Machine Learning Research Engineer, Agents - Enterprise GenAI Job at Scale (San Francisco)

Machine Learning Research Engineer, Agent Data Foundation - Enterprise GenAI

Join the team shaping the future of AI at Scale. The Enterprise ML Research Lab ...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

3+ years of building with LLMs in a production environment
Clear experiences with constructing high quality data to use to improve an LLM/Agent
Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
PhD or Masters in Computer Science or a related field

Job Responsibility

Build synthetic data pipelines to generate enterprise environments to use for RL post-training
Create agents to convert traces from production into actionable insights to use to improve agents
Contribute to our agent building product which can construct other agents using coding agents + proprietary algorithms
Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend

Fulltime

Staff Machine Learning Research Engineer, Agent Post-training - Enterprise GenAI

Build out our next-gen Agent RL training platform; build out the platform that w...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

5+ years of LLM training in a production environment
Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
PhD or Masters in Computer Science or a related field

Job Responsibility

Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers
Research cutting edge algorithms to integrate directly into our training stack
Design solutions that enable complex multi-agent systems to directly learn from both process + outcome based rewards

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend
equity based compensation

Fulltime

Senior/Staff Machine Learning Engineer, General Agents, Enterprise GenAI

Scale AI is the data foundation for AI, helping organizations build and deploy r...

Location

United States , San Francisco; New York

Salary:

218000.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases
Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience
Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design
Proven proficiency in Python, including writing production-quality, testable, and maintainable code
Experience building systems that integrate models with external tools, APIs, databases, and services
Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints
Strong communication skills and comfort working in customer-facing or cross-functional environments

Job Responsibility

Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases
Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints
Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings
Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs
Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems
Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage
Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
equity based compensation
commuter stipend

Fulltime

Director, Enterprise Machine Learning & Research

The Enterprise ML team works on the front lines of the AI revolution, partnering...

Location

United States , San Francisco, CA; New York, NY

Salary:

289800.00 - 362250.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

8+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains
A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.)
Experience and track of recording in landing major research impacts in a fast-paced environment
Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.
Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners

Job Responsibility

Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).
Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
Stay deeply connected to the research community, understanding major trends, and helping set them.
Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
learning and development stipend
generous PTO
commuter stipend

Fulltime

Machine Learning Systems Research Engineer, Agent Post-training - Enterprise GenAI

The Enterprise ML Research Lab works on the front lines of this AI revolution. W...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

At least 1-3 years of LLM training in a production environment
Passionate about system optimization
Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster
Experience with multi-node LLM training and inference
Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
Strong written and verbal communication skills to operate in a cross functional team environment
PhD or Masters in Computer Science or a related field

Job Responsibility

Build, profile and optimize our training and inference framework
Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements
Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation
Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
additional benefits such as a commuter stipend
equity based compensation

Fulltime

Senior Engineer, Machine Learning Engineering

Mastercard’s Business & Market Insights (B&MI) group empowers organizations to a...

Location

India , Pune

Salary:

Not provided

Mastercard

Expiration Date

Until further notice

Requirements

Master’s/bachelor’s degree in computer science or engineering
Considerable work experience with a proven track-record of successfully leading and managing complex projects/products and delivering to aggressive market needs
Expert-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions
Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems
Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks
Experience with AI and machine learning platforms such as TensorFlow, PyTorch, or similar
Strong programming skills in languages such as Python/SQL is a must
Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus

Job Responsibility

Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents
Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence
Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning
Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems
Implement advanced Python-based backend services for inference orchestration, async job handling, and distributed data workflows supporting high-throughput AI operations
Establish end-to-end LLMOps and MLOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining frameworks
Apply traditional AI/ML and statistical modeling techniques (regression, clustering, forecasting, ensemble methods) alongside deep learning models for hybrid interpretability and explainability
Engineer state and memory management subsystems that preserve context, track embeddings, and enable agents to reason temporally across multiple modalities and interactions
Implement Responsible AI practices—bias detection, explainability dashboards, data ethics checks, and performance governance ensuring fairness and transparency of deployed models
Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration to drive enterprise-scale intelligence and automation

Fulltime

Principal Machine Learning Engineer

This is a high-leverage leadership role that spans architecture, execution, and ...

Location

India , Bengaluru

Salary:

Not provided

Ema

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s (or PhD) degree in Computer Science, Machine Learning, Statistics, or a related field
A strong track record (usually 10-12+ years) of applied experience with ML techniques, especially in large-scale settings
Experience building production ML systems that operate at scale (latency / throughput / cost constraints)
Experience in Knowledge retrieval and Search space
Exposure in building Agentic Systems and Frameworks
Proficiency in relevant programming languages (e.g. Python, C++, Java) and ML frameworks (TensorFlow, PyTorch, etc.)
Strong understanding of the full ML lifecycle: data pipelines, feature engineering, model training, serving, monitoring, maintenance
Experience designing systems for monitoring, diagnostics, logging, model versioning, etc.
Deep knowledge of computational trade-offs: distributed training, inference, optimizations (e.g. quantization, pruning, batching)
Excellent communication skills

Job Responsibility

Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products
Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning
Define and own the multi-year ML roadmap for GenAI infrastructure — including agent frameworks, RAG systems, world-class evaluation loops, and integration with MCP, browser, and vision pipelines
Identify and integrate cutting-edge ML methods / research (deep learning, large models, recommender systems, LLMs, etc.) into Ema’s products or infrastructure
Research, prototype, and integrate cutting-edge ML and LLM advancements (reasoning, memory architectures, multi-modal perception, long-context models, autonomous agents) into the platform
Optimize trade-offs between accuracy, latency, cost, interpretability, and real-world reliability across the agent lifecycle — from prompt design to orchestration and execution
Champion engineering excellence — drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems
Mentor and elevate senior engineers and researchers, fostering a culture of scientific rigor, experimentation, and system-level thinking
Collaborate cross-functionally with product, infra, and research teams to align ML innovation with enterprise needs — enabling secure integrations, privacy-aware deployments, and scalable use cases
Influence data strategy — guide how retrieval indices, embeddings, structured/unstructured corpora, and feedback loops evolve to improve grounding, factuality, and reasoning depth

Fulltime

Director, Applied AI Engineering- Enterprise

Join the team shaping the future of AI at Scale. The Applied AI team works on th...

Location

United States , San Francisco; New York

Salary:

285200.00 - 356500.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

8+ years of experience building, deploying, and scaling ML systems in production, including Enterprise-grade Gen AI and agentic applications
6+ years of people leadership experience, including managing managers and leading multi-team organizations delivering customer-facing AI products
Deep applied expertise in Gen AI, including LLM-based systems, agent orchestration, evaluation and reliability frameworks, and production system design
Experience owning end-to-end AI product delivery—from problem definition and technical architecture to deployment, iteration, and scaling
Strong technical judgment across software engineering, system design, and applied ML fundamentals
Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners
Bachelor’s degree required
Master’s or PhD in Computer Science or a related field strongly preferred

Job Responsibility

Drive enterprise impact with GenAI: Partner directly with customers to identify high-leverage opportunities for generative AI, design solutions, and deliver production systems that create clear business value
Lead and scale a GenAI-native Applied AI organization: Build, mentor, and manage multiple teams of Applied AI Engineers and ML Engineers who are native to modern GenAI technologies, agentic systems, and multimodal models
Set technical and strategic direction: Define best practices for agent development, evaluation, deployment, and iteration across customer engagements and internal product efforts
Bridge frontier research to real-world applications: Translate advances in LLMs, multimodal models, and agentic workflows into practical, scalable solutions across industries
Discover model limitations and inspire innovation: Identify failure modes, inefficiencies, and gaps in current models
use these insights to influence internal research, product roadmaps, and customer strategy
Partner cross-functionally: Work closely with Product, Research, Sales, and Leadership to shape Applied AI offerings, prioritize investments, and guide long-term platform evolution
Influence the broader AI ecosystem: Represent Scale’s Applied AI capabilities with customers and partners, and help position Scale as a trusted leader in enterprise GenAI deployment

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
equity based compensation
commuter stipend

Fulltime

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Machine Learning Research Engineer, Agents - Enterprise GenAI

Scale

Location:
United States , San Francisco ▼
New York

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:
February 20, 2026

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Machine Learning Research Engineer, Agents - Enterprise GenAI

Scale

Location:United States , San Francisco ▼New York

Category:IT - Software Development

Contract Type:Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:February 20, 2026

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Job Posted:
February 20, 2026