Member of Technical Staff, Machine Learning Datasets Job at Runway

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

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 - ML Research Engineer, Data

Our Data team powers Liquid Foundation Models across pre-training, vision, audio...

Location

United States , San Francisco; Boston

Salary:

Not provided

Liquid AI

Expiration Date

Until further notice

Requirements

Strong Python skills with the ability to quickly comprehend problems and translate them into clean, working code
Solid ML fundamentals: experience training, evaluating, and iterating on models (PyTorch preferred)
Track record of learning new technical domains quickly
3+ years relevant experience with an M.S., or 1+ year with a Ph.D. (5+ years with a B.S.)

Job Responsibility

Build and maintain data processing, filtering, and selection pipelines at scale
Create pipelines for pretraining, midtraining, SFT, and preference optimization datasets
Design synthetic data generation systems using LLMs, structured prompting, and domain-specific generators
Design and run evaluations and ablations to measure dataset's impact on model performance
Monitor public datasets across text, vision, and audio domains
Collaborate with pre-training, vision, and audio teams on modality-specific data needs

What we offer

Competitive base salary with equity in a unicorn-stage company
We pay 100% of medical, dental, and vision premiums for employees and dependents
401(k) matching up to 4% of base pay
Unlimited PTO plus company-wide Refill Days throughout the year

Fulltime

Member of Technical Staff, MLE

This is not a typical “Applied Scientist” or “ML Engineer” role. As a Member of ...

Location

Singapore

Salary:

Not provided

Cohere

Expiration Date

Until further notice

Requirements

Strong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions
Fluency with Python and core ML/LLM frameworks
Experience working with (or the ability to learn) large-scale datasets and distributed training or inference pipelines
Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies
Demonstrated ability to meaningfully shape LLM performance
A broad view of the ML research landscape and a desire to push the state of the art
Bias toward action, high ownership, and comfort with ambiguity
Humility and strong collaboration instincts
A deep conviction that AI should meaningfully empower people and organizations

Job Responsibility

Contribute to the design and delivery of custom LLM solutions for enterprise customers
Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies
Build custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets
Develop SOTA modeling techniques that directly enhance model performance for customer use-cases
Contribute improvements back to the foundation-model stack — including new capabilities, tuning strategies, and evaluation frameworks
Work as part of Cohere’s customer facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact to our enterprise customers

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, MLE

Our mission is to scale intelligence to serve humanity. We’re training and deplo...

Location

United States; Canada , San Francisco; New York; Toronto; Montreal

Salary:

Not provided

Cohere

Expiration Date

Until further notice

Requirements

Strong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions
Fluency with Python and core ML/LLM frameworks
Experience working with (or the ability to learn) large-scale datasets and distributed training or inference pipelines
Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies
Demonstrated ability to meaningfully shape LLM performance
A broad view of the ML research landscape and a desire to push the state of the art
Bias toward action, high ownership, and comfort with ambiguity
Humility and strong collaboration instincts
A deep conviction that AI should meaningfully empower people and organizations

Job Responsibility

Contribute to the design and delivery of custom LLM solutions for enterprise customers
Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies
Build custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets
Develop SOTA modeling techniques that directly enhance model performance for customer use-cases
Contribute improvements back to the foundation-model stack — including new capabilities, tuning strategies, and evaluation frameworks
Work as part of Cohere’s customer facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact to our enterprise customers

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, Pre-Training Data

As a Machine Learning Engineer specializing in pretraining data, you will play a...

Location

Salary:

Not provided

Cohere

Expiration Date

Until further notice

Requirements

Strong software engineering skills, with proficiency in Python and experience building data pipelines
Familiarity with curriculum learning, data mixing and data attribution
Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools
Experience working with large-scale datasets, including web data, code data, and multilingual corpora
Knowledge of data quality assessment techniques and experimentation with data mixtures
A passion for bridging research and engineering to solve complex data-related challenges in AI model training

Job Responsibility

Conduct data ablations to assess data quality and experiment with data mixtures to enhance model performance
Develop robust data modeling techniques to ensure datasets are structured and formatted for optimal training efficiency
Research and implement innovative data curation methods, leveraging Cohere’s infrastructure to drive advancements in natural language processing
Collaborate with cross-functional teams, including researchers and engineers, to ensure data pipelines meet the demands of cutting-edge language models

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, Data Research Engineer

We are seeking Data Research Engineers to join our Multimodal team, where we are...

Location

United Kingdom , London

Salary:

Not provided

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or a related technical field AND technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.)
OR equivalent experience
Experience in data analysis or data engineering
Proficiency in statistics and exploratory data analysis methods
Ability to communicate technical findings effectively to research and product teams

Job Responsibility

Create high-quality datasets for training and evaluation
run experiments on new datasets (data ablations) to assess their impact and determine the most effective data
Develop and maintain scalable data pipelines for multimodal ingestion, pre-processing, filtering, and annotation
Analyse real-world multimodal datasets to assess quality, diversity, relevance, and identify areas for improvement
Build lightweight tools and workflows for dataset auditing, visualization, and versioning
Collaborate with Safety, Ethics, and Governance teams to ensure datasets meet standards for quality, privacy, and responsible AI practices

Fulltime

Member of Technical Staff - Forward Deployed Engineer

You will work directly on customer engagements that generate revenue. This is ha...

Location

United States , San Francisco, Boston

Salary:

Not provided

Liquid AI

Expiration Date

Until further notice

Requirements

Hands-on fine-tuning experience with modern LLMs (last 12-18 months): LoRA, PEFT, DPO, instruction tuning, or similar
Strong ML fundamentals: you understand how models learn, fail, and improve
Experience generating or curating training data to address model gaps
Autonomous coding and debugging skills in Python and PyTorch
Proficiency with open-source ML ecosystem (Hugging Face transformers, datasets, accelerate)
Fine-tunes models: You have hands-on experience with techniques like LoRA, PEFT, DPO, instruction tuning, or RLHF. You've written training loops, not just API calls
Works with modern architectures: Your experience includes models released in the last 12-18 months (Llama 3.x, Mistral, Gemma, Qwen, etc.), not just BERT or classical ML
Generates and curates data: You've created synthetic training data to address specific model failure modes. You understand how data quality drives model performance
Debugs methodically: When a model underperforms, you diagnose whether it's a data problem, architecture problem, or training problem, and you fix it
Ships to customers: You can translate ambiguous customer requirements into concrete technical specs and deliver against quality metrics

Job Responsibility

Fine-tune LFMs on customer data to hit quality and latency targets for on-device and edge deployments
Generate and curate training data to address specific model failure modes
Run experiments, track metrics, and iterate until customer success criteria are met
Translate ambiguous customer requirements into concrete technical specifications
Provide analytics to commercial teams for contract structuring and pricing
Work across text, vision, and audio modalities as customer needs require

What we offer

Competitive base salary with equity in a unicorn-stage company
We pay 100% of medical, dental, and vision premiums for employees and dependents
401(k) matching up to 4% of base pay
Unlimited PTO plus company-wide Refill Days throughout the year

Fulltime

Member of Technical Staff, Machine Learning Datasets

Runway

Location:
United States

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:
January 10, 2026

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