Machine Learning Infra Engineer Job at Reducto (San Francisco)

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

Machine Learning Engineer – HPC

At Meshy, we believe 3D creation should be boundless and accessible. Our mission...

Location

China , Shanghai

Salary:

Not provided

Meshy LLC

Expiration Date

Until further notice

Requirements

Hands-on experience with CUDA and GPU programming
Strong programming skills in C++ and Python
Solid understanding of parallel programming, performance tuning, and numerical computation

Job Responsibility

Design, implement, and optimize GPU computing kernels to accelerate model training and inference for next-generation 3D GenAI models
Develop and maintain domain-specific libraries and performance-critical components for 3D generation workloads
Work closely with researchers and infra engineers to identify bottlenecks, benchmark performance, and deliver high-efficiency, production-ready GPU modules

Fulltime

Senior Machine Learning Engineer

Start.io, a leading mobile marketing and audience platform, empowers the app eco...

Location

Salary:

Not provided

Start.io

Expiration Date

Until further notice

Requirements

B.Sc. or M.Sc. in Computer Science, Software Engineering, or a related technical discipline
5+ years of experience building high-performance backend or ML inference systems
Deep expertise in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML)
Experience with scalable service architecture, message queues (Kafka, Pub/Sub), and async processing
Strong understanding of model deployment practices, online/offline feature parity, and real-time monitoring
Experience in cloud environments (AWS, GCP, or OCI) and container orchestration (Kubernetes)
Experience working with in-memory and NoSQL databases (e.g. Aerospike, Redis, Bigtable) to support ultra-fast data access in production-grade ML services
Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry) and best practices for alerting and diagnostics
A strong sense of ownership and the ability to drive solutions end-to-end
Passion for performance, clean architecture, and impactful systems

Job Responsibility

Own and lead the design and development of low-latency Algo inference services handling billions of requests per day
Build and scale robust real-time decision-making engines, integrating ML models with business logic under strict SLAs
Collaborate closely with DS to deploy models seamlessly and reliably in production
Design systems for model versioning, shadowing, and A/B testing at runtime
Ensure high availability, scalability, and observability of production systems
Continuously optimize latency, throughput, and cost-efficiency using modern tooling and techniques
Work independently while interfacing with cross-functional stakeholders from Algo, Infra, Product, Engineering, BA & Business

What we offer

Lead the mission-critical inference engine that drives our core product
Join a high-caliber Algo group solving real-time, large-scale, high-stakes problems
Work on systems where every millisecond matters, and every decision drives real value
Enjoy a fast-paced, collaborative, and empowered culture with full ownership of your domain

Senior Machine Learning Engineer

We’re seeking a Senior Machine Learning Engineer (P50) to join our new GenAI Mod...

Location

Singapore

Salary:

Not provided

Atlassian

Expiration Date

Until further notice

Requirements

Extensive experience (generally 5+ years) in ML systems engineering, backend engineering, or infrastructure roles
Strong background in one or more of: LLMs, NLP, search/retrieval, embeddings, or applied ML
Hands-on experience with at least one GenAI area: RAG pipelines, fine-tuning, hybrid retrieval, or orchestration frameworks
Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex)
Familiarity with vector databases (Weaviate, Pinecone, FAISS, etc.) and large-scale serving infra
Strong coding skills (Python, backend engineering) and ability to move fast from idea to prototype
Comfort working in fast-paced, experimental environments with evolving direction
Bachelor’s or Master’s in Computer Science, Machine Learning, or related field—or equivalent experience

Job Responsibility

Build and apply advanced GenAI models
Develop and fine-tune LLMs and embeddings for Atlassian’s unique knowledge and enterprise data
Implement retrieval-augmented generation (RAG), hybrid retrieval, and knowledge-grounded modeling approaches
Work hands-on with modern frameworks, contributing directly to high-value prototypes and experiments
Prototype and experiment quickly
Build proof-of-concept systems for GenAI-powered assistants, agentic workflows, and innovative user experiences
Run experiments, collect feedback, and iterate fast to validate impact
Design and implement evaluation methods for quality, groundedness, and user value
Collaborate and contribute
Work closely with peers across ML, engineering, and product teams to bring new ideas to life

What we offer

Health and wellbeing resources
Paid volunteer days

Machine Learning Operations Engineer II

Kensho is S&P Global’s hub for AI innovation and transformation. With expertise ...

Location

United States , Cambridge; New York

Salary:

130000.00 - 175000.00 USD / Year

Kensho Technologies

Expiration Date

Until further notice

Requirements

2+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
Experience managing distributed systems with Kubernetes
Cloud Platform (AWS) understanding
Python proficiency
Familiarity with distributed computing frameworks and workflow orchestration (ie. Ray, Airflow)
Familiarity with software engineering best practices in an ML context
Some basic understanding of ML concepts, LLMs and agents
Ability to debug distributed systems across infrastructure, networking and application layers
Excellent communication skills to drive adoption of new tools and best practices across multiple teams
Someone who’s very curious, driven, low-ego and eager to learn across a range of engineering disciplines

Job Responsibility

Iterate on Kensho’s ML processes to develop tools, services, and frameworks that make every stage of the ML workflow robust, auditable, and usable
Work closely with ML engineers to understand their unique processes, identify pain points, and form effective solutions
Empower engineers with the stable tooling necessary to rapidly experiment and actualize their research into demonstrable prototypes and mature products
Provide resources and training for ML teams on best practices, enabling them to efficiently productionize their work to be leveraged by high-value products and services
Evaluate, select and champion open source and third-party solutions, driving their adoption across teams and integrating into Kensho’s existing platform ecosystem
Ship scalable, efficient, and automated processes for model fine-tuning and reinforcement learning and for the evaluation of LLMs/Agents
Improve LLM and Agentic observability to help monitor agentic applications in production, detecting performance, decay and drift issues
Stay at the frontier by actively tracking emerging tools and frameworks, promote best practices and strengthen the technical expertise of the team with your unique skill set

What we offer

Medical, Dental, and Vision insurance
100% company paid premiums
Unlimited Paid Time Off
26 weeks of 100% paid Parental Leave (paternity and maternity)
401(k) plan with 6% employer matching
Generous company matching on donations to non-profit charities
Up to $20,000 tuition assistance toward degree programs, plus up to $4,000/year for ongoing professional education such as industry conferences
Plentiful snacks, drinks, and regularly catered lunches
Dog-friendly office (CAM office)
Bike sharing program memberships

Fulltime

Machine Learning Data Engineer - Systems & Retrieval

As a Machine Learning Data Engineer - Systems & Retrieval, you will build and op...

Location

United States , Palo Alto

Salary:

Not provided

Zyphra

Expiration Date

Until further notice

Requirements

Strong software engineering background with fluency in Python
Experience designing, building, and maintaining data pipelines in production environments
Deep understanding of data structures, storage formats, and distributed data systems
Familiarity with indexing and retrieval techniques for large-scale document corpora
Understanding of database systems (SQL and NoSQL), their internals, and performance characteristics
Strong attention to security, access controls, and compliance best practices (e.g., GDPR, SOC2)
Excellent debugging, observability, and logging practices to support reliability at scale
Strong communication skills and experience collaborating across ML, infra, and product teams

Job Responsibility

Design and implementation of distributed data ingestion and transformation pipelines
Building retrieval and indexing systems that support RAG and other LLM-based methods
Mining and organizing large unstructured datasets, both in research and production environments
Collaborating with ML engineers, systems engineers, and DevOps to scale pipelines and observability
Ensuring compliance and access control in data handling, with security and auditability in mind

What we offer

Comprehensive medical, dental, vision, and FSA plans
Competitive compensation and 401(k)
Relocation and immigration support on a case-by-case basis
On-site meals prepared by a dedicated culinary team
Thursday Happy Hours

Fulltime

Engineering Manager - Machine Learning Infrastructure

We build simple yet innovative consumer products and developer APIs that shape h...

Location

United States , New York

Salary:

216000.00 - 367200.00 USD / Year

Plaid

Expiration Date

Until further notice

Requirements

8–10 years of experience in ML infrastructure, including direct hands-on expertise as an engineer, IC/TL
2+ years of experience managing infrastructure or ML platform engineers
Proven experience delivering and operating ML or AI infrastructure at scale
Solid technical depth across ML/AI infrastructure domains (e.g., feature stores, pipelines, deployment, inference, observability)
Demonstrated ability to drive execution on complex technical projects with cross-team stakeholders
Strong communication and stakeholder management skills

Job Responsibility

Lead and support the ML Infra team, driving project execution and ensuring delivery on key commitments
Build and launch Plaid’s next-generation feature store to improve reliability and velocity of model development
Define and drive adoption of an ML Ops “golden path” for secure, scalable model training, deployment, and monitoring
Ensure operational excellence of ML pipelines, deployment tooling, and inference systems
Partner with ML product teams to understand requirements and deliver solutions that accelerate model development and iteration
Recruit, mentor, and develop engineers, fostering a collaborative and high-performing team culture

Fulltime

ML Infra Engineer (Data Systems)

As an ML Infra Engineer (Data Systems), you’ll build and operate the data infras...

Location

United States , San Francisco

Salary:

Not provided

Physical Intelligence

Expiration Date

Until further notice

Requirements

Strong software engineering fundamentals
Experience building distributed systems or large-scale data pipelines
Comfort reasoning about performance, memory, I/O, and storage efficiency
Familiarity with batch and/or streaming processing systems
Experience with object storage systems and data format tradeoffs
Ownership mindset: design, build, operate, and iterate on systems end-to-end
Enjoy working closely with researchers and unblocking fast-moving projects

Job Responsibility

Data Ingestion & Processing: Design and build high-throughput pipelines that validate, transform, and featurize raw multimodal data
Batch & Streaming Systems: Operate large-scale batch and streaming workflows over massive datasets
Storage Systems: Design object storage layouts, metadata systems, and efficient access patterns
choose file formats with performance and scalability in mind
Data Lifecycle Management: Build systems for backfills, dataset rebuilds, garbage collection, and large-scale transformations
Training-Time Performance: Optimize dataloaders, sharding, prefetching, caching, and throughput to reduce time from data arrival → model training
Metadata & Indexing: Build scalable metadata stores for datasets, annotations, and training artifacts
Data Movement: Move hundreds of terabytes to petabytes efficiently across clusters and environments
Operational Correctness: Implement observability, validation, and guardrails to prevent silent data regressions
Cross-Functional Collaboration: Work closely with cross-functional teams of researchers, engineers and roboticists to translate evolving data needs into robust systems

Fulltime

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Machine Learning Infra Engineer

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