Afresh is the leading AI company in fresh food—partnering with grocers like Albertsons, Wakefern, Meijer, and Stater Bros to order billions of dollars of fresh food in over 12,000 grocery departments nationwide. Following record-breaking 70% growth in 2025, we’ve expanded our platform to cover all fresh departments, launched our full store suite, and debuted DC Fresh Buying. We’re on a mission to eliminate food waste and make fresh food accessible to all. In 2025 alone our software helped save 200M lbs of food waste. If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines the future of fresh, there is no better time to join us. The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions. We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models. This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities. As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support, gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities). About the Role As an ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States. What you will do: - In your first 3 months, you might deliver a project that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems. - By the end of your first 6 months, you will have owned the design and implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh. Skills and Experience - BS in Computer Science or a relevant technical field. - 4+ years of professional software development experience with a proven track record of shipping high-quality applications and services. - Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models. - Technical leadership experience and a demonstrated ability to mentor junior engineers. - Deep expertise in library design, API design, data structures, and algorithms. - Strong familiarity with Python. Salary Range in U.S. $156,000 - $211,000 Salary Range for Canada in CAD: $137,000 - $185,000 About Afresh Founded in 2017, Afresh is working on the #1 solution to curb climate change: reducing food waste. By combining human insight and transformative technology, we're helping grocers provide fresher food to customers at more affordable prices. Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals including ICML, and we've raised over $148 million in funding from investors including former co-CEO of Whole Foods Market Walter Robb and Eric Schmidt's Innovation Endeavors. Fresh is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility. Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law. Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.

Do you want to shape how AI models are validated, optimized, and deployed at global scale? Are you passionate about building the systems that ensure AI models perform reliably and responsibly in production Join the Akamai Inference Cloud Team! The Akamai Inference Cloud (AIC) team is part of Akamai's Cloud Technology Group. We design and operate AI platforms enabling customers to run models with unmatched performance, compliance, and economics. This team owns the end-to-end model lifecyclefrom validation and security scanning through quantization, optimization, and monitoring. We ensure every model meets rigorous standards for quality, safety, and performance. Partner with the best As an ML Senior Engineer, you will build and operate systems responsible for model validation, quantization, and safety across the AIC. You'll develop pipelines that scan models for vulnerabilities, apply quantization and optimization techniques, and build guardrails that enforce safety and compliance policies. This role requires hands-on ML experience and deep understanding of modern architectures, inference optimization, and responsible AI. #AIC As a Machine Learning Senior Software Engineer, you will be responsible for: - Developing and maintaining model validation and security scanning pipelines that assess models for quality, correctness, and vulnerabilities prior to deployment - Implementing quantization, pruning, and other optimization techniques to reduce model footprint and improve inference latency across diverse hardware configurations - Building guardrail and content safety systems that enforce compliance policies and mitigate risks such as jail breaking and prompt injection - Designing model routing and prompt management infrastructure that supports intelligent request handling across model variants - Contributing to model evaluation frameworks that measure accuracy, performance, and safety metrics across the model lifecycle Do what you love To be successful in this role you will: - Have 5 years of relevant experience and a Bachelor's/Master's degree in Computer Science, Machine Learning, or a related field - Demonstrate hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX, including model training, fine-tuning, and inference optimization - Show proficiency in model quantization techniques (GPTQ, AWQ, GGUF) and an understanding of how model compression impacts accuracy and latency - Have experience working with large language models (LLMs), transformer architectures, and multi-modal AI systems - Demonstrate familiarity with model safety and responsible AI practices, including content filtering, red-teaming, or adversarial robustness evaluation - Show proficiency in Python and experience building production data or ML pipelines - Have experience with containerized deployments, CI/CD practices, and cloud infrastructure (not required, but suggested) Work in a way that works for you FlexBase, Akamai's Global Flexible Working Program, is based on the principles that are helping us create the best workplace in the world. When our colleagues said that flexible working was important to them, we listened. We also know flexible working is important to many of the incredible people considering joining Akamai. FlexBase, gives 95% of employees the choice to work from their home, their office, or both (in the country advertised). This permanent workplace flexibility program is consistent and fair globally, to help us find incredible talent, virtually anywhere. We are happy to discuss working options for this role and encourage you to speak with your recruiter in more detail when you apply. Learn what makes Akamai a great place to work Connect with us on social and see what life at Akamai is like! We power and protect life online, by solving the toughest challenges, together. At Akamai, we're curious, innovative, collaborative and tenacious. We celebrate diversity of thought and we hold an unwavering belief that we can make a meaningful difference. Our teams use their global perspectives to put customers at the forefront of everything they do, so if you are people-centric, you'll thrive here. Working for you At Akamai, we will provide you with opportunities to grow, flourish, and achieve great things. Our benefit options are designed to meet your individual needs for today and in the future. We provide benefits surrounding all aspects of your life: - Your health - Your finances - Your family - Your time at work - Your time pursuing other endeavors Our benefit plan options are designed to meet your individual needs and budget, both today and in the future. About us Akamai powers and protects life online. Leading companies worldwide choose Akamai to build, deliver, and secure their digital experiences helping billions of people live, work, and play every day. With the world's most distributed compute platform from cloud to edge we make it easy for customers to develop and run applications, while we keep experiences closer to users and threats farther away. Join us Are you seeking an opportunity to make a real difference in a company with a global reach and exciting services and clients? Come join us and grow with a team of people who will energize and inspire you! Akamai Technologies is an Affirmative Action, Equal Opportunity Employer that values the strength that diversity brings to the workplace. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of gender, gender identity, sexual orientation, race/ethnicity, protected veteran status, disability, or other protected group status. If no date is displayed, applications are being accepted on an ongoing basis until the job is filled. Compensation Akamai is committed to fair and equitable compensation practices. For US based candidates only - the base salary for this position ranges from $121,400 - $218,600/year; a candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, certifications and location. Compensation for candidates outside the US will vary. The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards and an Employee Stock Purchase Plan (ESPP). Akamai provides industry-leading benefits including healthcare, 401K savings plan, company holidays, vacation (in the form of PTO), sick time, family friendly benefits including parental leave and an employee assistance program including a focus on mental and financial wellness; Eligibility requirements apply.

• Innovate ML algorithms to maximize ROI and advertising performance. This ranges from creating entirely new algorithms, to improvements on state-of-the art methods, to development using a deep understanding of classic methods • Write production code, sometimes collaborating with Data Engineers, to implement the novel ML algorithms • Prototype potential algorithms and pipelines, test them using historical data, and iterate to modify based on insights

• Develop and maintain large-scale distributed machine learning systems using frameworks like TensorFlow, PyTorch, and Scikit-Learn • Build predictive models including churn prediction, user journey analysis, and sales forecasting using behavioral data • Work with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting techniques • Collaborate with business units to understand their ML needs • Optimize feature extraction, transformation, and selection while managing Feature Stores for reusability across ML pipelines • Strong focus on MLOps practices including model training, versioning, monitoring, and deployment using CI/CD pipelines, Docker, Kubernetes, Airflow, SageMaker, and MLflow • Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform while maintaining model observability and connecting outcomes to product and strategic goals