Data Science & Machine Learning Jobs

Sonepar Management Group (SMG) supports our Sonepar brands (i.e. operating companies) in the US through a shared services model. The SMG teams enable our brands to do business in their local regions while taking advantage of the scale and collective resources of a global enterprise. We are seeking a talented and motivated AI Developer to join our team with LLM specific experience. Understanding of machine learning, neural networks, and natural language processing. 1 to 2 years of experience, preferably gained through internships or part-time roles related to data science, machine learning, or artificial intelligence.

6+ years of experience in data science, machine learning, and AI. Ample experience with Azure cloud services including Machine Learning, Data Factory, etc. Technical tools include Azure Cloud platform, Databricks, Data Factory, Synapse, TensorFlow, PyTorch, Scikit-learn, Spark, Hadoop, and more. Build a rewarding long-term career with us at CFS-when we knock, doors open.. CFS Technology is a Chicagoland based, IT dedicated search practice.

Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.. We are searching for an experienced and collaborative machine learning research engineer focused on building biological foundation models.. This role will contribute to the development and application of Arc’s frontier DNA foundation model (Evo), Arc’s Virtual Cell Initiative focusing on developing cell biological models capable of predicting the impact of perturbations and stimuli, and other projects in the context of Institute-wide machine learning efforts.. You are an innovative machine learning engineer with experience in training and evaluating large deep learning models.. Well-versed in machine learning frameworks such as PyTorch or JAX.

The Sr Machine Learning (ML) Engineer applies expertise in deploying and scaling AI pipelines across at least one major cloud platform (AWS, GCP, or Azure).. Expert knowledge of at least one major cloud platform (AWS, GCP, or Azure). Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native services. Design and implement model validation and monitoring systems with alerts. Automate data pipelines for feature engineering, model retraining, and data versioning using cloud data services (e.g., Redshift, BigQuery, Synapse)

In this role, candidates should have experience in designing and developing machine learning and deep learning systems, professional software development experience, and hands-on experience running machine learning tests and experiments. Knowledge of DevOps tools like Ansible, Jenkins, ELK (preferred). Experience with cloud services like AWS, GCP, Azure. Understanding of execution paradigms like low latency/streaming and batch processing. Preferred Qualifications/Skills Experience with Big Data platforms (Hadoop, Spark, Kafka) and Data Warehouses (Redshift, BigQuery, Snowflake).

Based in the Sales Engineering organization, you will be working with strategic customers using Snowflake to expand their use of the Snowflake Data Cloud to bring data science use-cases from ideation to deployment.. Support workshop and design thinking sessions, to deliver discovery sessions with customer stakeholders to specify advanced use-cases and solutions.. Hands-on scripting experience with Python, with experience using libraries such as Pandas, HuggingFace, XGBoost, PyTorch, TensorFlow, SciKit-Learn or similar.. Experience with Databricks/Apache Spark. Vertical expertise in a core vertical such as FSI, Retail, Manufacturing etc.

The AQMed team is seeking a Principal Machine Learning Engineer (MLE) with deep expertise and industry experience to tackle complex challenges in advancing cardiac diagnostics. The Principal MLE will not only be able to work with rich data sets (both synthetic and real clinical study data) but will also work at the cutting edge of architecting, training, and productizing Deep Learning (DL) and Machine Learning (ML) models. Deep Learning and Machine Learning Expertise: Proficient in advanced DL/ML frameworks, with a focus on self-supervised methods like contrastive learning, generative modeling, masked modeling, graph networks, and clustering. Engineering capabilities: You're proficient in Python and using versioning control systems (e.g., git), show high-quality code standards, and actively collaborate with other scientists and researchers reviewing PRs and writing scalable, maintainable code. Cloud Proficiency: Comfortable with AWS, docker, and scalable batching pipelines.

Our Natural Language Processing (NLP) and analytics software is used by policy and decision makers to evaluate and prioritize current and emerging areas of research.. The Data Scientist will have a firm understanding of data management, data standardization, and advanced statistical methods including regression analysis and predictive modeling.. Proficiency with data visualization tools such as Tableau, Splunk, Google Analytics. Experience with cloud computing environments (AWS, Azure, Google Cloud). Knowledge of database systems including SQL, MongoDB, and ElasticSearch

We are looking for a Principal Data Scientist to join our Government Consumer Analytics team at CVS Health.. 8+ years' experience solving analytics problems using advanced statistics, machine learning, mathematical algorithms and predictive modeling. 8+ years' experience with: Python, SQL, Cloud applications (GCP, AWS, Azure), machine learning, casual inference and experimental design. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above.. including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching.

Cyvl is a Boston-based tech startup revolutionizing the way civil engineering firms and governments map and manage transportation infrastructure.. Our enterprise-grade hardware and software solutions leverage 3D mapping sensors to capture LiDAR, imagery, and GPS data, retrofitted to our customers' vehicles.. Strong hands-on experience with Kubernetes and container orchestration for ML workloads. Proficiency in Python and experience with ML libraries (e.g., PyTorch, TensorFlow, scikit-learn). Exposure to data lake / warehouse solutions like Snowflake or BigQuery

As a Machine Learning Engineer on the Atlas team, you will join a world-class team of engineers and scientists focused on creating next-generation Machine Learning foundational models for perception to power behavior development.. Your contributions will help us build new robot technologies for Atlas and other R&D efforts at Boston Dynamics.. Design systems to mine for valuable data and organize large in-house datasets. Familiarity with popular ML frameworks - pytorch, tensorflow. PhD in Computer Science, Machine Learning, Robotics, or a related field

We’re seeking a Director of Machine Learning to lead our Ads Applied Research team—a small, senior group dedicated to shaping the future of Unity’s monetization and ad tech products. This high-impact leadership role sits at the crossroads of cutting-edge machine learning, large-scale systems, and real-time optimization. Oversee the full lifecycle of machine learning systems powering Unity Ads, including ad delivery, bidding strategies, marketplace optimization, and targeting. Demonstrated success in delivering robust, scalable ML systems in high-scale domains (e.g., ad tech, recommendation systems, marketplaces). Deep knowledge of modern machine learning techniques, frameworks (e.g., PyTorch, TensorFlow), and advanced model architectures.

We’re seeking a Director of Machine Learning to lead our Ads Applied Research team—a small, senior group dedicated to shaping the future of Unity’s monetization and ad tech products.. This high-impact leadership role sits at the crossroads of cutting-edge machine learning, large-scale systems, and real-time optimization.. Oversee the full lifecycle of machine learning systems powering Unity Ads, including ad delivery, bidding strategies, marketplace optimization, and targeting.. Demonstrated success in delivering robust, scalable ML systems in high-scale domains (e.g., ad tech, recommendation systems, marketplaces).. Deep knowledge of modern machine learning techniques, frameworks (e.g., PyTorch, TensorFlow), and advanced model architectures.

Minimum of 3 years' experience building and deploying scalable, production-grade AI/ML pipelines in AWS and Databricks. Practical knowledge of tools such as MLflow, Delta Lake, and Apache Spark for pipeline development and model tracking. Familiarity with data pipeline orchestration and a strong grasp of DevSecOps best practices in cloud-native environments. o TensorFlow or PyTorch. o Elasticsearch, Logstash, Kibana Education Requirement

As a core member of the NLP team, you will research, prototype, develop, deploy and scale innovative Machine Learning/Deep Learning solutions in collaboration with Linguistic Experts and Product Management teams. You will develop predictive models on large-scale datasets to address various business problems leveraging advanced statistical modeling, machine learning, deep learning or data mining techniques. Experience with deep learning-based NLP models such as BERT, GPT, other transformers. Neurodivergence, for example, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, dyslexia, dyspraxia, other learning disabilities. Traumatic brain injury

Strong understanding of deep learning architectures, particularly transformer-based models. Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX). Publications at top-tier ML conferences (NeurIPS, ICML, ACL, CVPR, etc.). Adjunct Lecturer in Machine Learning and Data Science Online Visiting Professor for Machine Learning Greater Chicago Area $75.00-$85.00 2 weeks ago. PhD Positions in Robotics, Machine Learning, and Physics Modeling – University of Illinois Chicago Chicago, IL $111,300.00-$222,700.00 2 weeks ago

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.. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy.. Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.. Strong understanding of core machine learning and artificial intelligence concepts.. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.

Employment Type: Permanent, Full Time. Responsibilities: Design, build, and deploy machine learning models to solve business challenges.. Requirements: Bachelor’s/Master’s in Computer Science, Data Science, or related field.. Strong knowledge of ML algorithms and frameworks (TensorFlow, PyTorch, Scikit-Learn).. Familiarity with cloud ML tools (AWS, Azure, GCP) and distributed computing (Spark) is a plus.

Guide the team in designing, implementing, and validating novel machine learning models tailored to drug discovery challenges, including molecular property prediction, generative modeling, and protein-ligand interactions.. Experience with state-of-the-art ML frameworks (e.g., PyTorch, TensorFlow, JAX) and distributed computing environments.. Our team has created the industry's most advanced molecular AI platform called GEMS (Genesis Exploration of Molecular Space), to accelerate and optimize small molecule drug discovery and to enable the discovery of novel first-in-class and best-in-class small molecule drugs for challenging and/or undruggable targets.. In addition, Genesis has three AI platform collaborations across a range of therapeutic areas, with Gilead Sciences, Eli Lilly, and Genentech.. We raised a $200M series B in August 2023, and have raised over $300M in funding from top technology and biotech investors, including Andreessen Horowitz, Rock Springs Capital, T. Rowe Price, Fidelity, Radical Ventures, NVentures (NVIDIA's VC arm), BlackRock, and Menlo Ventures.

Lead the design and development of advanced machine learning models for autonomous driving tasks, including perception, decision-making, and control. Apply state-of-the-art deep learning techniques, such as reinforcement learning, imitation learning, and self-supervised learning, to improve autonomous driving performance. Stay updated on the latest trends and research in machine learning and autonomous driving, bringing innovative approaches to the team. Extensive experience with deep learning algorithms (CNN, RNN, Transformer) and their applications in autonomous systems. Experience in research and development related to autonomous driving and robotics