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A people operations manager who owns HR data, onboarding and offboarding administration, employee lifecycle changes, benefits operations, people vendor management, compensation analysis, and internal operating system testing. The role requires experience in people operations, HR operations, or payroll, including time at an early-stage startup, along with hands-on admin experience with modern HR systems, multi-state and international employee management, and US benefits and compliance knowledge.
Strategic Business Development, Startups Lead. Owns startup revenue by qualifying and closing inbound demand, building outbound through developer communities and VC relationships, designing startup programs and commercial offers, and translating customer feedback into product improvements. Requires commercial judgment, direct startup sales experience, technical platform sales experience, and technical fluency with models, APIs, and AI infrastructure.
Member of technical staff - ML research engineer on pre-training data, focused on building purpose-built datasets, quality filters, deduplication, synthetic data sources, and curriculum experiments for foundation models. Owns pre-training data pipelines end to end, designs experiments to measure data impact on model performance, maintains scientific validity, and builds production pipelines for training runs at scale. Requires hands-on depth in pre-training or mid-training data, experience connecting data changes to model outcomes, strong Python skills, large-scale data processing experience, and practical experience with PyTorch and model training.
An Office Manager & Executive Assistant who runs day-to-day Boston office operations, facilities, vendor management, and events while providing executive support to the Co-Founder and Chief Science Officer. The role requires experience in office or facilities operations, event planning, executive calendar and travel coordination, vendor management, and proficiency in Google Workspace.
A Member of Technical Staff - Applied ML, RecSys in the enterprise recommendation systems domain. You own end-to-end applied machine learning work, adapting frontier sequential recommendation architectures and fine-tuning models for customer personalization and ranking capabilities under production constraints. The posting asks for Python, PyTorch, large-scale data pipelines, user behavior modeling, sequential recommendation architectures, and transformer-based models like HSTU or SASRec.
Member of technical staff - developer relations for general-purpose AI systems and small models. This role owns developer advocacy, creating reference apps, cookbooks, and technical content, engaging communities on Hugging Face, GitHub, and Discord, and hosting in-person hackathons and workshops. Skills and technologies required include hands-on LLM fine-tuning, PyTorch, Hugging Face, model serving frameworks like llama.cpp and vLLM, quantization, and on-device deployment on iOS, Android, and embedded targets.
Strategic Business Development lead for Life Sciences in the life sciences and drug discovery domain. Owns end-to-end deal making, managing partnerships, and driving adoption of Liquid Foundation Models with pharmaceutical, biotech, and research organizations. Requires 10+ years in business development within pharma, biotech, or life sciences technology, deal experience bringing AI/ML or computational platforms into R&D, and technical fluency in foundation models and drug discovery.
Controller building the accounting infrastructure, internal controls, and financial systems for a high-growth AI company. Day-to-day responsibilities include owning the monthly and quarterly close, managing technical accounting positions such as ASC 606 and ASC 718, overseeing equity compensation and tax processes, and leading annual audits. Requires an active CPA, deep expertise in US GAAP, ERP systems like NetSuite, and extensive progressive accounting experience.
Strategic Business Development lead focusing on driving enterprise adoption of foundation models and building long-term partnerships and revenue. You own deals from end to end, lead technical conversations with senior engineering leaders, and shape product and go-to-market strategies. Requires 10+ years of experience in strategic business development within AI/ML infrastructure or technically deep products, with strong technical fluency and complex agreement negotiation skills.
Member of Go-to-Market Staff for an artificial intelligence foundation-model company. Owns outbound pipeline generation from scratch, builds sales automation layers, leads deals, gathers customer feedback to shape product positioning, and partners with marketing on growth initiatives. Requires 4 to 6 years of go-to-market experience in technical B2B software, developer tools, or infrastructure, with proficiency in modern go-to-market tooling, prospecting to technical personas, and revenue accountability.
A Member of Technical Staff for Inference Systems who owns the inference engine layer and benchmarking infrastructure, porting models and running partner verifications. Day to day involves designing benchmark suites, porting models like LFM2 onto various runtimes, and maintaining the inference engine across hardware targets. Requires hands-on experience with inference frameworks like llama.cpp, ONNX Runtime, or MLX, along with strong C++ and Python skills in performance-sensitive contexts.
Member of Technical Staff - ML Scientist, Japanese Multimodal focusing on Japanese and global foundation models. You own the full post-training loop including supervised fine-tuning, preference optimization, reinforcement learning, data curation, systematic error analysis, and evaluation. Requires hands-on machine learning experience, solid engineering skills in the open-source ML ecosystem, and proficiency in English with optional Japanese reading skills.
Member of technical staff on applied ML for Japanese multimodal systems. You own end-to-end applied machine learning projects for enterprise customers in Japan, including model deployment, inference optimization, fine-tuning, evaluation, and system integration. Requires production-level ML engineering experience, hands-on work with language or multimodal models, performance profiling, and strong customer-facing technical communication skills.
A Product Manager driving product bets and experiments from idea to product-market fit. This hands-on role owns product direction, prioritization, and rapid prototyping, working closely with machine learning, inference, and engineering teams. The posting requires direct hands-on machine learning or applied LLM experience, technical fluency in AI, and strong execution skills.
A member of recruiting staff and technical recruiter who owns technical searches end-to-end across product, applied ML, inference, infrastructure, and research. They build pipelines through creative sourcing, partner with hiring managers and founders, manage candidates through the close, and operate in Ashby. The role requires full-cycle recruiting experience, market depth, candidate judgment, and the ability to learn unfamiliar technical domains quickly.
A member of technical staff on post-training for applied vision systems. The role involves owning end-to-end vision-language model post-training engagements for enterprise customers, managing visual data curation, fine-tuning, alignment, and evaluation while contributing to core multimodal model development. Required skills include hands-on experience with VLM data generation, SFT, preference alignment, reinforcement learning, and multimodal evaluation.
Member of technical staff - post training, applied (audio) on multimodal speech and text language models. Owns applied post-training, data generation pipelines, supervised fine-tuning, preference alignment, and reinforcement learning to adapt audio models for enterprise use cases like voice-driven function calling. Requires hands-on experience with language model post-training, data generation, evaluation pipelines, and function calling or structured output training.
Member of Technical Staff - Post Training, Applied working on foundation models and text workloads. Owns enterprise post-training engagements, data curation, fine-tuning, evaluation, and building reusable tooling and workflows. Requires experience in LLM post-training, data quality, SFT, instruction tuning, RLHF, DPO, PyTorch, Hugging Face, and modern language model architectures.
Member of technical staff - multi-modal, audio building frontier speech-language models for STT, TTS, and speech-to-speech. Day to day responsibilities include building and scaling data pipelines for audio model training, designing evaluation systems, fine-tuning and adapting audio models for customer deployments, and contributing production code to the core audio repository. The posting asks for strong programming fundamentals, experience building and shipping production ML systems, proficiency in PyTorch, and familiarity with distributed training frameworks and audio or speech models.
A Research Engineer working on adaptive intelligence, novel architectures, training methods, and inference strategies for efficient AI systems. The role involves designing and implementing machine learning research and production-grade ML systems, translating scientific ideas into working prototypes, and deploying scalable models. The posting requires fluency in Python and frameworks such as PyTorch, JAX, or TensorFlow, along with a track record of publication in tier-1 venues.
Member of technical staff - distributed training engineer on the training infrastructure team. You own the design, implementation, and optimization of large-scale distributed training infrastructure for GPU clusters, including parallelism strategies, topology-aware collectives, data loading, checkpointing, and monitoring tools. The posting asks for hands-on experience with PyTorch Distributed, DeepSpeed, Megatron-LM, performance profiling, hardware accelerators, and networking topologies.
Member of Technical Staff - GPU Performance Engineer who designs and ships custom CUDA kernels, profiles at the hardware level, and integrates research ideas into production code for training and inference pipelines. The role requires writing high-performance GPU kernels, integrating them into PyTorch pipelines, and building performance benchmarks. Required skills and technologies include custom CUDA kernel development, deep understanding of GPU architecture, low-level profiling tools like Nsight Systems and Nsight Compute, and strong C and C++ skills.