-to-value, improve quality and reliability, and promote compliant, best-practice use of agentic systems.

RESPONSIBILITIES:

• Lead the design, development, and operation of core agentic platforms, services, and tooling used across the Kroger enterprise
• Own technical outcomes for a more specific product area, such as registries (agent/MCP/scorer/prompt), evaluation frameworks, observability/monitoring, SDKs/APIs/UIs, and low-code/no-code agent platforms
• Provide in-depth technical leadership to scope and implement scalable, secure, and reliable solutions;
• Uphold standards for architecture, code quality, testing, and documentation
• Partner with the Agentic Foundations leadership, as well as engineering and data science leads, to plan quarters/cycles/sprints and ensure efficient, predictable delivery
• Work with offshore reliability engineering teams to ensure SLAs are upheld
• Occasional participation in level 2 on-call support for production systems
• Abstract complex capabilities into developer-friendly interfaces (APIs, Python packages, CLIs, and UIs) to reduce friction and accelerate adoption
• Drive operational excellence across platforms, including incident management, on-call support, capacity planning, and cost optimization
• Mentor and guide engineers through code reviews, architecture discussions, and pairing; contribute to team growth, onboarding, and enablement

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• Professional experience with cloud-based software architecture focused on scalability, performance, and reliability
• Professional experience with end-to-end SDLC leveraging CI/CD and AIOps/MLOps, including experience with agentic SDLC tooling
• Professional experience with large-scale AI/ML systems, including 1+ years with GenAI and deep learning methods
• Professional experience with AI/ML frameworks and tooling (PyTorch, OpenAI SDK, LangChain, autogen)
• Professional experience with AIOps/MLOps tooling and concepts (MLflow, Model Registry, Model Monitoring)
• Professional experience with scaling and deploying AI/ML systems using tooling in Microsoft Azure and Google Cloud, including CI/CD, containerization, orchestration, and API development (in particular FastAPI)
• Professional experience with Kubernetes (AKS) and Helm
• Professional experience with Python
• Professional experience in ambiguous environments
• Skilled in problem solving, analytic thinking, and creativity
• Skilled in planning, organization, and verbal/written business communication
• Skilled in maintaining a positive leader persona in ambiguous and changing environments
• Demonstrated excellence as a teammate and leader
• 4-6 years relevant experience
• Education: Bachelor’s degree or higher in Software Engineering, Computer Science, Machine Learning, or a related field

#LI-SSS

#LI-Remote

How You’ll Make an Impact
As an Applied Machine Learning Engineer, you will collaborate with architects, data scientists, agentic AI developers, platform engineers, and the product team to build advanced AI and ML capabilities into our platform. Your work will drive innovation in generative AI and beyond, integrating and customizing a wide range of machine learning techniques to solve complex problems in healthcare. By developing next-generation AI agents, algorithms, and computation engines, you will help Strata strengthen its market leadership, improve operational efficiency, and support healthcare providers in delivering high-quality care while maintaining financial health.

A Day in the Life

• Read and translate the latest research (e.g., arXiv papers) into production-ready solutions in Python.

• Prototype and iterate on machine learning models, focusing on areas such as regression, causal inference, optimization, and vector embeddings.

• Collaborate with cross-functional teams to embed ML and AI capabilities directly into our software platform.

• Partner with data scientists to design experiments and apply statistical concepts to real-world data.

• Optimize, test, and scale ML models to support mission-critical healthcare analytics.

Our Technology Stack
Our core platform is used by more than half of the nation’s leading healthcare providers, enabling them to leverage financial, operational, and clinical data. Our AI and ML stack includes:

• Languages & Libraries: Python, PyTorch, NumPy, Pandas, Polars, PyMC

• Infrastructure: AWS, Snowflake, Docker, GitHub

• Techniques & Tools:

  • Regression (with and without Bayesian priors)

  • Vector embeddings, similarity, clustering

  • Core statistics and distributions for EDA

  • Optimization methods (multi-armed bandit, mixed integer programming)

  • Causal inference and probabilistic modeling

What We’re Looking For
We’re seeking a technically curious engineer who thrives on turning theory into practice. The ideal candidate has:

• Strong experience implementing ML models in Python.

• Familiarity with regression, embeddings, causal inference, and optimization techniques.

• Experience applying statistical methods to exploratory data analysis.

• Comfort working with modern ML libraries and frameworks.

Bonus points if you have worked with:

• NLP tasks (LLMs, spaCy, neural networks).

• Recommender systems, latent factors, matrix factorization.

• Graph algorithms.

• Claude Code, Docker, and GitHub.ess computation engine.

Estimated Salary Range: $117,000-150,000

Actual salary will be determined based on factors including, but not limited to, skill set and level of experience. This salary range is a good faith estimate of base pay. Strata also provides discretionary variable pay programs based on role. In addition, Strata provides a comprehensive benefits package including retirement benefits, health and welfare benefits, paid time off, parental leave, life and accident insurance, and other voluntary and well-being benefits.

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 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

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

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

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.**

About Charles River Associates

CRA is a leading global consulting firm that provides independent economic and financial analysis behind litigation matters, guides businesses through critical strategy and operational issues to become more profitable, and advises governments on the economic impact of policies and regulations. Our two main services – economic and management consulting – are delivered by practice groups that focus on specific areas of expertise or industries. Click here to learn how CRA can help you launch your career.

Position Overview

CRA’s Forensic Services practice supports companies’ commitment to integrity by assisting them and their counsel in independently responding to allegations of fraud, waste, abuse, misconduct, and non-compliance. We are noted for deploying cross-trained teams of forensic professionals to assist our clients in gaining deeper insights and greater value more quickly. We provide technical development, expert, and forensic services as well as cybercrime investigation services.

As an Associate Principal, you will lead projects that sit at the intersection of artificial intelligence, full stack development, forensic investigation, synthetic media analysis, and litigation support. In this role, you will serve as a key subject matter expert, builder, and technical advisor across a portfolio of unique client problems , including matters involving large language model (LLM) misuse, AI-generated content authentication, deepfake detection, and AI-enabled fraud. You will lead, learn from, and work alongside a team of like-minded, supportive, and highly technical colleagues.

A day in the life consists of collaborating across client matters, supporting forensic investigations, advising upon AI governance and synthetic media standards, building and deploying detection tooling, conducting structured and unstructured data analysis, and staying current with rapidly evolving developments in the generative AI and AI forensics space.

As an Associate Principal, you will:

• Support our dev team in building unique AI-based solutions for our clients.
• Lead and support technical vision and execution for forensic investigations involving AI-generated content, large language model misuse, deepfake media, synthetic voice and video, and AI-enabled fraud or misconduct.
• Develop, deploy, and validate deepfake detection pipelines and multimodal AI forensic tooling to analyze and authenticate text, image, audio, and video evidence at scale.
• Perform AI content attribution and provenance analysis, including authorship attribution for LLM-generated text, model fingerprinting, training data inference, and C2PA (Coalition for Content Provenance and Authenticity) manifest analysis.
• Apply LLM-based analytical frameworks, including retrieval-augmented generation (RAG) pipelines, structured output generation, and document intelligence, to accelerate investigation workflows and enhance analytical throughput.
• Conduct adversarial prompt analysis, prompt injection detection, and evaluation of AI system vulnerabilities in the context of client incidents, regulatory matters, and litigation.
• Design and build forensic data pipelines and investigatory tooling used to process and analyze large and varied datasets, including unstructured text corpora, media archives, model outputs, and system logs.
• Serve as technical subject matter expert advising legal counsel and corporate executives on complex AI, generative media, and data integrity challenges , translating sophisticated technical findings into defensible, plain-language expert reports and testimony suitable for judicial and regulatory audiences.
• Deliver training programs for clients and internal colleagues on responsible LLM use, AI-generated content identification methodologies, forensic readiness, and AI governance frameworks.
• Lead cross-functional engagements requiring coordination across technical analysis, legal strategy, digital forensics, and stakeholder communication under aggressive deadlines.
• Mentor junior team members.
• Contribute to internal initiatives, thought leadership, and practice development.

Education

• Bachelor’s degree required; Computer Science, Electrical Engineering, Data Science, Computational Linguistics, Information Systems, or a related technical field.
• Graduate degree (M.S. or Ph.D.) in Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a closely related discipline preferred.

Experience

• 8–10+ years of progressive experience in machine learning engineering, AI research, digital forensics, data science, or a closely related technical field, with deep expertise in at least two of the following domains:
• Development, fine-tuning, evaluation, or production deployment of large language models (LLMs) or generative AI systems
• Synthetic media detection, deepfake analysis, or AI-generated content forensics
• Natural language processing, computational linguistics, or authorship attribution
• Digital forensics, incident response, eDiscovery, or cybercrime investigation
• Consulting delivery, expert witness services, or client-facing technical advisory roles in a litigation or regulatory context
• A representative portfolio of project contributions , including open-source contributions, published research, technical blog posts, or other observable works , demonstrating sustained, applied AI proficiency across investigation-relevant domains.
• Demonstrated ability to conduct or support expert witness engagements, produce legally defensible forensic reports, and communicate complex technical findings to non-technical audiences including judges, regulators, and corporate executives.
• Familiarity with AI governance and risk management frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001:2023 (AI Management Systems), and the OWASP Generative AI Security guidelines.

Technical Skills

• LLM Proficiency: Deep understanding of large language model architectures (transformer-based models including GPT, LLaMA, Mistral, Gemma, and related families); proficiency with fine-tuning methodologies including LoRA, QLoRA, and instruction tuning; and experience with alignment techniques (RLHF, RLAIF, DPO).
• LLM Ecosystems and Tooling: Proficiency with the HuggingFace ecosystem (Transformers, PEFT, Datasets, Evaluate, TRL); major LLM inference frameworks (vLLM, llama.cpp, Ollama); and orchestration frameworks including LangChain and LlamaIndex.
• Retrieval-Augmented Generation (RAG) Pipelines: Experience building, evaluating, and optimizing RAG pipelines using vector databases (Pinecone, Weaviate, ChromaDB, pgvector) and embedding models, including chunking strategy, retrieval evaluation, and hybrid search.
• Deepfake Detection and Synthetic Media Forensics: Competency in deepfake detection methodologies including CNN- and transformer-based detection models (trained on FaceForensics++, DFDC, or equivalent datasets); GAN architecture analysis; multimodal artifact inspection (lip synchronization, temporal consistency, audio-visual misalignment); and pixel-level manipulation detection tools (e.g., Amped Authenticate, Sensity AI).
• Content Provenance and Authentication: Understanding of content provenance standards including C2PA (Coalition for Content Provenance and Authenticity), cryptographic content credentials, digital watermarking approaches (including SynthID), and blockchain-based authenticity verification.
• Text Forensics and Authorship Attribution: Experience with NLP-based forensic methodologies including stylometric analysis, semantic embedding-based authorship attribution, human vs. machine-generated text classification, and LLM source attribution (model fingerprinting, training data membership inference).
• Programming Languages and Core Libraries: Advanced proficiency in Python; competency with PyTorch and/or TensorFlow for deep learning model development and evaluation; data analysis using pandas, NumPy, and scikit-learn; and SQL for structured data querying and investigation support.
• Computer Vision and Multimodal Analysis: Experience with computer vision libraries (OpenCV, torchvision, PIL/Pillow) and audio/video processing tools for media forensics, including EXIF metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
• Cloud and Infrastructure: Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure Machine Learning) and containerization approaches (Docker, Kubernetes) for deploying and managing AI forensic tooling in enterprise environments.
• Data Engineering: Experience with data engineering frameworks and tools including Apache Spark, Airflow, and modern data warehousing platforms (Snowflake, BigQuery, Redshift) for processing and analyzing large-scale unstructured and semi-structured datasets.

To Apply

To be considered for a position in the United States, we require the following:

• Resume – please include current address, personal email and telephone number;

If you are interested in applying for one of our international locations, please visit our Careers site to view and apply for available jobs.

Career Growth and Benefits 

• CRA’s robust skills development programs, including a commitment to offering 100 hours of training annually through formal and informal programs, encourage you to thrive as an individual and team member. Beginning with research and analysis skill building, training continues with technical training, presentation skills, internal seminars, and career mentoring and performance coaching from an assigned senior colleague. Additional leadership and collaboration opportunities exist through internal firm development activities.
• We offer a comprehensive total rewards program including a superior benefits package, wellness programming to support physical, mental, emotional and financial well-being, and in-house immigration support for foreign nationals and international business travelers.

Work Location Flexibility

CRA creates a work environment that enables our colleagues to benefit from being together in the office to best deliver on our promise of career growth, mentorship and inclusivity. At the same time, we recognize that individuals realize a range of benefits when working from home periodically. We currently expect that individuals spend at least 3 to 4 days a week working in the office (which may include traveling to another CRA office or to client meetings), with specific days determined in coordination with your practice or team.

Our Commitment to Equal Employment Opportunity

Charles River Associates is an equal opportunity employer (EOE). All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, status as a protected veteran, or any other protected characteristic under applicable law.

Salary and other compensation

A good-faith estimate of the annual base salary range for this position is $140,000 - $170,000. Stating pay within this range may vary based on factors such as education level, experience, skills, geographic location, market conditions, and other qualifications of the successful candidate. This position may be eligible for additional bonus incentive compensation.

CRA offers a comprehensive benefits package, subject to eligibility requirements, which may include: medical, dental, and vision insurance; 401(k) retirement plan with employer match; life and disability insurance; paid time off (vacation, sick leave, holidays); paid parental leave; wellness programs and employee assistance resources; and commuter benefits.

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.

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