Core ML Engineer - Deep Learning Architecture

The OpportunityJoin us at mecka.ai as a Core ML Engineer specializing in Deep Learning Architecture. In this pivotal role, you will spearhead enhancements to our model architecture across all pipelines.This position emphasizes foundational deep learning engineering over applied machine learning. We seek an engineer adept at creating, debugging, and refining internal model architectures from the ground up, moving beyond mere utilization of pre-existing models or standard fine-tuning.Our current ML systems predominantly utilize frame-by-frame models, yet our data is inherently temporal. Your primary responsibility will be transforming and optimizing these models for temporal inference—a crucial factor in unlocking pipeline performance.Additionally, you will become the key resource for model-level debugging, architecture design, and optimization throughout our organization. This is a high-impact, deeply technical role suited for someone who possesses a keen architectural mindset.Key ResponsibilitiesImmediate FocusTemporal Model Conversion: Transition frame-by-frame models to temporal architectures that harness sequential data.Benchmark and validate temporal models against existing frame-based baselines.Ongoing TasksLead enhancements in model architecture across all pipelines (e.g., CV, pose estimation).Tune and debug ML models at the architecture level—modify structural code, craft custom layers, and address the fundamental mathematics rather than relying solely on high-level APIs or hyperparameter tuning.Profile and optimize model performance metrics, including latency, throughput, and memory usage.Assess and integrate new architectures, training strategies, and optimization techniques.Collaborate with CV, ML, and infrastructure teams to deploy enhanced models.

Join Our Team at RebarRebar is revolutionizing the operating systems used by commercial HVAC, electrical, and plumbing suppliers and subcontractors. In the past year, our initial quoting product has successfully scaled to processing thousands of quotes weekly, and we've doubled our revenue in 2026 while gaining traction among top suppliers across North America. Fresh from securing a $14M Series A investment from leading construction technology investors, we are poised for our next growth phase with AI at the core of our innovations.We are seeking a Principal Deep Learning Engineer who possesses a strong background in modern neural network techniques and proficiency in PyTorch. You will be instrumental in advancing computer vision applications in real-world settings. As part of a tight-knit, highly skilled team, you will focus on delivering practical, production-ready machine learning systems—from building data pipelines to developing finely-tuned models—within a dynamic startup environment.This position is ideal for individuals who thrive on working closely with models, refining training workflows, and tackling novel engineering challenges. Our work transcends mere model inference; we create training workflows, develop robust evaluation pipelines, and construct systems that enhance standard model usage.

The Applied Analytics program at Columbia University is excited to invite seasoned data analytics professionals to join our team as a part-time Associate Faculty  for the graduate-level course Applied Deep Learning and AI. This advanced course immerses students in deep learning concepts, integrating essential components from Statistical Machine Learning. Participants will establish a robust foundation in supervised learning alongside various algorithms and methodologies. Key topics include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversarial Networks (GAN), and Reinforcement Learning. Practical applications will provide students with insights on preparing, training, testing, and validating models.An Associate Faculty member plays a pivotal role in delivering subject matter expertise and enhancing the instructional experience for course participants. This position offers an exceptional opportunity to gain invaluable exposure to graduate-level teaching within the prestigious environment of Columbia University.Responsibilities:Attend and engage in all on-campus class sessions, assist with instruction, lead breakout discussions, and facilitate student engagement.Evaluate and assess student work as directed by the course Lecturer.Address student inquiries and concerns proactively.Conduct regular office hours for student support.

About SandbarSandbar is a pioneering technology company based in New York City, dedicated to enhancing human capabilities in an agentic world. Our mission is to empower individuals to think, act, and move with freedom. Our innovative team has developed software, machine learning, and hardware solutions that have been instrumental in the success of leading companies such as Meta, CTRL-labs, Google, Apple, Fitbit, Peloton, and Equinox.Our flagship product, Stream, represents a groundbreaking human-agent interface—a private voice ring and conversational computer. Stream has garnered attention in prestigious publications like the Wall Street Journal, Bloomberg, and Wired, with shipments set to commence in Summer '26.We invite you to join us in shaping a new paradigm for self-augmentation.

Your OpportunityJoin our innovative team at LM Studio as a Software Engineer specializing in AI/ML Systems. We seek a dynamic individual who thrives at the crossroads of machine learning and systems engineering, enjoys tackling complex challenges, and is dedicated to crafting exceptional products.In this role, you will be responsible for developing and enhancing on-device inference engines and integrations for local large language models (LLMs) and advanced AI technologies within LM Studio. You will design and maintain the runtime that facilitates on-device model inference, provide immediate support for new models on launch day, and optimize performance across diverse hardware setups.Collaboration is key here. You will engage closely with model authors and upstream open-source communities (such as llama.cpp, MLX, and others) to ensure that LM Studio users access cutting-edge AI experiences.If you're passionate about bridging the gap between models and systems and care about delivering outstanding local AI-powered experiences for both users and developers, this position could be your next great adventure!

Join Columbia University as an Adjunct Lecturer in Applied Deep Learning and Artificial Intelligence for the Fall 2026 semester. This role offers a unique opportunity to engage with students and impart knowledge in a rapidly evolving field. As part of our esteemed faculty, you will have the chance to contribute to the academic growth of our students and help shape the future of technology.

Join asapp as a Lead AI/ML Engineer, where you will have the opportunity to drive innovation and create cutting-edge machine learning solutions. You will lead a talented team of engineers and collaborate with cross-functional teams to develop, implement, and optimize AI technologies that enhance user experiences. Your expertise in machine learning algorithms and passion for artificial intelligence will be pivotal in shaping our product offerings.

About TennrAt Tennr, we are transforming the way healthcare referrals are managed. When patients are referred to specialists, they often face frustrating delays. Our innovative platform, powered by RaeLM™, ensures that every referral is processed swiftly and accurately, allowing healthcare providers to capture more referrals and reduce denials. Join us in revolutionizing the healthcare experience.About the RoleAs the pioneering ML Ops Engineer at Tennr, you will be instrumental in developing and refining our Machine Learning and AI infrastructure. You will be responsible for creating and optimizing machine learning training and inference pipelines that can manage escalating traffic demands and an expanding range of products. Your expertise will ensure that our AI-driven healthcare platform operates on robust, scalable, and efficiently deployed models.Our Machine Learning team is at the forefront of innovation, developing proprietary VLMs, LLMs, and other specialized models tailored to address critical healthcare challenges. This is not just a position of maintaining existing systems; it is an opportunity to lead groundbreaking experimentation and the introduction of new capabilities. Your contributions will significantly impact our ML and data systems, enhancing Tennr's ability to solve vital issues for patients and healthcare providers.Key ResponsibilitiesDesign, architect, and implement scalable ML software systems for model deployment and management.Maintain infrastructure that supports efficient ML operations, including data pipelines, model evaluations, and large-scale training.Work collaboratively with ML engineers, software developers, and cross-functional teams to integrate models seamlessly with data pipelines and products.Identify and resolve production issues while continuously enhancing system performance and efficiency.Develop tools for online and offline evaluation of ML and LLM systems.

About SciemoSciemo is at the forefront of AI innovation for consumer goods, providing technology that empowers businesses to make more informed, rapid, and human-centric decisions throughout the Integrated Business Planning (IBP) process. Our platform effectively transforms disorganized, isolated data into significant business outcomes. Through real-time assistance from our AI agents, we simplify complexity, enabling decision-makers to focus on what truly matters. Our headquarters is located in New York City.OverviewAs a leading startup, Sciemo is dedicated to advancing AI solutions for consumer brands. Utilizing machine learning, generative AI, agent-based systems, and graph technologies, we enable our clients to extract insights within seconds and achieve business impacts in minutes through our innovative products.We are seeking a Founding Member of Technical Staff to join our team, overseeing the development and deployment of sophisticated ML systems, reporting directly to Dan Wald, our Co-Founder & Chief AI Officer.RoleIn this dual capacity as a Data Scientist and Machine Learning Engineer, you will be instrumental in crafting, developing, and deploying the intelligent solutions that power our AI products. Your expertise will span the full spectrum of applied AI, including data science, machine learning, and large-scale production engineering. This role necessitates a combination of innovative model development and the engineering aptitude to deploy and sustain these models within robust, scalable infrastructures.You will collaborate closely with data engineers, product leaders, backend developers, and customer-facing teams to ensure our AI systems yield tangible value in real-world applications. As an early technical hire, you will help shape our AI strategy, establish technical standards, and create best practices for scalable applied AI.ResponsibilitiesDevelop and deploy AI systems that include:Architecting, building, and deploying ML/GenAI products on cloud infrastructure (AWS or equivalent).Designing and implementing end-to-end AI workflows: data ingestion, feature engineering, modeling, evaluation, and deployment.Creating automated pipelines for continuous learning, model promotion, and performance monitoring.System architecture and reliability:Leading the design of ML orchestration frameworks (such as Airflow, Kedro, ZenML, Flyte) to ensure reproducibility and reliability.

At Artemis, we are pioneering the future of AI-powered defense systems to empower businesses to effectively detect and counter threats in a landscape where AI combats AI on the cyber frontlines.We are supported by esteemed investors such as First Round Capital and Brightmind, alongside a coalition of leading operators in the cybersecurity sector.Our visionary founders, Shachar (former leaders at Palo Alto Networks, AWS, Demisto) and Dan (ex-Abnormal Security, Twitter) have successfully built, launched, and scaled cybersecurity solutions that have earned the trust of thousands of clients, leveraging deep expertise in customer needs, technology, and security to realize our ambitious vision.Our diverse and highly skilled team comprises software developers, AI researchers, security experts, and product designers from industry giants like Google, Abnormal AI, Wiz, Meta, AWS, CERN, and SentinelOne.As we expand our footprint, we are seeking passionate innovators to join our mission and enhance our growing clientele.Job OverviewArtemis is at the forefront of analyzing vast streams of data from cloud services, identity providers, network logs, and more. We are constructing a cutting-edge, large-scale AI-native data platform from inception and require outstanding AI/ML Engineers to help shape and realize this vision. You will collaborate with engineers who have designed some of the world’s most significant distributed systems.A key focus of this role involves utilizing agents as the primary classification and reasoning layer across our platform, enabling us to transcend traditional static rules and approaches, allowing for a deeper understanding and response to multifaceted security data.This position offers substantial responsibility and impact. You will engage with crucial components of our system, from LLM-powered classification and detection to extensive data processing and management.

Join Our Innovative TeamBecome a pivotal member of our team dedicated to creating the world's first biological reasoning model. Collaborate with us on groundbreaking generative foundational models that interpret biological systems across various scales—from molecular interactions to entire organisms—empowering us to predict, comprehend, and manipulate living systems like never before.Output is currently in stealth mode, driven by a team of seasoned founders and biotech experts who have successfully exited ventures in AI and biotechnology, supported by leading venture capital firms including Y Combinator.As a Machine Learning Engineer, you will partner with our founders and talented team members to innovate and deploy advanced AI solutions that facilitate intricate biological reasoning across diverse scales.Develop foundational models for biological systems capable of encoding and decoding biological data at scale.Create deep generative models tailored for biological applications, experimenting with innovative architectures to capture the complexities of multi-scale biological systems.Work on distributed training systems to scale our models to billions of parameters, focusing on performance and efficiency across multi-GPU and multi-node configurations while processing extensive biological datasets.Design efficient data pipelines to manage and process large biological datasets, tackling challenges in data loading, partitioning, and memory optimization.Establish and implement robust evaluation frameworks for complex biological models, ensuring data integrity and preventing leakage across dataset splits.

Machine Learning Developer Experience EngineerAbout Our TeamAt Adaptive ML, we are at the forefront of AI innovation, developing a cutting-edge Reinforcement Learning Operations (RLOps) platform that empowers enterprises to specialize and deploy Large Language Models (LLMs) with tangible outcomes. Our platform provides the foundational infrastructure needed to tune, evaluate, and serve specialized models at scale, enabling the pioneering of task-specific LLM development and the execution of production-ready workflows that manage millions of requests while optimizing for both cost and performance across distributed systems.Our dynamic team has previously contributed to the development of state-of-the-art open-access large language models. We successfully raised $20M in seed funding led by Index Ventures and ICONIQ in early 2024, and we are already operational with clients such as Manulife, AT&T, and Deloitte, spanning the travel and financial services sectors, with many more partnerships on the horizon.About This RoleWe are searching for a talented ML Developer Experience Engineer who will empower ML engineers and development teams to utilize reinforcement learning effectively, without needing extensive RL expertise. You will be responsible for creating developer-focused tools, abstractions, and workflows that simplify the training, evaluation, and deployment of RL models, making them intuitive, robust, and scalable. Your contributions will enhance our customers' ability to adopt, grow, and manage RL use cases independently.This position plays a crucial role in our growth strategy: your work will facilitate independent scaling for our customers, minimizing friction and enabling self-service adoption throughout their organizations. You will closely collaborate with Technical Success, Product, and Engineering teams to identify common patterns, extract reusable solutions, and delineate the boundaries between tooling and product.This position is based in Paris, New York, or Toronto.Your ResponsibilitiesDesign and build user-friendly SDKs, libraries, APIs, and tools that enhance RL workflows for developers and ML engineers.Achieve a balance between simplicity and flexibility—supporting common patterns while allowing for advanced configurations and extensibility.Collaborate with internal teams to refine core components into documented, reusable modules that drive customer success.Engage with technical and customer success teams to identify recurring customer patterns and workflow impediments.Translate user feedback into tooling enhancements, error messaging improvements, onboarding processes, and example references.Maintain comprehensive documentation to support user engagement.

Join our talented team at espresso as a Staff Machine Learning Engineer, where you will train advanced models aimed at significantly optimizing our users' compute costs. Leverage your expertise to innovate and drive impactful solutions that enhance efficiency and performance.

Trunk Tools seeks a Senior Machine Learning Engineer to join its team onsite in New York City. This role centers on creating and deploying models that drive the company’s main objectives. Working alongside colleagues from various backgrounds is a key part of the job. Responsibilities Design and build machine learning models for practical use cases Collaborate with cross-functional teams to gather requirements and deliver effective solutions Deploy models into production environments and monitor their scalability Work Location This position is based at the Trunk Tools headquarters in New York City. Regular onsite presence is required.

Join our innovative Machine Learning team at Jane Street as a Performance Engineer, where your expertise in low-level systems programming and optimization will play a critical role in enhancing our machine learning capabilities. Machine learning is a vital component of Jane Street's global operations. Our dynamic trading environment acts as a unique, rapid-feedback platform for ML experimentation, allowing us to seamlessly integrate new concepts and methodologies. Your primary responsibility will be to optimize the performance of our models during both the training and inference phases. We prioritize efficient large-scale training, low-latency inference in real-time systems, and high-throughput inference in research scenarios. This involves not only refining CUDA implementations but also taking a holistic approach that encompasses storage systems, networking, as well as host and GPU-level considerations. We aim to ensure that our platform operates efficiently at the lowest levels—questioning whether high throughput translates into effective goodput and analyzing the actual time taken to load vectors from the L2 cache. If you're curious and passionate about tackling complex problems, you’ll find a welcoming environment here, even if you haven't previously considered a career in finance.

About SandbarSandbar is a pioneering interface company based in New York City, dedicated to enhancing individual autonomy in an agentic world. Our team comprises experts who have developed cutting-edge software, machine learning, and hardware products at renowned companies like Meta, CTRL-labs, Google, Apple, Fitbit, Peloton, and Equinox.Our flagship product, Stream, represents a revolutionary human-agent interface—a private voice ring and conversational computer. This innovative product has received accolades in major publications such as the Wall Street Journal, Bloomberg, and Wired, with shipping scheduled for Summer '26.Join us in redefining self-augmentation through advanced interfaces.

Confido is revolutionizing the AI infrastructure that empowers Consumer Packaged Goods (CPG) brands by streamlining critical processes from deduction to production planning. Our integrated platform unifies cash application, deductions, disputes, trade promotion management, forecasting, demand planning, and analytics. This integration results in significant time savings, enhanced decision-making, and accelerated scaling capabilities.With over 200 trusted brands managing more than $20 billion in revenue—including notable names like OLIPOP, Simple Mills, Dr. Squatch, and Tropicana—Confido has demonstrated exceptional growth. Recently, we secured a $15 million Series A funding round led by Footwork Ventures and Y Combinator to further drive our expansion.As a Senior Machine Learning / AI Engineer, you will play a pivotal role in developing the AI systems that form the backbone of the Confido platform. Your responsibilities will span from LLM-powered document understanding to creating predictive models that assist brands in making informed decisions.You will collaborate closely with engineering and product teams to transition AI capabilities from research into practical applications.

The mission of The New York Times is to seek the truth and help people understand the world. Independent journalism is at the core of our operations, supported by a world-renowned newsroom that reports from nearly 160 countries. We prioritize the reader's experience across various platforms, from print to digital, with a commitment to delivering exceptional journalism that resonates with our audience.About the Role:The Machine Learning (ML) team at The New York Times is dedicated to enhancing the experience of our 150 million global digital readers and expanding our subscriber base through intelligent content recommendations and personalization.As an MLOps Engineer, you will work alongside product managers, data scientists, and ML platform engineers to build and sustain the infrastructure that drives the machine learning lifecycle. Your expertise will be essential in automating and refining the processes for the training, deployment, monitoring, and management of ML models.This position reports directly to the Senior Engineering Manager of Data Management Infrastructure.

About AdaptiveAdaptive Security stands at the forefront of AI cybersecurity, recognized as NVIDIA and OpenAI’s exclusive investment in this domain. Our mission is to thwart AI-driven cyberattacks. In December 2025, we proudly announced an $81M Series B funding round led by NVIDIA and Bain Capital Ventures, joined by esteemed investors including Capital One Ventures, Citi Ventures, and ongoing support from Andreessen Horowitz (a16z), the OpenAI Startup Fund, and Abstract Ventures. This round signifies NVIDIA’s inaugural foray into AI cybersecurity investments.Founded by seasoned entrepreneurs Brian Long and Andrew Jones, who have a proven track record of building and scaling industry-defining companies, Adaptive is set to shape the security landscape for the AI era. Brian and Andrew previously co-founded Attentive, which achieved over $500M in annual revenue and a valuation exceeding $10B, and TapCommerce, acquired by Twitter. Their wealth of experience in creating high-growth, product-focused businesses is instrumental as Adaptive secures enterprises against ever-evolving threats.With a growing clientele that includes leading banks, technology firms, and healthcare organizations, Adaptive is dedicated to safeguarding teams from emerging threats like deepfakes, smishing, and AI-generated voice scams. As we experience rapid enterprise adoption and explore a market exceeding $200B, we are just scratching the surface of our potential.Role OverviewWe are on the lookout for a Staff Machine Learning Engineer to spearhead the development of Adaptive's ML capabilities. As an AI cybersecurity firm, our products leverage LLMs and ML models to identify, classify, and respond to threats in real time. The role of ML is pivotal to the future of our offerings, and we seek an individual who can define our strategy, establish the infrastructure, and execute hands-on initiatives.Currently, we lack dedicated ML infrastructure or a team; you will have the unique opportunity to build this from the ground up. You will set the technical direction for ML applications across the organization, establish the necessary infrastructure, and engage in hands-on development.Key ResponsibilitiesCraft and define Adaptive's ML strategy: identify optimal applications of ML across our product suite, determine required infrastructure, and guide build vs. buy decisions.Design and implement end-to-end production ML systems, including data pipelines, model training, evaluation frameworks, and inference serving.Establish a robust evaluation methodology to measure model quality, identify regressions, and facilitate data-driven decisions regarding model modifications.Take ownership of data strategy: ensure the acquisition of necessary data in the required formats, define labeling processes, and establish feedback loops to enhance model performance over time.Collaborate with product engineers to integrate ML capabilities seamlessly into our products.

Join the cutting-edge team at sola as a Software Engineer focused on our Machine Learning Platform. In this role, you will contribute to the development and optimization of innovative machine learning solutions that drive our business forward. Collaborate with a talented group of engineers and data scientists to deliver high-quality software that supports advanced machine learning models and applications.