Member of Technical Staff - Distributed Training Engineer Job at Liquid AI (San Francisco)

Member of Technical Staff, AI Training Infrastructure

As a Training Infrastructure Engineer, you'll design, build, and optimize the in...

Location

United States , San Mateo

Salary:

175000.00 - 220000.00 USD / Year

Fireworks AI

Expiration Date

Until further notice

Requirements

Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience
3+ years of experience with distributed systems and ML infrastructure
Experience with PyTorch
Proficiency in cloud platforms (AWS, GCP, Azure)
Experience with containerization, orchestration (Kubernetes, Docker)
Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)

Job Responsibility

Design and implement scalable infrastructure for large-scale model training workloads
Develop and maintain distributed training pipelines for LLMs and multimodal models
Optimize training performance across multiple GPUs, nodes, and data centers
Implement monitoring, logging, and debugging tools for training operations
Architect and maintain data storage solutions for large-scale training datasets
Automate infrastructure provisioning, scaling, and orchestration for model training
Collaborate with researchers to implement and optimize training methodologies
Analyze and improve efficiency, scalability, and cost-effectiveness of training systems
Troubleshoot complex performance issues in distributed training environments

What we offer

meaningful equity in a fast-growing startup
comprehensive benefits package

Fulltime

Member of Technical Staff, Cloud Infrastructure

As a Software Engineer on our Cloud Infrastructure team, you'll be at the forefr...

Location

United States , New York, NY; San Mateo, CA; Redwood City, CA

Salary:

175000.00 - 220000.00 USD / Year

Fireworks AI

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
5+ years of experience designing and building backend infrastructure in cloud environments (e.g., AWS, GCP, Azure)
Proven experience in ML infrastructure and tooling (e.g., PyTorch, TensorFlow, Vertex AI, SageMaker, Kubernetes, etc.)
Strong software development skills in languages like Python, or C++
Deep understanding of distributed systems fundamentals: scheduling, orchestration, storage, networking, and compute optimization

Job Responsibility

Architect and build scalable, resilient, and high-performance backend infrastructure to support distributed training, inference, and data processing pipelines
Lead technical design discussions, mentor other engineers, and establish best practices for building and operating large-scale ML infrastructure
Design and implement core backend services (e.g., job schedulers, resource managers, autoscalers, model serving layers) with a focus on efficiency and low latency
Drive infrastructure optimization initiatives, including compute cost reduction, storage lifecycle management, and network performance tuning
Collaborate cross-functionally with ML, DevOps, and product teams to translate research and product needs into robust infrastructure solutions
Continuously evaluate and integrate cloud-native and open-source technologies (e.g., Kubernetes, Ray, Kubeflow, MLFlow) to enhance our platform’s capabilities and reliability
Own end-to-end systems from design to deployment and observability, with a strong emphasis on reliability, fault tolerance, and operational excellence

What we offer

Meaningful equity in a fast-growing startup
Competitive salary
Comprehensive benefits package

Fulltime

Member of Technical Staff, Performance Optimization

We're looking for a Software Engineer focused on Performance Optimization to hel...

Location

United States , San Mateo

Salary:

175000.00 - 220000.00 USD / Year

Fireworks AI

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
5+ years of experience working on performance optimization or high-performance computing systems
Proficiency in CUDA or ROCm and experience with GPU profiling tools (e.g., Nsight, nvprof, CUPTI)
Familiarity with PyTorch and performance-critical model execution
Experience with distributed system debugging and optimization in multi-GPU environments
Deep understanding of GPU architecture, parallel programming models, and compute kernels

Job Responsibility

Optimize system and GPU performance for high-throughput AI workloads across training and inference
Analyze and improve latency, throughput, memory usage, and compute efficiency
Profile system performance to detect and resolve GPU- and kernel-level bottlenecks
Implement low-level optimizations using CUDA, Triton, and other performance tooling
Drive improvements in execution speed and resource utilization for large-scale model workloads (LLMs, VLMs, and video models)
Collaborate with ML researchers to co-design and tune model architectures for hardware efficiency
Improve support for mixed precision, quantization, and model graph optimization
Build and maintain performance benchmarking and monitoring infrastructure
Scale inference and training systems across multi-GPU, multi-node environments
Evaluate and integrate optimizations for emerging hardware accelerators and specialized runtimes

What we offer

Meaningful equity in a fast-growing startup
Competitive salary
Comprehensive benefits package

Fulltime

Senior Staff Machine Learning Engineer

Help design our AI platform and develop our next generation of machine learning ...

Location

United States , San Francisco

Salary:

216500.00 - 324500.00 USD / Year

GoFundMe

Expiration Date

Until further notice

Requirements

9+ years of hands-on experience in machine learning engineering, AI development, software engineering, or related fields
Experience emphasizing secure, large-scale, distributed system design, AI/ML pipeline development, and implementation
Extensive experience designing, developing, and operating scalable backend systems
Experience applying software engineering best practices such as domain-driven design, event-driven architectures, and microservices
Deep expertise in agentic workflows, AI evaluation solutions, prompt management, and secure AI development and testing practices
Strong knowledge of relational and document-based databases, data storage paradigms, and efficient RESTful API design
Experience establishing robust CI/CD pipelines, automated testing (unit and integration), and deployment practices
Strong leadership skills, including effective planning and management of complex projects, mentoring of team members, and fostering a collaborative, high-performing engineering culture
Excellent communicator, able to articulate complex technical concepts clearly to both technical and non-technical stakeholders
Bachelor's degree in Computer Science, Software Engineering, or a related technical field (preferred)

Job Responsibility

Design and implement AI platforms to enable scalable and secure access to LLMs from multiple model providers for diverse use cases
Design and implement agentic workflows, agentic tool ecosystems, and LLM prompt management solutions
Design, build, and optimize scalable model training, fine tuning, and inference pipelines, ensuring robust integration with production systems
Influence technical strategy and approach to developing embedding stores, vector databases, and other reusable assets
Lead initiatives to streamline ML and AI workflows, improve operational efficiency, and establish standardized procedures to achieve consistent, high-quality results across our AI systems
Design and develop backend services and RESTful APIs using Python and FastAPI, integrating seamlessly with ML pipelines and services
Take operational responsibility for team-owned services, including performance monitoring, optimization, troubleshooting, and participation in an on-call rotation
Collaborate with both technical and non-technical colleagues, including data and applied scientists, software engineers, product managers, and business stakeholders, to deliver reliable and scalable ML-driven products
Coach and mentor fellow ML engineers, promoting a culture of collaboration, continuous improvement, and engineering excellence within the team
Employ a diverse set of tools and platforms including Python, AWS, Databricks, Docker, Kubernetes, FastAPI, Terraform, Snowflake, Coralogix, and GitHub to build, deploy, and maintain scalable, highly available machine learning infrastructure

What we offer

Competitive pay
Comprehensive healthcare benefits
Financial assistance for things like hybrid work, family planning
Generous parental leave
Flexible time-off policies
Mental health and wellness resources
Learning, development, and recognition programs

Fulltime

Staff Backend Engineer

OpenSea is the gateway to web3’s next chapter—where NFTs, fungible tokens, and e...

Location

United States

Salary:

190000.00 - 345000.00 USD / Year

OpenSea

Expiration Date

Until further notice

Requirements

At least 6 years of experience as a software engineer
Strong fluency in event-driven system design patterns, leveraging technologies such as Kafka, Flink, and Debezium, as well as distributed systems more generally
Strong fluency in JVM languages like Kotlin or Java
Strong fluency and opinionated in database choice and schema design for highly performant and scalable applications
Intrinsic interest in leveraging new tools (e.g. AI) to increase work efficiency and quality
Passion for (blockchain) technology, NFTs, and the potential of digital ownership is a huge plus

Job Responsibility

Form, communicate, and execute on a technical vision for the next generation of OpenSea’s architecture that leverages highly scalable and event-driven patterns
Write reliable, low latency marketplace infrastructure software that will eventually process billions of dollars a day worth of transaction volume, including highly performant blockchain indexing systems, order management systems at scale, and REST & Websocket API endpoints
Raise the bar for internal understanding of best practices in building highly performant & event-driven systems with “live” characteristics
Mentor and train other team members and act as an internal thought leader and agitator for architectural rigor and cleanliness

What we offer

Health Benefits: We cover 100% Dental/Vision/Medical for employees and 90% for dependents
Flexible Time Off Policy
Parental Leave: 16 Weeks of Paid Parental Bonding & up to 8 additional weeks for the birthing parent
Mental Health: We offer access to Spring Health, covering 8 therapy & 8 coaching sessions per year
11 Company Holidays
Fidelity 401K Plan
Internet/Mobile Reimbursement Plan
Reimbursement or Monthly Snack Delivery
Company & Team retreats to get together for fun and collaboration
Team Member Co-Working and Gathering Expense

Fulltime

Staff Machine Learning Engineer

We are seeking a Staff Machine Learning Engineer to join our Foundation AI team....

Location

United States , Boston

Salary:

170000.00 - 230000.00 USD / Year

Whoop

Expiration Date

Until further notice

Requirements

Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Electrical Engineering, or a related field, or equivalent professional experience
7+ years of experience in applied ML, AI research, or large-scale modeling, with a track record of delivering production systems
Expertise in modern deep learning (e.g., transformers, state space models) and multimodal model training
Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience building and scaling large datasets and training large models in distributed compute environments
Strong applied experience with representation learning, self-supervised methods, and fine-tuning for downstream applications
Familiarity with MLOps best practices including model versioning, evaluation, CI/CD for ML, and cloud-based compute
Excellent communication skills and ability to collaborate cross-functionally with engineers, researchers, and product teams
Passion for WHOOP’s mission to improve human performance and extend healthspan through science and technology

Job Responsibility

Design, train, and optimize large-scale multimodal foundation models that integrate wearable sensor data, text, biomarkers, and behavioral data
Conduct applied research in self-supervised learning, representation learning, and downstream task fine tuning to advance WHOOP’s core model capabilities
Develop scalable, distributed training pipelines for large models on high-performance compute environments
Collaborate with MLOps, data engineering, and software engineering teams to operationalize models for production deployment, ensuring robustness, reproducibility, and observability
Partner with product and research teams to translate foundation model capabilities into downstream features that deliver meaningful member value
Contribute to the technical roadmap and architectural direction for foundation model development at WHOOP
Serve as a technical mentor for other data scientists, sharing best practices in deep learning, large-scale training, and multimodal data integration
Ensure models adhere to WHOOP’s standards for ethical, transparent, and privacy-preserving AI

What we offer

competitive base salaries
meaningful equity
benefits
generous equity package

Fulltime

New

Member of Technical Staff, Training Infra Engineer

Contribute in and provide strong support for model training pipelines, ship stat...

Location

Salary:

Not provided

Cohere

Expiration Date

Until further notice

Requirements

Extremely strong software engineering skills
Proficiency in Python and related ML frameworks such as JAX, Pytorch and XLA/MLIR
Experience with distributed training infrastructures (Kubernetes, Slurm) and associated frameworks (Ray)
Experience using large-scale distributed training strategies
Hands on experience on training large model at scale and having contributed to the tooling and/or setup of the training infrastructure

Job Responsibility

Design and write high-performant and scalable software for training
Improve our training setup from an infrastructure and codebase performance standpoint
Craft and implement tools to speed up our training cycles and improve the overall efficacy of our training infrastructure
Research, implement, and experiment with ideas on our supercompute and data infrastructure
Learn from and work with the best researchers in the field

What we offer

An open and inclusive culture and work environment
Work closely with a team on the cutting edge of AI research
Weekly lunch stipend, in-office lunches & snacks
Full health and dental benefits, including a separate budget to take care of your mental health
100% Parental Leave top-up for up to 6 months
Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
6 weeks of vacation (30 working days!)

Fulltime

New

Member of Technical Staff, Integration/RL Team (Research Engineer)

The integration team is responsible for developing and scaling machine learning ...

Location

Salary:

Not provided

Cohere

Expiration Date

Until further notice

Requirements

Extremely strong software engineering skills
Value test-driven development methods, clean code, and strive to reduce technical debts at all levels
Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR
Experience using and debugging large-scale distributed training strategies (memory/speed profiling)
[Bonus] Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray)
[Bonus] Hands-on experience with the post-training phase of model training, with a strong emphasis on scalability and performance
[Bonus] Experience in ML, LLM and RL academic research

Job Responsibility

Design and write high-performing and scalable software for training models
Develop new tools to support and accelerate research and LLM training
Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.) to create a strong and integrated post-training ecosystem
Craft and implement techniques to improve performance and speed up our training cycles, both on SFT, offline preference, and the RL regime
Research, implement, and experiment with ideas on our cluster and data infrastructure
Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams!

What we offer

An open and inclusive culture and work environment
Work closely with a team on the cutting edge of AI research
Weekly lunch stipend, in-office lunches & snacks
Full health and dental benefits, including a separate budget to take care of your mental health
100% Parental Leave top-up for up to 6 months
Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
6 weeks of vacation (30 working days!)

Fulltime

Member of Technical Staff - Distributed Training Engineer

Liquid AI

Location:
United States , San Francisco ▼
Boston

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
February 21, 2026

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