Key Responsibilities:

• Lead the technical strategy and execution for SoundCloud’s ML systems (e.g., recommendation, ranking, personalization), including model development, deployment, and scaling
• Lead the exploration of advanced modeling techniques including Deep Neural Networks, LLMs, large recommender systems, multi-modal embeddings and transformer-based architectures to create unique and impactful listening experiences
• Define and drive the overall technical direction for ML in SoundCloud including model architecture, feature engineering, training and serving infrastructure at scale, feature stores, pipeline orchestration, and model registry and versioning all aimed at accelerating our ability to bring ML-powered features to market
• Own short- and long-term objectives, ensuring ML investments deliver measurable user and business impact
• Lead, mentor, and grow ML Engineers and Scientists across SoundCloud
• Be a strategic partner to the CTO in bringing an “ML Everywhere” strategy to all surfaces in the product (both Creator and Fan), and helping to lead the charge on AI-native engineering
• Stay current with industry trends and advancements in machine learning, identifying opportunities to apply them at SoundCloud
• Lead the leveraging of agentic workflows and AI-assisted engineering as a force multiplier to work at 10x the speed of traditional methods

Experience and Background:

• 10+ years of experience in ML engineering, plus 3+ years in search or recommendations in an internet scale service with the reach of tens of millions of daily active users. Experience in a music streaming environment is a bonus
• Proven track record of defining technical strategy and driving execution for large-scale ML systems, particularly in recommendation, personalization, or search
• Experience setting technical roadmaps, influencing cross-functional priorities, and balancing business, product, and engineering trade-offs at an organizational level
• Experience leading and scaling teams of ML Engineers and Scientists, including hiring, mentoring, and developing high-performing talent
• Deep expertise in delivering data-driven products and ML solutions at scale on cloud-based platforms, with strong understanding of infrastructure and lifecycle management
• Hands-on familiarity with modern ML frameworks (PyTorch, TensorFlow) and data processing pipelines, combined with strong software engineering foundations
• Thought leadership in ML and data science, with a track record of driving technical innovation and applying emerging industry trends
• Exceptional communication, strategic influence, and cross-functional leadership skills across senior stakeholders and global teams

The salary range for this role is $280,000 - $325,000 annually. The final salary offered will be determined based on relative experience, skills, internal equity, and location. We also offer a generous total rewards program - read more about additional benefits and perks below!

About us:

• We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London)
• We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home
• We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone’s voice, perspective and experience is respected and heard
• We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities

Benefits:

• Comprehensive health benefits including medical, dental, and vision plans, as well as mental health resources
• Robust 401k program
• Employee Equity Plan
• Generous professional development allowance
• Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
• Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
• 16 paid weeks for all parents (birthing and non-birthing), regardless of gender, to welcome newborns, adopted and foster children
• Various snacks, goodies, and 2 free lunches weekly when at the office

Diversity, Equity and Inclusion at SoundCloud

SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. We are dedicated to creating an inclusive environment at SoundCloud for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or care-giver status.

At SoundCloud you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee-organized groups focused on supporting and promoting the interests of a particular underrepresented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.

About the Role

Together AI is building the Inference Platform that powers the world's most advanced generative AI models. Your role will be a critical bridge between cutting-edge research and real-world applications, focusing on making translating our internal model training research to production-ready deployment for our customers. This involves a deep commitment to data-centric development, meticulous hyperparameter tuning, and rigorous checkpoint evaluation before models ever hit production.

This role will involve understanding customer specific needs and fine-tuning models on our internal data recipe and their proprietary data. The goal is to transform general-purpose models into highly performant, specialized tools that solve real business problems. You will not be training foundation models from scratch but rather focusing on creating highly efficient, specialized models by working with dedicated GPU clusters.

Responsibilities

• Design and iterate on novel speculator algorithms, combining architectural innovations with carefully curated data to push the frontier of accuracy–efficiency tradeoffs.
• Be the critical link between raw data and a production-ready model, seeing your work directly impact our customers' success.
• Work in a fast-paced, high-impact role at the cutting edge of generative AI.
• Collaborate with a team of experts dedicated to solving real-world, high-performance challenges.
• You'll collaborate directly with customers to understand their needs, and work closely with our core inference and Applied ML research teams to integrate your work into the production platform.
• A culture of deep technical ownership where you are empowered to take on and solve challenging problems

Requirements

• A genuine love for data curation and processing, with a meticulous attention to detail. You believe that great models start with great data.
• Demonstrated ability to perform effective hyperparameter searches and understand the trade-offs involved in tuning models for specific tasks.
• Experience working with and building on top of existing training codebases. You are comfortable navigating complex code and contributing to its improvement.
• Strong attention-to-detail in evaluating model checkpoints to ensure they meet strict quality, performance, and reliability standards.
• Experience with Python and PyTorch.
• Familiarity with SLURM and/or Kubernetes clusters and experience submitting and managing jobs in a high-performance computing environment.
• Familiarity with modern LLMs and generative models.
• Basic understanding of distributed training frameworks (e.g., FSDP, DeepSpeed).
• Bachelor’s, Master’s degree, or Ph.D. in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, ATLAS, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $190,000 - $270,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy

What We're Looking For

As we continue to expand our footprint, we are looking for a sharp, agile, and business-minded lawyer to join our lean legal team. You will be the primary engine behind our commercial contracting process. This is not a "siloed" role; you will be the cross-functional glue between Finance, Engineering, Sales, and Security, ensuring our legal strategy moves at the speed of our innovation. 

This is an ideal opportunity for a high-performing lawyer with a strong grasp of transactional fundamentals who is ready to "level up." While your primary focus will be owning the commercial contract lifecycle, you will have a front-row seat to the broader legal challenges of a hyper-growth AI company.

You will be joining the Legal Team and report to our VP of Legal. This is a hybrid role based in our San Francisco or New York office with an in-office requirement of 2 days per week.

What You’ll Do

• Lead the drafting and negotiation of a wide variety of contracts, including Master Service Agreements (MSAs), NDAs, Data Processing Addenda (DPAs), and vendor agreements.
• Serve as the legal point of contact for CX, Sales, Engineering, and Finance teams to streamline deal cycles and mitigate risk.
• Navigate complex technology transactions, specifically focusing on intellectual property rights and data privacy frameworks in the context of AI and cloud computing.
• Beyond commercial matters, you will assist the VP of Legal on special projects, potentially spanning corporate governance, employment, and regulatory compliance as the company scales.

What You’ll Need

• 3–5 years of legal practice, with a focus on commercial or corporate transactions.
• A proven track record at a top-tier law firm, in-house at a high-growth tech company, or a strategic mix of both.
• A deep understanding of technology transactions, including the nuances of IP licensing and data privacy provisions.
• You are a confident negotiator who knows when to hold the line and when to find a creative solution to get the deal done.

Compensation

What We’re Looking For

We are looking for a Senior Finance Associate to drive priorities across strategic finance, corporate development, and FP&A as Lightning AI scales. You will partner with cross-functional stakeholders to shape unit economics, advise on deal pricing, and guide investment decisions. You will support GPU financing, fundraising, and investor relations to help Lightning AI acquire and allocate capital effectively. You will also build an operating cadence for P&L management, reporting, forecasting, and planning, turning data into decision-ready insights for leadership. 

The finance scope is deep, leadership visibility is high, and the work directly influences revenue growth, margin expansion, and capital allocation. If you want meaningful ownership, steep learning, and proximity to the decisions that shape an AI cloud, this role is for you.

This is a hybrid role based in our San Francisco or New York office with an in-office requirement of 2 days per week.

What You’ll Do

• Support deal reviews with Sales and leadership and recommend pricing decisions
• Partner with Marketing on spend allocation, pipeline efficiency, and unit economics analyses (LTV/CAC, payback period)
• Partner with Product on ROI for software investments and strategic initiatives
• Partner with Infrastructure Operations on capacity planning and CapEx assessment
• Model GPU financing structures and assess returns
• Support lender and investor communications, materials, and data rooms
• Monitor industry trends and the competitive landscape to identify potential business risks and opportunities
• Contribute to M&A sourcing and diligence models
• Build and maintain a driver based model for P&L, balance sheet, and cash flow statement
• Prepare executive-level business reviews for leadership and board
• Lead monthly and quarterly reporting, ensuring accuracy and highlighting key business drivers
• Develop forecast cycles and strengthen forecast accuracy by partnering closely with cross-functional stakeholders
• Support annual planning and long-range planning 
• Conduct rigorous analysis of key performance indicators, operational metrics, and business trends
• Manage FP&A systems and tooling and maintain data accuracy

What You’ll Need

• Bachelor's degree in Business Administration, Finance, Economics, or a related field
• 4+ years in Strategic Finance, FP&A, investment banking, private equity, or venture capital, ideally with a focus on software, AI, and/or cloud infrastructure
• Advanced proficiency in Excel and Google Sheets and experience building complex financial models
• Excellent written and verbal communication
• Track record of cross-functional partnership
• Analytical and strong problem-solving ability
• Team player with a growth mindset, positive attitude, and strong work ethic
• Bias to action and desire to work in a startup environment
• Experience with capital markets, M&A, or CapEx financing
• Exposure to cloud or AI infrastructure economics and data center capacity planning
• Familiarity with FP&A systems and data tooling (e.g., NetSuite)

Compensation

What We're Looking For

As a Machine Learning Solutions Engineer, you will operate at the intersection of machine learning, distributed systems, and cloud infrastructure.
You will partner with customers to design and deploy end-to-end AI systems, spanning:

• Model development and training
• GPU infrastructure and cluster design
• Distributed inference and production deployment

This role goes beyond traditional ML solutions engineering—you will act as a technical architect, helping customers make critical decisions across compute, orchestration, and system design.

The role can be based out of our New York City or San Francisco office, with an in-office requirement of at least 2-3 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time. The annual base pay range for this role is $150,000 - $195,000, in addition to a variable pay component and meaningful equity. 

What You’ll Do

Customer Architecture & Technical Leadership

• Partner with customers to understand ML workloads, infrastructure constraints, and scaling requirements
  
  
• Architect end-to-end solutions across:
  
  
• Data pipelines (CPU → GPU workflows)
  
  
• Distributed training (multi-node, multi-GPU)
  
  
• High-throughput inference systems
  
  
• Translate business goals (latency, cost, throughput) into technical system design decisions
  
  

GPU & Infrastructure Design

• Design and optimize workloads across GPU clusters (H100, H200, B200, etc.)
  
  
• Advise on:
  
  
• Training vs inference cluster design
  
  
• Interconnect choices (Ethernet vs Infiniband / RDMA vs Roce)
  
  
• Storage strategies (local NVMe vs networked / object storage)
  
  
• Model and optimize for:
  
  
• Tokens/sec, tokens/$
  
  
• Throughput vs latency tradeoffs
  
  
• GPU utilization and scheduling efficiency
  
  

Kubernetes & Platform Systems

• Design and support deployments on Kubernetes (EKS, GKE, on-prem clusters)
  
  
• Work with:
  
  
• GPU scheduling (time-slicing, MIG, bin-packing)
  
  
• Autoscaling and workload orchestration
  
  
• Helm-based deployments and multi-tenant environments
  
  
• Help customers balance:
  
  
• Raw Kubernetes flexibility vs platform abstraction (Lightning)
  
  

Demos, POCs, and Execution

• Build and deliver technical demos and POCs that showcase:
  
  
• Distributed training workflows
  
  
• Scalable inference endpoints
  
  
• End-to-end ML pipelines on Lightning AI
  
  
• Scope and lead POCs aligned to customer success metrics (latency, cost, reliability)
  
  

Cross-Functional Impact

• Act as the bridge between customers, product, and engineering
  
  
• Provide feedback on:
  
  
• Platform gaps in infrastructure, orchestration, and performance
  
  
• Emerging patterns in GPU usage and distributed systems
  
  
• Influence roadmap across ML workflows and infrastructure capabilities
  
  

Enablement & Thought Leadership

• Create technical content
• Architecture guides (e.g., high-throughput LLM inference systems)
• Best practices for GPU utilization and scaling
• Educate customers on modern AI infrastructure patterns

What You’ll Need

ML + Systems Expertise

• 3–6+ years experience in:
  
  
• Machine Learning / AI Engineering
  
  
• Solutions Engineering / Sales Engineering / ML Consulting
  
  
• Strong understanding of:
  
  
• Training vs inference workloads
  
  
• Model optimization (quantization, batching, caching, etc.)
  
  

GPU & Distributed Systems

• Experience working with:
  
  
• GPU clusters (NVIDIA stack preferred)
  
  
• Distributed training or inference systems
  
  
• Familiarity with:
  
  
• NCCL, CUDA, or GPU performance profiling
  
  
• Networking concepts (RDMA, Roce, Infiniband, high-throughput systems)
  
  

Kubernetes & Cloud Platforms

• Hands-on experience with:
  
  
• Kubernetes (EKS, GKE, or on-prem)
• Slurm 
• Containerization (Docker)
  
  
• Exposure to:
  
  
• GPU scheduling in Kubernetes environments
  
  
• Multi-tenant or production ML deployments
  
  

Programming & Tooling

• Strong Python skills (PyTorch preferred)
  
  
• Experience building:
  
  
• ML pipelines
  
  
• APIs or inference services
  
  
• Familiarity with Lightning AI, PyTorch Lightning, or similar frameworks is a plus
  
  

Customer-Facing Excellence

• • Ability to:
    
    
    • Explain complex infrastructure and ML tradeoffs clearly
      
      
    • Run technical discovery and uncover quantifiable success metrics
      
      
  • Experience working cross-functionally with:
    
    
    • Sales, product, and engineering teams

What We're Looking For

Lightning AI is seeking a Senior Storage Infrastructure Engineer to join our Infrastructure Engineering team.

In this role, you will focus on building and operating the storage systems that power large-scale AI/ML training, inference, and HPC workloads. You will work at the intersection of software, hardware, and operations—developing automation, improving reliability, and scaling distributed storage systems across our bare-metal infrastructure.

You will help own the data plane of our storage infrastructure, supporting high-throughput, low-latency data access for some of the most demanding AI workloads. You’ll play a key role in managing and evolving our storage stack (including VAST and S3-compatible systems like Ceph), ensuring performance, reliability, and efficiency at scale.

We’re flexible on location for this team. This role can work hybrid out of one of our US-based hubs (Seattle, NYC, or SF) or fully remote within the U.S., with occasional company and team offsites. We are not able to provide visa sponsorship for this position at this time.

What You'll Do

Storage Systems & Infrastructure

• Operate and scale distributed storage systems, including VAST and S3-compatible object storage (e.g., Ceph)
• Improve performance, reliability, and efficiency of storage systems supporting large-scale AI/ML workloads
• Troubleshoot complex storage and data path issues across hardware and software layers
• Optimize storage performance to support high-throughput, low-latency AI training and inference workloads

Automation & Tooling

• Build and maintain automation for provisioning, managing, and monitoring storage infrastructure
• Develop Python-based tools and workflows to reduce manual operational overhead
• Improve lifecycle management of storage clusters, from deployment through maintenance and scaling

Systems & Operations

• Manage and operate Linux-based systems in production, including bare-metal environments
• Partner with infrastructure and data center teams on hardware bring-up, upgrades, and issue resolution
• Support capacity planning, utilization tracking, and forecasting for storage systems
• Leverage monitoring and telemetry to diagnose issues and improve system performance and reliability

Cross-Functional Collaboration

• Work closely with Infrastructure Engineering, Network Engineering, and Platform teams to integrate storage into the broader platform
• Contribute to design discussions around new infrastructure deployments and scaling strategies
• Help define best practices for operating storage systems in high-performance computing environments

What You'll Need

Required Qualifications

• 5+ years of experience in infrastructure engineering, systems engineering, or related roles
• Hands-on experience operating distributed storage systems (e.g., VAST, Ceph, or similar)
• Strong Linux systems experience in production environments
• Proficiency in Python or similar scripting/programming languages for automation
• Experience working with bare-metal infrastructure and hardware-oriented systems
• Ability to debug complex issues across system boundaries (storage, OS, hardware, networking)
• Experience with storage networking protocols (e.g., NFS or similar)
• Experience with capacity planning, monitoring, and performance tuning

Ideal Experience

• Experience with VAST storage systems in production environments
• Experience operating S3-compatible object storage at scale
• Data center operations experience, including working with physical hardware
• Familiarity with AI/ML or HPC workloads and their storage requirements
• Background in high-performance or low-latency distributed systems
• Familiarity with high-performance data transfer technologies (e.g., RDMA, GPU Direct Storage)
• Experience supporting GPU-based workloads or large-scale compute clusters

Compensation

What We’re Looking For

Lightning AI is seeking an Observability Infrastructure Engineer to join our Infrastructure Engineering team.

In this role, you will own and evolve observability systems across large-scale, GPU-enabled bare-metal infrastructure. You’ll operate at the intersection of infrastructure, data, and product, building platforms for metrics, logs, traces, and alerting that power both internal operations and customer-facing visibility.

You will play a key role in productizing observability, enabling scalable, multi-tenant monitoring experiences while keeping pace with rapid infrastructure buildouts. This includes designing telemetry pipelines, improving signal quality, and delivering actionable insights that ensure reliability and transparency across our platform.

We’re flexible on location for this team. This role can work hybrid out of one of our US-based hubs (Seattle, NYC, or SF) or fully remote within the U.S., with occasional company and team offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

Observability Platform & Productization

• Own and evolve a scalable observability platform spanning metrics, logs, traces, and events
• Drive the productization of observability capabilities for both internal teams and external customers
• Design multi-tenant observability systems with scoped access, RBAC, and customer-facing visibility
• Continuously improve observability systems to keep pace with rapid infrastructure buildouts

Telemetry & Data Pipelines

• Design and operate telemetry pipelines ingesting data from GPUs, CPUs, networking (Ethernet & InfiniBand), containers, APIs, and BMC/Redfish
• Build systems to correlate signals across infrastructure layers to enable faster debugging and root cause analysis
• Implement streaming and real-time data pipelines using tools such as Kafka, OTEL, Promtail, or similar

Alerting, Reliability & Insights

• Design and implement noise-resistant alerting systems to improve signal quality and reduce operational load
• Create dashboards and alerting for InfraOps, Engineering, and Customer Success teams
• Build automated insights and enable proactive detection, forecasting, and system health visibility at scale

Systems & Infrastructure Engineering

• Contribute to broader infrastructure engineering projects beyond observability
• Partner with infrastructure and platform teams to embed observability into core systems and workflows
• Support large-scale, distributed systems across compute, networking, and storage environments

Cross-Functional Collaboration

• Work closely with customer-facing teams to deliver external observability experiences
• Collaborate with engineering, operations, and support teams to improve system transparency and reliability
• Help define best practices for observability across the organization

What You’ll Need

Required Qualifications

• 5+ years of experience in infrastructure engineering, SRE, or observability-focused roles
• Strong experience with monitoring systems such as Prometheus, Grafana, ELK, or VictoriaMetrics
• Experience building and operating observability platforms at scale
• Proficiency in Python, Go, or bash for automation and data integration
• Familiarity with containerized environments and Kubernetes observability
• Experience with streaming telemetry pipelines (Kafka, OTEL, Promtail, or equivalent)
• Experience with multi-tenant monitoring architectures
• Strong written and verbal communication skills

Ideal Experience

• Experience with GPU observability, particularly NVIDIA DCGM
• Experience monitoring large-scale GPU or HPC clusters
• Familiarity with InfiniBand fabric observability
• Experience building customer-facing or productized infrastructure systems
• Experience with correlation engines, RCA workflows, or predictive alerting systems
• Broad exposure to infrastructure domains including networking, storage, and provisioning

Compensation

Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. 

Our research spans:

• Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. 
• Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science
• Imaging foundation models trained across the world's largest microscopy datasets
• Reasoning and agentic systems that connect frontier LLMs with biological foundation models
• Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights
• Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled

Join our Team!

As an ML Engineer, you'll join some of the strongest infrastructure engineers in AI, building the systems that connect everything together. The infrastructure problems you solve directly determine what science becomes possible. 

What You'll Do

• Work with high-dimensional scientific data formats and contribute to backend compatibility, format evaluation, and I/O performance benchmarking at petabyte scale.
• Define and shape the engineering patterns your team and collaborating researchers will build on for years; the abstractions you write today become the foundation others depend on at scale.
• Work at the intersection of AI systems and biological discovery, where the infrastructure problems you solve directly determine what science becomes possible.
• Deploy models to production and manage artifact tracking across models and datasets.
• Design and optimize GPU-native data loading pipelines for large-scale multi-dimensional tensor workloads, including profiling and resolving hardware utilization bottlenecks across multi-backend systems.
• Work on simplification and improvement of codebase abstractions to accelerate research momentum.
• Build and maintain primitives for pre-training infrastructure that ensure the reliability and continuity of large-scale training runs.
• Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring.
• Possesses the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training.
• Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.
• Collaborate with partner researchers and engineers to deploy our technology within external infrastructure.

What You'll Bring

• Hands-on experience with PyTorch, including custom training loops, distributed training, or low-level performance work.
• Familiarity with GPU-native data I/O tools and large-scale tensor formats (e.g. Zarr, HDF5, TensorStore, or similar).
• Experience with distributed computing frameworks such as Apache Spark, Dask, or Ray.
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
• Experience building or working with AI agent frameworks is a plus.
• A track record of building systems that other engineers and researchers depend on. Not just running experiments, but shipping infrastructure that scales.

Compensation

The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $150,000 to $350,000+ annually. Compensation ranges will vary based on job-related skills, level of experience, and knowledge. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! 

#LI-Hybrid

What We're Looking For

Lightning AI is seeking a GPU & Compute Infrastructure Engineer to join our Infrastructure Engineering team.

In this role, you will own image management, system diagnostics, and validation across large-scale bare-metal compute infrastructure, with a particular focus on GPU-enabled systems. You will work at the intersection of hardware, systems, and software—developing automation, improving reliability, and enabling efficient cluster bring-up for AI/ML and HPC workloads.

You will play a key role in owning and evolving our image pipeline, running validation environments and test clusters, and supporting both system-level and GPU hardware qualification. This role is critical to ensuring that our infrastructure is consistent, performant, and ready to support demanding AI workloads from day one.

We’re flexible on location for this team. This role can work hybrid out of one of our US-based hubs (Seattle, NYC, or SF) or fully remote within the U.S., with occasional company and team offsites. We are not able to provide visa sponsorship for this position at this time.

What You'll Do

Systems, Image & Validation Infrastructure

• Own and evolve systems for image management, deployment, and validation across bare-metal infrastructure
• Run and maintain test clusters used for system validation, diagnostics, and bring-up
• Validate firmware, drivers, and OS images across compute and GPU-enabled systems
• Support hardware qualification efforts for next-generation platforms

GPU Diagnostics & Performance

• Own GPU diagnostics and validation workflows across large-scale infrastructure
• Diagnose and resolve complex issues across GPUs, drivers, OS, and hardware layers
• Analyze system and GPU performance using tools such as NVIDIA DCGM
• Identify failure patterns and drive improvements in system stability and validation coverage

Automation & Tooling

• Build and maintain automation for provisioning, validation, and system bring-up
• Develop Python-based tools and workflows to improve efficiency and reduce manual operational overhead
• Improve the reliability, repeatability, and scalability of image pipelines and validation systems

Systems & Operations

• Manage and operate Linux-based systems in production and validation environments
• Manage virtualization technology
• Support bare-metal provisioning workflows, including PXE and image-based systems
• Interface with hardware management systems (e.g., IPMI, Redfish) for monitoring and debugging

Cross-Functional Collaboration

• Partner with Infrastructure, Hardware, and Data Center teams on system bring-up and validation
• Collaborate with platform and ML teams to ensure systems meet workload requirements
• Contribute to best practices for provisioning, diagnostics, and lifecycle management of infrastructure

What You'll Need

Required Qualifications

• 5+ years of experience in infrastructure engineering, systems engineering, or related roles
• Strong Linux systems experience in production environments
• Hands-on experience with GPU-enabled systems and tools such as NVIDIA DCGM
• Familiarity with bare-metal provisioning and system bring-up workflows
• Proficiency in Python or similar scripting/programming languages for automation
• Ability to debug complex issues across hardware, OS, GPUs, and system software

Ideal Experience

• Experience with high-performance interconnects (e.g., InfiniBand, NVLink)
• Experience with PXE boot environments, LiveCD systems, or image-based provisioning workflows
• Experience with hardware management interfaces such as iDRAC, IPMI, or Redfish
• Data center operations experience, including working with physical hardware
• Experience supporting AI/ML or HPC workloads at scale
• Experience with GPU validation frameworks or large-scale hardware qualification processes

Compensation

What We're Looking For

We’re looking for a Fullstack Engineer to help develop and scale the Lightning AI platform across the frontend, CLI, APIs, and core backend systems. In this role, you’ll take ownership of key features and drive them end-to-end, working closely with engineering, product, and design to ship high-quality, iterative improvements. This is an opportunity to build systems that support real-world machine learning workloads at scale.

This role is based in one of our hubs (NYC, SF, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

• Write readable/testable/efficient code in React and either Python or Go (Golang).
• Build deep expertise in our technology stack to deliver new features, improve system stability, and increase overall performance.
• Partner with engineering and product leaders on developing the Lightning product while using your experience to set the technical direction for large projects.
• Evaluate, strengthen, and document technical architecture, tools, and processes.
• Maintain high standards for software quality, implement automation, drive continuous delivery, and reduce time to production while proactively reducing technical debt.
• Mentor and coach engineers on system design, operating in high uncertainty, and problem-solving to create a supportive, inclusive environment where each engineer can grow.

What You’ll Need

• Proficiency in React and either Go (Golang) or Python, with a preference for Go
• Strong understanding of software engineering principles and lifecycle
• Hands-on experience as a Fullstack Engineer in a SaaS technology company
• Proven ability to take ownership of key features and drive end-to-end development
• Communication and collaboration with engineers, product managers, and designers
• Ability to operate in high uncertainty and rapidly changing environments

Compensation

What We're Looking For

We’re looking for a Frontend Engineer to help develop and scale the UI and frontend infrastructure of the Lightning AI platform. In this role, you’ll take ownership of key features and drive them end-to-end, working closely with engineering, product, and design to ship high-quality, iterative improvements. With over 10,000 organizations building with Lightning, you’ll have a unique opportunity to impact how the world builds and deploys AI to production.

This role is based in one of our hubs (Seattle, NYC, SF, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

• Write readable/testable/efficient code in React and Redux, and build deep expertise in our technology stack to deliver new features, improve system stability, and increase overall performance.
• Partner with engineering and product leaders on developing the user interface and frontend infrastructure while using your experience to set the technical direction for large projects.
• Evaluate, strengthen, and document technical architecture, tools, and processes.
• Maintain high standards for software quality, implement automation, drive continuous delivery, and reduce time to production while proactively reducing technical debt.
• Mentor and coach engineers on system design, operating in high uncertainty, and problem-solving to create a supportive, inclusive environment in which each engineer can grow.

What You’ll Need

• Proficiency in React, JavaScript and TypeScript.
• Strong understanding of software engineering principles and lifecycle.
• Hands-on experience as a Frontend Engineer in a SaaS technology company.
• Proven ability to take ownership of key features and drive end-to-end development.
• Communication and collaboration with engineers, product managers, and designers.
• Ability to operate in high uncertainty and rapidly changing environments.

Compensation

What We're Looking For

We’re looking for a Backend Engineer to help develop and scale the Lightning AI platform, spanning APIs, infrastructure, billing, security, and integrations. In this role, you’ll take ownership of key features and drive them end-to-end, working closely with engineering, product, and design to ship high-quality, iterative improvements. This is an opportunity to build systems that support real-world machine learning workloads at scale.

This role is based in one of our hubs (Seattle, NYC, SF, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.

What You’ll Do

• Write readable/testable/efficient code in Go (Golang).
• Build deep expertise in our technology stack to deliver new features, improve system stability, and increase overall performance.
• Partner with engineering and product leaders on developing the Lightning product while using your experience to set the technical direction for large projects.
• Evaluate, strengthen, and document technical architecture, tools, and processes.
• Maintain software quality, implement automation, drive continuous delivery, and reduce time to production while proactively reducing technical debt.
• Mentor and coach engineers on system design, operating in high uncertainty, and problem-solving to create a supportive, inclusive environment where each engineer can grow.

What You’ll Need

• Extensive experience writing efficient, maintainable code in Go. Familiarity with best practices in testing, readability, and optimization is essential.
• Broad knowledge of backend and frontend systems, with experience building and scaling APIs, CLI tools, billing systems, and security protocols. Exposure to frontend frameworks is a plus.
• Experience with cloud platforms like AWS, GCP, or Azure and designing systems that are reliable, performant, and scalable. Familiarity with containerization (Docker, Kubernetes) is a strong advantage.
• Proven ability to take ownership of key features and drive end-to-end development
• Commitment to software quality, test automation, and continuous delivery. Experience proactively managing technical debt and speeding up time to production.
• Hands-on experience as a backend or product engineer in a SaaS technology company
• Communication and collaboration with engineers, product managers, and designers
• Ability to operate in high uncertainty and rapidly changing environments

Compensation

What We're Looking For

We are looking for an Account Executive with experience selling highly technical, developer-focused products to CTOs, VPs of Engineering, software engineers, researchers, and/or data scientists. You thrive in a fast-paced, start-up environment focusing on every step of the sales process, from prospecting, qualifying, and nurturing leads to running demos, negotiating contracts, and closing deals. We want someone who thinks about how to uncover and address our customers’ problems to drive customer acquisition while feeding their requirements back to inform the product roadmap.

This role is based out of our New York City office, with an in-office requirement of at least 3 days per week and occasional team and company offsites.

What You’ll Do

• Build an understanding of our target markets, value proposition, and pricing models to not only meet but exceed quarterly revenue targets.
• Focus on high-quality, consistent outbound prospecting across key market segments to build the sales pipeline and optimize the customer funnel.
• Qualify and develop both inbound and outbound sales leads.
• Collaborate with the product, engineering, and marketing teams to continuously improve and iterate on the sales process.
• Analyze industry trends and customer segmentation to identify and capitalize on new sources of qualified leads.
• Work alongside the marketing team to increase the number and quality of inbound leads.
• Champion data integrity and accuracy using HubSpot to track activity and manage sales opportunities through the funnel.

What You’ll need

• 5+ years of experience as an Account Executive or in a similar sales role, selling technical or developer-focused products to technical audiences.
• Familiarity with the machine learning, AI, or software development ecosystem, and an ability to grasp complex technical concepts.
• Proven track record of meeting or exceeding revenue targets in a fast-paced startup environment.
• Experience with outbound prospecting, qualifying leads, running demos, and closing deals is essential.
• Ability to uncover customer pain points, articulate value propositions, and effectively address objections to drive solutions.
• Strong interpersonal skills to work cross-functionally with leadership, product, engineering, and marketing teams.
• Proficiency with HubSpot or a similar CRM to track activity, manage the sales pipeline, and ensure data integrity.
• Solid understanding and application of a sales methodology (e.g., MEDDIC, Challenger, or Sandler) with the ability to tailor approaches to different customer needs and scenarios.

Compensation

This role offers $300K OTE, with a 50/50 base/variable split, in addition to meaningful equity. 

About the Role

We’re looking for a Director of Engineering to lead our Intelligent Messaging group—one of the most critical and high-impact areas at Attentive. This team powers the systems behind billions of personalized messages (SMS, email, push) sent every month, helping the world’s leading brands connect with consumers in smarter, more meaningful ways.

At the core of this is AI, machine learning, and real-time data. This allows us to drive personalization at massive scale - turning every message into an opportunity to engage, convert, and build long-term customer relationships.

In this role, you’ll lead multiple teams and play a key role in defining how AI shapes the future of messaging at Attentive. You’ll partner closely with Product and Engineering leadership to build and scale systems that are fast, reliable, and deeply intelligent. You’ll stay close to the work - guiding architecture, unblocking complex problems, and stepping in when it matters - while also building strong teams and developing great managers.

This is a role for someone who likes to operate deep in the systems, close to the people, and aligned with the business. You’ll own the technical direction of a high-scale, high-uptime platform and help us push what’s possible as we continue to grow.

This is a hybrid role based in NYC, with occasional travel to our SF office.

What You’ll Accomplish

• You’ll own the delivery of cutting-edge, AI-powered consumer messaging experiences used by millions
• You’ll manage and evolve a multi-team organization, build on our great culture, and set a high bar for engineering excellence
• You'll oversee the technical strategy in multiple domains, supporting various layers of our messaging applications by using existing and emerging technology platforms
• You’ll accelerate our engineering velocity using cutting-edge agentic and LLM tooling, while successfully managing safety and stability
• You’lll collaborate with product, ML, design, and data science to shape the roadmap - not just execute on it
• You’ll work directly with partner teams and stakeholders in the platform, go-to-market, and customer success organizations

Your Expertise

• Above all else, you are technical and love to dive deep to understand architecture, code, and infrastructure. This is the most critical attribute for success at Attentive.
• You have 10+ years of industry experience, including managing teams of 30+ engineers, and at least 2 years managing managers. From a technical perspective, you have 8+ years of experience in software engineering roles, ideally reaching staff level as an individual contributor.
• You embrace agentic workflows, and understand the benefits, complexity and risks of the fast-moving AI ecosystem
• You care about software quality and have a track record of building reliable, high-uptime systems, particularly in data-intensive environments
• You’re comfortable operating in fast-moving, high growth environments, translating business needs into the right levels of implementation

What We Use

• Our backend is Java / Kotlin / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Aurora, AirFlow, Postgres, and Redis, hosted via AWS
• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS, using tooling like Istio, Datadog, Terraform, CloudFlare, and Helm
• Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
• Our automation is driven by custom and open source machine learning models, industry-leading LLMs, lots of data and tech like Python, Metaflow, HuggingFace, PyTorch, TensorFlow, and Pandas

You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.

For US based applicants:

• The US base salary range for this full-time position is $250,000 - $350,000 annually + equity + benefits
• Our salary ranges are determined by role, level and location

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The opportunity
Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company.

Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform also supports large-scale model training, feature generation, and experimentation workflows that power production ML systems.

To support this growth, we need strong technical ownership to ensure our ML pipelines remain reliable, scalable, and architecturally sound.

We are seeking a staff ML engineer to design and evolve the large-scale offline platform. This role focuses on building reliable infrastructure for generating training datasets, orchestrating ML workflows, and enabling efficient, distributed model training at scale. You will work closely with ML engineers and platform teams to ensure our pipelines can efficiently handle growing data volumes and increasingly complex training workloads.

You will play a key role in shaping how model datasets are prepared as well as model training, validated, and delivered to distributed training systems, while ensuring the reliability, scalability, and performance of our offline ML platform.

What you'll be doing

• Design and operate large-scale data pipelines that generate training datasets used for machine learning training and experimentation
• Develop infrastructure that supports distributed training workflows using technologies such as Pytorch, Ray Data, and Ray Train, etc.
• Integrate ML pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows
• Improve reproducibility and observability of ML pipelines through dataset validation, monitoring, and automated testing
• Optimize performance and resource utilization across distributed compute systems used for data processing and model training
• Partner closely with ML engineers to enable efficient large-scale experimentation and model iteration
• Lead architectural improvements to ensure our offline ML pipelines remain scalable, reliable, and cost-efficient

What we're looking for

• Strong experience building large-scale ML pipelines
• Experience working with distributed computing frameworks such as Ray, Spark, Flink and familiarity in the Ray ecosystem (Ray Data, Ray Train) for distributed data processing and model training
• Experience building infrastructure for training data generation, dataset preparation, or ML feature pipelines
• Deep experience designing and operating production-grade data pipelines
• Strong programming skills in Python and experience working with large-scale distributed workloads
• Experience with modern data infrastructure (data lakes, warehouses, orchestration systems, streaming platforms)
• Strong systems thinking, with the ability to reason about performance, scalability, reliability, and cost tradeoffs in distributed systems
• Proven ability to lead technical direction and influence architectural decisions across teams without formal authority

Additional information

• Relocation support is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

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Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

Our Intern/Co-op program is designed to give you a real-world view of what it's like to be a Machine Learning Engineer at PathAI. You will be embedded directly into our AI product and ML Core teams, working on active projects that have a direct impact on our technical roadmap and business goals.

You will collaborate with a cross-functional team of scientists, engineers, and product managers to translate clinical needs into scalable ML solutions. This is your chance to contribute to meaningful work that directly relates to patient outcomes.

Timeline: This opportunity is for Fall 2026. We are looking for interns/co-ops who can commit to 3-6 months at PathAI (4-6 months preferred).

Location: We have offices in Boston, MA and New York, NY and would prefer interns/co-ops who can work from one of those locations—however, we will also consider remote candidates (US based only).

What You’ll Do

• Contribute to the design, development, and evaluation of machine learning models for our AI products, AI services and core ML models.
• Work on subprojects derived from our ongoing ML product roadmap, focusing on areas like model improvements with novel techniques.
• Partner with our MLOps and platform teams to help build and improve our ML pipelines.
• Participate in experimental design, analysis, and team knowledge-sharing initiatives like design reviews, technical forums and journal clubs.

What You Bring 

• Currently pursuing a M.S., or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering with a focus on Computer Vision and Machine Learning.
• Strong programming skills in Python and hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow).
• A solid grasp of the fundamentals of machine learning and/or computer vision.
• Excellent communication skills and a desire to work in a highly collaborative, cross-functional environment.
• A passion for our mission to improve patient outcomes.
• [Nice-to-have] Any high impact contributions through any AI/ML publications, open-source projects and any AI based products.

We are looking for team players who are passionate about what they do and are willing to tackle any task to contribute to our goals. You could be exactly who we are looking for!

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 
PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.  

About the Role
The Onsite Customer Growth team builds the features that help Attentive’s customers grow directly on their websites. Our mission is to accelerate list growth and improve conversion by delivering creative, high performing onsite experiences powered by strong engineering and intelligent AI driven optimization.

What You’ll Accomplish

• Lead, mentor, and grow a high performing engineering team responsible for building the backend systems powering onsite customer growth products
• Set the technical vision and roadmap for backend services, APIs, and data driven systems that power personalized onsite experiences at scale
• Partner closely with AI, data, and product teams to develop intelligent capabilities that improve performance and user experience
• Ensure the reliability, scalability, and performance of the systems that support customer growth and real time user interactions
• Drive best practices in design discussions, code reviews, architecture decision making, and operational excellence
• Foster a learning oriented team culture that encourages collaboration, curiosity, ownership, and continuous improvement
• Own delivery of key product initiatives, balancing long term platform investments with near term business objectives
• Promote clean, maintainable, well tested code and high engineering standards across the team

Your Expertise

• 7+ years of professional backend software engineering experience, including at least two years managing or leading engineers
• Strong proficiency in one or more modern programming languages such as Java, or Typescript
• Experience designing and operating distributed systems, APIs, and microservice architectures at scale
• Hands on familiarity with cloud platforms such as AWS and containerization technologies such as Docker or Kubernetes
• Deep understanding of databases, messaging systems, or streaming technologies such as PostgreSQL, Redis, or Kafka
• Track record of building reliable, scalable, high throughput systems that support customer facing products
• Demonstrated ability to coach and develop engineers, drive team alignment, and create an inclusive, collaborative engineering environment
• Strong communication and cross functional partnership skills, with the ability to work effectively with AI, data, product, and design teams
• A mindset rooted in ownership, curiosity, and a passion for building meaningful systems that drive measurable customer impact

What We Use

• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
• Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
• Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, AirFlow, Postgres, and Redis, hosted via AWS
• Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
• Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas

You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.

For US based applicants:

• The US base salary range for this full-time position is $193,000 - $250,000 annually + equity + benefits
• Our salary ranges are determined by role, level and location

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About the Role

The New York Times is a technology company committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company.

The Algorithmic Recommendations and Audience Data Science team aims to help users discover content across the Times' website, apps and emails by applying algorithms that make use of information about our readers' behavior and our editorial judgment. We also build internal tools for the newsroom to better understand story performance and coverage trends. We are a group of machine learning scientists and data analysts that partner with teams across The New York Times. We are looking for a Machine Learning Scientist to join the team and apply machine learning methods to meet this challenge. You will report to a Lead Machine Learning Scientist, and collaborate with partners across the company.

Responsibilities:

• You will reframe business and newsroom goals as machine learning tasks that deliver accurate predictions, relevant insights, and optimization

• You will adapt or develop machine learning algorithms in cases when existing algorithms are insufficient, while implementing simple approaches when appropriate

• You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs

• You will turn models into data products, collaborate with engineering teams, and integrate into processes throughout The Times

• You will communicate complex ideas in machine learning while collaborating with technical and non-technical colleagues in in engineering, analytics, product management, marketing, editorial, and executive leadership groups

• Demonstrate support and understanding of our value of journalistic independence and a commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

• PhD, MS + 2 years experience, or 3+ years work experience in machine learning, statistics, computational social science, applied mathematics, or another quantitative/computational discipline

• 2+ years experience with open source machine learning or statistical analysis tools

• 2+ years coding experience in Python

• 2+ years experience in SQL and manipulating large structured or unstructured datasets for analysis

Preferred Qualifications:

• 1+ year of experience with recommendation systems, using natural language processing and small and large language models.

• 1+ years of experience with deep learning architectures, fine-tuning, embeddings and Pytorch or Tensorflow

• 1+ years of experience translating ambiguous business questions into machine learning problems

• 1+ years of experience building data products, either internal or consumer-facing

REQ-020029

Expression of Interest

We know that for the most talented engineers, the right opportunity is about more than just a job title, it’s about the right challenge at the right time. Even if our current openings don't perfectly fit your timing and interests today, we still want to hear from you. We view this as a priority pathway to connect with curious, driven MLEs whose unique expertise can help us architect the next generation of planet-scale ML systems. 

At Moloco, our mission is to empower the global digital economy by turning massive first-party data into measurable business outcomes. Whether it’s optimizing real-time bidding for Moloco Ads or building AI-native stacks for Streaming and Commerce, we’re looking for engineers to help us lead the shift toward a more performance-driven, AI-powered internet.

If you’re excited to scale and innovate in a fast-moving environment, share your profile and we will reach out the moment we see a potential match for your experiences and future goals.

About the Role

As a Machine Learning Engineer at Moloco, you will design, train, and deploy the large‑scale models that power our programmatic advertising and commerce media products. You will work at the heart of our real-time bidding and pricing systems, helping shape an end-to-end ML ecosystem that processes billions of daily events. Your work will directly improve marketplace performance for global advertisers and publishers by optimizing for relevance, ROI, and user experience at a scale few companies can match.

The Opportunity

• Architect and iterate on high-performance models that improve ad relevance, click-through rates, and conversion performance.
• Productionize and maintain scalable ML pipelines - from data ingestion to online inference - on top of planet-scale infrastructure.
• Extract insights from massive datasets of user behavior and auction signals to define new features and refine modeling strategies.
• Translate business goals (revenue, ROI, engagement) into concrete modeling challenges and success metrics in collaboration with Product and Data Science.
• Validate innovation through rigorous experimentation, including A/B tests, offline evaluations, and counterfactual analyses.
• Balance marketplace health by implementing models that harmonize advertiser value with a high-quality user experience.
• Enhance system reliability by refining feature stores, monitoring data quality, and establishing robust debugging practices.
• Drive platform evolution, contributing to ML tools and guidelines that increase experimentation velocity and deployment speed.
• Optimize for performance, partnering with Infra teams to reduce latency and improve throughput for low-cost, high-scale decisioning.
• Elevate technical excellence by sharing best practices in modeling and production ML with the broader engineering team.

Expertise We Value:

MLEs at Moloco are hands-on builders of some of the world’s highest-scale ML workloads. They bridge technical innovation with global revenue outcomes, owning the critical systems that define our next generation of machine learning and optimization platforms.

While every journey is unique, we find that the most successful MLEs at Moloco bring a deep technical foundation in the following areas:

• Production ML at Scale: 5-12+ years of experience delivering ML solutions in high-stakes production environments (e.g., Ads, Recommenders, Search, or Marketplaces), with a track record of scaling models for massive throughput and low-latency requirements.
• Algorithmic Mastery: A strong foundation in ML and statistics applied to real-world business challenges, with expertise in areas such as ranking, calibration, exploration-exploitation, causal inference, or multi‑objective optimization.
• Full-Lifecycle Engineering: Deep understanding of the end-to-end ML lifecycle, including data pipelining, feature engineering, model architecture, inference optimization, and the deployment of models into online systems.
• Technical Versatility: Proficiency across a modern ML toolkit (Python, SQL, and frameworks like TensorFlow or PyTorch) and experience leveraging data processing stacks like Spark, Beam, or Flink to deliver results at scale.
• Operational Stewardship: Experience ensuring the long-term health of ML systems in live environments—monitoring performance, diagnosing drift, and iterating based on real-world feedback to maintain measurable impact.
• Technical Mentorship: A passion for advancing the team’s collective bar by sharing best practices for large-scale ML systems and guiding the technical direction of complex machine learning initiatives.

Our Tech Stack:

• Languages: Python, Go, C++
• ML Frameworks: TensorFlow, PyTorch, JAX
• Infrastructure: GCP, AWS, Kubernetes, Spark, BigQuery, BigTable

How You Work: 

• Your ethos is ownership
• You thrive in ambiguity and enjoy partnering with others to shape vague requirements into clear roadmaps with pragmatic tradeoffs.
• You take shared accountability for the end-to-end journey; communicating progress, surfacing risks early, and following through to ensure your work creates measurable value.
• You demonstrate strong judgment under uncertainty. 
• You prioritize based on long-term impact, over short-term noise, making deliberate decisions when information is incomplete.
• You explicitly weigh tradeoffs like speed vs. quality and remain agile enough to adjust your approach thoughtfully as new information/data emerges.
• You lead through clarity and active listening. 
• You reduce ambiguity for the team by asking the right questions upfront, tailor your communication to your audience, document your decisions, and bring helpful structure to complex discussions. 
• You provide thoughtful, evidence-based feedback when needed and align behind decisions once they are made.
• You collaborate early, instead of solving in isolation.
• You build trust across the organization by listening first and navigate conflicting needs with empathy, creating buy-in around priorities and scope. 
• You navigate technical tensions productively, disagreeing thoughtfully while maintaining momentum and strengthening professional relationships.
• You fall in love with the problem, not the solution
• You build for the long run, looking beyond immediate tasks to understand root causes, second-order effects, and the long-term health of our ML ecosystem. 
• When something breaks, you don’t just patch it; you seek structural improvements that prevent recurrence and elevate the technical bar for the entire organization.
• You prioritize customer impact and take action to make it real.
• You prioritize effective solutions that drive business impact over technical elegance, spotting opportunities that others might overlook. 
• You have the initiative to turn ideas into outcomes and the agency to identify and fix gaps, even when they aren’t yet on the formal roadmap.

You can apply for this role through our Open Positions page (or through our Internal Job Board if you are a current employee). We will review submissions on an ongoing basis.

Hudson River Trading (HRT) is hiring an AI Researcher to join the HAIL team. HAIL (HRT AI Labs) is the team at HRT responsible for developing and maintaining our most powerful models, which are used by our trading teams to drive a significant fraction of our trading. We are building and deploying "foundation models for markets", that ingest and train on vast amounts of market data, to make predictions about future market state. We are seeking experienced AI researchers to join our team to accelerate our efforts.

Researchers have great independence to pursue the research directions they think would be most impactful as part of a small focused team with minimal bureaucracy. They are enabled by state-of-the-art research clusters with very high GPU-to-researcher ratios, and supported by excellent engineering, hardware, and systems teams to realize their vision. Your work will be directly, clearly, highly impactful on the business, and it will be challenging: this is a field with no easy or obvious solutions. You'll be responsible for improving every part of our models: from featurization of data, to architecture design, to training dynamics, to how trading decisions are made.

Qualifications

• Must have two or more years work experience in deep learning research, for any domain: robotics, biology, chemistry, physics, audio, video, etc.
• Experience translating methods between areas of application is highly valued.
• LLM experience is valuable, but not necessary.
• Strong engineering skills valued, especially any of: GPU kernel development, lower-level Pytorch/JAX development, intermediate C++, FPGA/ASIC experience, HPC experience.

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. 

This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package which includes medical, dental, vision, basic life insurance, and enrollment in our company’s retirement savings plans. Employees will receive sick and parental leave, as well as other paid time off (including 20 vacation days and 10 paid holidays in the US). Please note that benefits and time off policies will vary across non-US locations.

Hudson River Trading (HRT) is a quantitative trading firm at the forefront of technological innovation. We build and deploy cutting-edge systems within one of the world’s most advanced computing environments to power our global trading operations. Our non-siloed, collaborative coding environment empowers talented engineers to make significant contributions and see their impact daily. At HRT, you'll be challenged to solve the most complex problems in trading alongside many of the brightest minds in the field.

We are seeking highly motivated and skilled Research Engineers who will work very closely with our Algo Developer (Quant Research) trading teams. These roles require exceptional programming fundamentals, a strong user focus, and the ability to thrive in a fast-paced environment with constantly evolving requirements. Multitasking and context switching are inherent aspects of this role, through which you will gain insight into the ideation and execution of some of the world's most sophisticated trading strategies.  

Research Engineers at HRT work on a wide variety of projects. Examples include:

• Designing highly customized quantitative systems that enable Algo Developers to easily express and backtest new predictive signals.
• Building world-class research tooling to speed up Algo Developers’ research iteration.
• Aiding the development and deployment of very large deep learning models for HRT’s live trading.
• Improving HRT’s monetization systems which turn our predictions into profitable trades.
• Evaluating sim vs. live differences to improve the fidelity of our simulations.
• Building systems to monitor and manage live trading.
• Specializing in specific time horizons, from high frequency trading to mid-to-low frequency hedge fund-style strategies.
• Working across a very wide variety of asset classes (from equities to fixed income to options) and regions (from North America to Asia Pacific) – you’ll interact with markets across the world!

Responsibilities

• Design, develop, and maintain robust, performant, and scalable software systems.
• Optimize existing codebases for performance, reliability, and efficiency.
• Debug complex issues across distributed systems in a time-sensitive environment.
• Optimize both the mechanics and ergonomics of Algo Developer research workflows; this requires developing a deep intuition for research methodologies.
• Balance the short term goals of individual trading teams with the longer term goals of HRT’s generalized trading platforms across trading teams. Because we don’t work in a pod environment, REs on different trading teams are expected to share technology and ideas.

Qualifications

• Completed a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. If you are currently pursuing your Bachelor's degree, please apply to our Campus/New Grad opportunities.
• Previous experience at a top-tier finance or technology company with an excellent track record of rapid advancement and superlative reviews.
• Experience with supporting internal users is preferred; bonus if you have real hands-on experience with quant research and/or ML/Deep Learning platforms.
• Experience with python numerical, ML and data-oriented libraries is a big plus (e.g. pandas, scikit, pytorch, etc.)
• Candidates should be prepared for technical interviews in either Python or C++. While there is no hard language requirement for RE roles, proficiency in one of these languages will be assessed during the interview process.
• Kindness and empathy is a real requirement. No “smart jerks” at HRT. 
• Strong understanding of data structures, algorithms, and design principles.
• Excellent communication skills, both written and verbal.
• HRT ships every day. The ability to thoughtfully, methodically, and reliably deploy new software and features is a critical skill. 
• If you have management experience, that’s always a bonus – but even so, expect to code!

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.

For more info about interviewing at HRT, see this blog!

Hudson River Trading (HRT) is looking for a Senior Software Engineer to focus on our Treasury Infrastructure. Coders at HRT work on small, highly productive, and efficient teams that design, improve, and maintain the technology that powers worldwide trading — at HRT, the code you write is our business. You’ll have an opportunity to work closely with Treasury Quantitative Researchers and our Funding Team to design and develop HRT's Treasury Optimization and Research Platform.

We are looking for highly skilled programmers who love to code and solve complex problems, appreciate a culture of collaboration, and thrive in a performance-driven environment. You can always expect to be challenged by the ever-changing financial markets, and find yourself working on critical software in an extremely fast-paced, real-time environment.

Responsibilities

• Contribute to the engineering design and development of HRT’s Treasury Optimization Platform.
• Collaborate with Treasury Researchers on HRT’s Treasury Research Infrastructure.
• Collaborate with Quantitative Developers as well as our Funding and Finance teams on managing cash and collateral requirements.

Qualifications

• Completed a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. If you are currently pursuing your Bachelor's degree, please apply to our Campus/New Grad opportunities.
• Working experience in Python and good CS fundamentals.
• Proficiency with numerical computing libraries (Numpy, PyTorch, TensorFlow) is a plus.
• Knowledge of global portfolio financing and funding is a plus.
• Strong problem-solving skills and ability to work in a fast-paced environment.
• Excellent communication skills and ability to collaborate with engineers, researchers, and non-technical teams.

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.

Role Description

• As an Senior Value Engineer specializing in CPG and Fleet Logistics, you are pushing the envelope in solving business-critical problems for some of the world's most recognized consumer brands and global mobility networks. You will be working intimately with our most strategic enterprise clients, understanding their uniquely complex objectives—spanning from high-velocity beverage manufacturing and Direct Store Delivery (DSD) optimization to massive-scale fleet lifecycle management and dynamic asset routing. You will build Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks).

  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers' massive supply chains, vehicle fleets, and distribution networks. This enables them to industrialize AI, unlocking real ROI on deployments at scale—ensuring products are always on the shelf and vehicles are always ready for the customer. There is no AI without PI. You will prototype these solutions, demonstrate their value to global Chief Supply Chain Officers (CSCOs) and Fleet Operations Executives, and ensure successful implementation, adoption, and value realization to solidify and expand the footprint of Celonis across these premier global networks.

Key Responsibilities:

• AI Discovery & Solutioning: Understand the clients' overarching AI strategies and the distinct supply chain challenges inherent to high-volume CPG and fleet mobility (e.g., predictive demand forecasting, route optimization, fleet predictive maintenance, dynamic asset allocation, and mitigating raw material volatility). As a Celonis product and logistics domain expert, translate these complex, physical-world challenges into innovative AI solutions that drive measurable impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the expansion/pre-sales cycle within various manufacturing, distribution, and fleet management business units. Remain deeply involved post-sale to guide implementation, ensuring agreed value, efficiency, and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box with a "can-do" attitude, tackling heavily siloed legacy logistics and telematics networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in client hackathons, solving critical pain points across inventory allocation, fleet routing, and DSD execution.

• Agentic Process Transformation: Support these enterprise clients in achieving tangible ROI from AI at scale. Enable a fundamental shift from traditional, rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform (e.g., autonomous fleet rebalancing, intelligent shipment exception handling, or automated trade promotion deductions).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, ensuring seamless integration with complex enterprise ERPs (e.g., SAP), Transportation Management Systems (TMS), Warehouse Management Systems (WMS), and massive IoT/telematics data streams.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for CPG and Mobility Supply Chains, scaling knowledge across the organization regarding the nuances of fast-moving consumer goods and asset-heavy fleet operations.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within highly complex Supply Chain, CPG/Beverage, or Fleet Logistics environments. This includes defining AI roadmaps, building compelling ROI/TCO business cases for large-scale distribution networks, and guiding technical implementations through to value realization.

• Deep understanding of supply chain business processes native to high-volume manufacturing and mobility (such as Direct Store Delivery (DSD), Order-to-Cash, Procure-to-Pay, Fleet Lifecycle Management, or Sales & Operations Planning) with the ability to translate high-level operational needs into specific AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent dispatch assistants, or automated extraction of data from complex vendor contracts and logistics documentation).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for handling massive, high-velocity transactional and IoT datasets.

• Strong presentation skills to both internal and external stakeholders (including supply chain executives, logistics directors, and enterprise IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, supply chain management, logistics, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for enterprise-grade supply chain environments.

• Familiarity with CPG and fleet-specific systems (e.g., SAP APO/IBP, Blue Yonder, Manhattan, or major telematics/fleet tracking platforms).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

What You’ll Do:

A lab signs with CoreWeave. They have thousands of GPUs waiting. Their first month? Spent on cluster setup instead of research. Building deployment scripts, fighting container images, wiring up job orchestration. They came to train models. Instead, they're doing operations.

This is the problem. We're building the Applied Training team to fix it.

You'll be an early member of a small team, responsible for our Kubernetes-native research cluster platform, or the sandbox client for agentic training and evaluation, or possibly a new project altogether. The goal is specific: Give every CoreWeave customer the research infrastructure that currently only exists inside frontier labs.

About the role:

• Contribute to the roadmap for Applied Training. Figure out what actually unlocks new workloads and what's just nice to have. Work directly and closely with customers, and other teams inside of CoreWeave that are building cloud native primitives. Compute, storage, networking, etc.
• For the research cluster platform: design and build a complete research cluster experience. CLI, job configuration schema, Kubernetes operators, daemons. Solve the problems researchers actually hit: code distribution, checkpoint-triggered evaluation, cross-cluster scheduling, programmatic job control. Replace the patchwork of scripts customers keep building on their own.
• For sandbox infrastructure: own the Python SDK and work in a very tight loop with the backend team. When an RL training run needs to spawn thousands of isolated containers for agent rollouts, that's this system. When someone wants to run agent benchmarks at scale, that's this system. Make it work with our Kubernetes clusters, storage, and auth so researchers don't have to think about infrastructure.
• Write the documentation for running popular OSS training frameworks on CoreWeave. The work that can unblock customers and help them succeed.
• Work with infrastructure teams and customers directly. The customers are large AI labs running thousands of GPUs. Understand how they structure their internal supercomputing stacks. Bring that knowledge back to what we build.

Who You Are:

• 8-12+ years building distributed systems, ML infrastructure, or developer platforms.
• Real Kubernetes experience: custom controllers, operators, scheduling, CRDs, workload orchestration at scale. Not just deploying things to Kubernetes or cluster administration.
• Excited about rigorous engineering, but enabled by AI based workflows.
• You understand what makes researchers productive. Code distribution matters. Fast iteration cycles matter. Workflows that don't require becoming infrastructure experts matter.
• Familiarity with training: how distributed jobs get scheduled, how ranks initialize, what breaks at scale.
• You've shipped infrastructure that other people rely on daily. Not prototypes. Production systems.
• Good communicator. Can work with customers, translate researcher complaints into system designs.

Preferred:

• Experience building internal ML platforms or research clusters at a company doing large-scale training.
• Familiarity with agentic AI: RL training with rollouts, agent evaluation, sandbox isolation for running untrusted code.
• Background with Slurm, Ray, or similar workload orchestration. Opinions on where they fall short.
• Experience with container runtimes, isolation (gVisor, Kata), or serverless platforms.
• OSS contributions to Kubernetes SIGs, Ray, PyTorch, or similar.

Wondering if you’re a good fit? We believe in investing in our people and value candidates who can bring their diverse experiences to our teams – even if you aren't a 100% skill or experience match.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do
The Field Engineering team at Weights & Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

About the Role

We’re hiring a Post-Sales AI Solutions Engineer (AISE), Scale Customer Success to help customers successfully implement and scale AI/ML workflows and GenAI/agentic applications on Weights & Biases. You will design and deliver technical enablement and adoption programs that reach many customers at once, create reusable assets that improve self-serve success, and use product signals and feedback loops to continuously improve outcomes.

Key Responsibilities 

1. Run 1-to-many onboarding and enablement programs
   Own and deliver scalable onboarding and adoption motions (webinars, cohort sessions, group training, and office hours) that help customers get to value quickly and consistently.
2. Build reusable technical assets that drive self-serve success
   Create and maintain playbooks, reference architectures, templates, sample code/notebooks, and troubleshooting guides that standardize best practices and reduce repeated 1:1 work.
3. Operate the scaled motion using signals and feedback loops
   Use product usage signals and customer patterns to segment and trigger the right interventions, track program impact (activation, time-to-first-value, feature adoption), and feed recurring insights back to Support, Product, and Field Engineering to continuously improve the scaled journey.

Who You Are

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Experience with deep learning frameworks (TensorFlow/Keras, PyTorch Lightning) and tools (e.g., Streamlit, LangChain)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Excellent communication and presentation skills, both written and verbal
• Organized and outcomes-driven: you can run programs, measure impact, and iterate.

Preferred

• Experience with GenAI and LLMs
• Proficiency with HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, or Ray
• Experience with hyperparameter optimization solutions
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love collaborating with top AI teams to solve real-world ML and GenAI challenges
• You’re curious about how to optimize AI workflows at scale
• You’re an expert in Python and ML tooling, with a passion for enabling customer success

Why Us?
We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do:

A lab signs with CoreWeave. They have thousands of GPUs waiting. Their first month? Spent on cluster setup instead of research. Building deployment scripts, fighting container images, wiring up job orchestration. They came to train models. Instead, they're doing operations.

This is the problem. We're building the Applied Training team to fix it.

You'll be an early member of a small team, responsible for our Kubernetes-native research cluster platform, or the sandbox client for agentic training and evaluation, or possibly a new project altogether. The goal is specific: Give every CoreWeave customer the research infrastructure that currently only exists inside frontier labs.

About the role:

• Contribute to the roadmap for Applied Training. Figure out what actually unlocks new workloads and what's just nice to have. Work directly and closely with customers, and other teams inside of CoreWeave that are building cloud native primitives. Compute, storage, networking, etc.
• For the research cluster platform: design and build a complete research cluster experience. CLI, job configuration schema, Kubernetes operators, daemons. Solve the problems researchers actually hit: code distribution, checkpoint-triggered evaluation, cross-cluster scheduling, programmatic job control. Replace the patchwork of scripts customers keep building on their own.
• For sandbox infrastructure: own the Python SDK and work in a very tight loop with the backend team. When an RL training run needs to spawn thousands of isolated containers for agent rollouts, that's this system. When someone wants to run agent benchmarks at scale, that's this system. Make it work with our Kubernetes clusters, storage, and auth so researchers don't have to think about infrastructure.
• Write the documentation for running popular OSS training frameworks on CoreWeave. The work that can unblock customers and help them succeed.
• Work with infrastructure teams and customers directly. The customers are large AI labs running thousands of GPUs. Understand how they structure their internal supercomputing stacks. Bring that knowledge back to what we build.

Who You Are:

• 5-8+ years building distributed systems, ML infrastructure, or developer platforms.
• Real Kubernetes experience: custom controllers, operators, scheduling, CRDs, workload orchestration at scale. Not just deploying things to Kubernetes or cluster administration.
• Excited about rigorous engineering, but enabled by AI based workflows.
• You understand what makes researchers productive. Code distribution matters. Fast iteration cycles matter. Workflows that don't require becoming infrastructure experts matter.
• Familiarity with training: how distributed jobs get scheduled, how ranks initialize, what breaks at scale.
• You've shipped infrastructure that other people rely on daily. Not prototypes. Production systems.
• Good communicator. Can work with customers, translate researcher complaints into system designs.

Preferred:

• Experience building internal ML platforms or research clusters at a company doing large-scale training.
• Familiarity with agentic AI: RL training with rollouts, agent evaluation, sandbox isolation for running untrusted code.
• Background with Slurm, Ray, or similar workload orchestration. Opinions on where they fall short.
• Experience with container runtimes, isolation (gVisor, Kata), or serverless platforms.
• OSS contributions to Kubernetes SIGs, Ray, PyTorch, or similar.

Wondering if you’re a good fit? We believe in investing in our people and value candidates who can bring their diverse experiences to our teams – even if you aren't a 100% skill or experience match.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! 

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

The Account Manager owns the commercial and relationship aspects of the post-sales journey across a portfolio of Digital Native and select Enterprise customers. You will drive renewals, identify and close upsell and cross-sell opportunities, and ensure customers achieve measurable adoption outcomes with Weights & Biases (W&B). You’ll partner closely with Field Engineering (FE)—who leads technical success—while you lead the commercial motions including renewal execution, usage-to-value alignment, growth pipeline creation, and multi-threaded stakeholder engagement.

About the role:

You will own renewals and lead commercial execution across your assigned accounts, including identifying and closing upsell and cross-sell opportunities. You will partner closely with Field Engineering (FE) while running the commercial motions: renewal execution, usage-to-value alignment, growth pipeline creation, and multi-threaded stakeholder engagement. You will maintain detailed account plans (account maps, whitespace, usage trends, risks, and growth opportunities) and maintain weekly forecasting accuracy and CRM hygiene across renewals and growth motions. This role requires comfort engaging highly technical personas (ML engineers, researchers, PhDs) and operating with autonomy in a rapidly evolving AI ecosystem; it is not a playbook-driven role.

• Own renewals, upsells, and cross-sells across your assigned accounts.
• Build and maintain detailed account plans including account maps, whitespace, usage trends, risks, and growth opportunities.
• Generate growth pipeline by identifying new use cases, teams, and product opportunities within existing accounts.

Who You Are:

• Based in the Bay Area (San Francisco, Peninsula, South Bay) strongly preferred; secondary options: New York City or Northern NJ.
• 6+ years in B2B/SaaS customer-facing roles commercializing technical products.
• 3+ years in a quota-carrying Account Management, Sales, or Renewals role owning post-sales outcomes.
• Strong, genuine interest in AI/ML and the evolving machine learning ecosystem.
• High technical and product curiosity; comfortable speaking with developers, ML engineers, and researchers.
• Proven ability to drive growth motions (upsells, cross-sells) and manage retention in technical accounts.
• Strong operational discipline: forecasting accuracy, CRM hygiene, renewal cadence management.
• Thrives in ambiguity; able to self-direct and operate independently without detailed playbooks.

Preferred:

• Experience working with ML, MLOps, DevOps, or data infrastructure teams.
• Familiarity with Git, Jupyter, Python, PyTorch, or cloud platforms (AWS, GCP, Azure).
• Experience designing or executing account activation or expansion strategies.
• Exposure to AI-native companies, model builders, or generative AI workflows.

Wondering if you’re a good fit?

We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love owning renewals and growth motions while building multi-threaded stakeholder relationships.
• You’re curious about AI/ML and enjoy learning quickly in a rapidly evolving ecosystem.
• You’re an expert in operating with strong discipline across account plans, forecasting, and CRM hygiene to drive commercial outcomes.

The base salary range for this role is $95,000 to $130,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. 

Lyft Ads is one of Lyft’s newest and fastest-growing businesses, focused on building the world’s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey—before, during, and after a ride—by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.

We are seeking an Algorithms Scientist  to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You’ll collaborate closely with Engineering, Product, Data Science, and Sales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.

This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders—pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.

Responsibilities:

• Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
• Own the end-to-end lifecycle of modeling projects — including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
• Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
• Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
• Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
• Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
• Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
• Investigate and resolve model behavior issues, production regressions, calibration drift, and performance anomalies in close partnership with Ads Infra teams.
• Drive innovation by staying current with advances in ML for ranking, recommendation, causal inference, optimization, and ads measurement — and proactively identifying opportunities to apply them.
• Contribute to Lyft Ads’ modeling and experimentation infrastructure, through model cards, documentation, reproducibility standards, and code quality improvements.

Experience:

• Master’s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
• 3–5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains.
  Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
• Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
• Practical experience building and evaluating:
• Ranking and relevance models
• Optimization or pacing algorithms
• Predictive models for CTR, CVR, or user response
• Causal or experimentation-based measurement methods
• Understanding of online/offline evaluation techniques, including:
• Offline metrics (AUC, NDCG, MRR, calibration)
• A/B testing methodologies
• Bias correction and counterfactual estimation
• Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
• Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
• Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
• Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the New York City area is $128,000 - $160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. 

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers.

We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments.

Responsibilities:

• Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media.
• Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.
• Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.
• Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.
• Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.
• Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.
• Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience.
• Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis.
• Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning.

Experience:

• PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience.
• 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.
• 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.
• Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.
• Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
• Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.
• Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.
• Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
• Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
• Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. 

Lyft Ads is one of Lyft’s newest and fastest-growing businesses, focused on building the world’s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey—before, during, and after a ride—by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.

We are seeking an Algorithms Scientist  to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You’ll collaborate closely with Engineering, Product, Data Science, and Sales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.

This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders—pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.

Responsibilities:

• Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
• Own the end-to-end lifecycle of modeling projects — including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
• Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
• Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
• Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
• Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
• Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
• Investigate and resolve model behavior issues, production regressions, calibration drift, and performance anomalies in close partnership with Ads Infra teams.
• Drive innovation by staying current with advances in ML for ranking, recommendation, causal inference, optimization, and ads measurement — and proactively identifying opportunities to apply them.
• Contribute to Lyft Ads’ modeling and experimentation infrastructure, through model cards, documentation, reproducibility standards, and code quality improvements.

Experience:

• Master’s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
• 3–5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains.
  Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
• Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
• Practical experience building and evaluating:
• Ranking and relevance models
• Optimization or pacing algorithms
• Predictive models for CTR, CVR, or user response
• Causal or experimentation-based measurement methods
• Understanding of online/offline evaluation techniques, including:
• Offline metrics (AUC, NDCG, MRR, calibration)
• A/B testing methodologies
• Bias correction and counterfactual estimation
• Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
• Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
• Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
• Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $128,000 - $160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. 

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers.

We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments.

Responsibilities:

• Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media.
• Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.
• Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.
• Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.
• Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.
• Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.
• Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience.
• Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis.
• Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning.

Experience:

• PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience.
• 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.
• 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.
• Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.
• Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
• Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.
• Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.
• Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
• Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
• Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the New York City area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production.

Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.

We are focused on two research areas:

1. World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
2. Trained Agents for Observability-- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.

What You'll Do:

• Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling
• Implement models, run experiments at scale, and profile for reliability, performance, and cost
• Build simulation environments and replay infrastructure for agent training and evaluation
• Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery
• Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity
• Collaborate with Research Scientists, Product, and Engineering to integrate capabilities into Datadog's products and to harden prototypes into reliable services
• Contribute to research publications at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and produce high-quality code, documentation, and open-source artifacts

Who You Are:

• You have depth in distributed computing, RL Infra, and ML systems for training and inference at scale; experience with Ray, Slurm, or similar frameworks is a plus
• You are proficient in Python, familiar with a systems language (e.g., Rust, C++, or Go), and comfortable with modern cloud and data infrastructure
• You have practical experience implementing and operating ML training and inference systems (e.g., PyTorch or JAX), including containerization, orchestration, and GPU acceleration
• You have practical experience with large-scale model training and fine-tuning, including frameworks like Megatron-LM, DeepSpeed, SkyRL, VeRL, or TorchTitan, and techniques such as SFT, RLVR, RLHF, and efficient inference (quantization, speculative decoding)
• You can explain design and performance trade-offs clearly to both technical and non-technical audiences
• You have experience supporting or contributing to research publications

Bonus Points (any of the following):

• You have strong software engineering skills with experience in domains such as observability, SRE, or security
• You have experience bridging research prototypes and real-world product applications, especially with large foundation models, world models, or RL-trained agents
• You have a passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
• You have hands-on experience with GPU programming and optimization, including CUDA
• You have experience writing production data pipelines and applications
• You have experience building simulation or sandbox environments for agent training

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Competitive global benefits 
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
• Opportunity to attend and present at conferences and meetups
• Intra-departmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

About Datadog: 

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

Equal Opportunity at Datadog:

Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Your Privacy:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.

#LI-Hybrid

As a Product Manager II for Model Lab, you will define and launch Datadog’s experiment tracking platform built for teams training and fine-tuning foundational models. Model Lab centralizes metrics, hyperparameters, datasets, code versions, artifacts, and lineage to help ML and AI teams achieve reliable, reproducible, and explainable training runs at scale.

This is a 0→1 opportunity to build a new product that will be deeply integrated into Datadog’s observability platform. You will shape the product vision, validate the market, and drive execution for a new category-defining offering that serves AI research, ML platform, and applied AI teams.

At Datadog, we value our office culture — the relationships it builds, the creativity it fosters, and the collaboration that comes from working together. We operate as a hybrid workplace to help employees create a work-life harmony that fits their needs.

What You'll Do:

• Define the vision and strategy for Model Lab, establishing Datadog’s position in experiment tracking and model training observability
• Lead 0→1 product discovery with AI research teams, ML platform engineers, and infrastructure leaders to deeply understand experiment tracking workflows and pain points
• Design a system that unifies training metrics, hyperparameters, artifacts, dataset lineage, and model evaluation into a coherent and scalable experience
• Identify differentiation opportunities vs. competitive alternatives and homegrown internal tooling
• Partner closely with engineering and design to ship foundational capabilities such as experiment lineage, artifact versioning, distributed training visibility, and reproducibility workflows
• Collaborate with go-to-market teams to define packaging, positioning, and early customer programs
• Drive early customer adoption, including design partnerships and private beta programs
• Translate ambiguous, research-oriented workflows into clear product primitives and APIs
• Establish success metrics and iterate quickly based on user feedback and usage data

Who You Are:

• You have 4+ years of Product Management experience, with at least one 0→1 product or major feature launch
• You are entrepreneurial and comfortable operating with ambiguity, defining problems before defining solutions
• You have experience with developer tools, ML infrastructure, data platforms, or AI/LLM systems
• You understand (or can quickly ramp on) modern ML training workflows — distributed training, experiment tracking, hyperparameter tuning, artifact storage, evaluation pipelines
• You have familiarity with frameworks such as PyTorch, TensorFlow, JAX, or distributed training frameworks
• You are opinionated about product quality and developer experience, especially around APIs, SDKs, and workflows
• You can translate highly technical concepts into compelling product narratives
• You are comfortable making prioritization tradeoffs in fast-paced, competitive markets
• You thrive in cross-functional environments and can align engineering, design, and GTM around a clear strategy
• You are excited to build in a rapidly evolving AI ecosystem

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. If you’re passionate about building technical products and want to shape the future of AI infrastructure, we encourage you to apply.

Benefits and Growth:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of employment and the nature of employment with Datadog.

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products.

Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.

We are focused on two research areas:

1. World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
2. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.

What You'll Do:

• Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability
• Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure
• Design and build simulated environments and RL training loops for on-policy agent training and evaluation
• Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products
• Stay at the forefront of foundation models, world models, and RL-based agent research
• Contribute to research publications, present at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks

Who You Are:

• You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)
• You have extensive experience designing and implementing deep learning models and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch)
• You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
• You are familiar with efficient training, post-training, and inference techniques for large foundation models
• You can explain complex models and research findings to both technical and non-technical audiences

Bonus Points (any of the following):

• Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents
• Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
• Experience writing production data pipelines and applications
• Hands-on experience with GPU programming and optimization, including CUDA

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Competitive global benefits 
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
• Opportunity to attend and present at conferences and meetups
• Intra-departmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

About Datadog: 

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

Equal Opportunity at Datadog:

Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Your Privacy:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.

About us:

• We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London)
• We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home
• We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone’s voice, perspective and experience is respected and heard
• We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities

Benefits:

• Comprehensive health benefits including medical, dental, and vision plans, as well as mental health resources
• Robust 401k program
• Employee Equity Plan
• Generous professional development allowance
• Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
• Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
• 16 paid weeks for all parents (birthing and non-birthing), regardless of gender, to welcome newborns, adopted and foster children
• Various snacks, goodies, and 2 free lunches weekly when at the office

Diversity, Equity and Inclusion at SoundCloud

SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. We are dedicated to creating an inclusive environment at SoundCloud for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or care-giver status.

At SoundCloud you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee-organized groups focused on supporting and promoting the interests of a particular underrepresented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.

About us:

• We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London)
• We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home
• We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone’s voice, perspective and experience is respected and heard.
• We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities

Benefits:

• Not located in Berlin? No worries, we offer extensive relocation support including allowances, one way flights, temporary accommodation and, by partnering with Expath, on the ground support on arrival
• Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
• Employee Equity Plan
• Generous professional development allowance
• Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
• We offer free German courses at beginning, intermediate and advanced
• Various snacks, goodies, and 2 free lunches weekly when at the office

Diversity, Equity and Inclusion at SoundCloud

SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. We are dedicated to creating an inclusive environment at SoundCloud for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or care-giver status.

At SoundCloud you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee-organized groups focused on supporting and promoting the interests of a particular underrepresented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.

What We're Looking For

We are seeking a highly skilled Research Engineer to work on optimizing training and inference workloads on compute accelerators and clusters, through the Lightning Thunder compiler and the broader PyTorch Lightning ecosystem. This role sits at the intersection of deep learning research, compiler development, and large-scale system optimization. You’ll be shaping technology that pushes the boundaries of model performance and efficiency, creating foundational software that will impact the entire machine learning ecosystem.

This role is based in one of our hubs (NYC, SF, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites.

What You'll Do

• Develop performance-oriented model optimizations at multiple levels:
• Graph-level (e.g., operator fusion, kernel scheduling, memory planning)
• Kernel-level (CUDA, Triton, custom operators for specialized hardware)
• System-level (distributed training across GPUs/TPUs, inference serving at scale)
• Advance the Thunder compiler by building optimization passes, graph transformations, and integration hooks to accelerate training and inference workloads.
• Work across the software stack to ensure optimizations are accessible to end users through clean APIs, automated tooling, and seamless integration with PyTorch Lightning.
  Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.
• Collaborate with hardware vendors and ecosystem partners to ensure Thunder runs efficiently across diverse backends (NVIDIA, AMD, TPU, specialized accelerators).
• Contribute to open-source projects by developing new features, improving documentation, and supporting community adoption.
• Engage with researchers and engineers in the community, providing guidance on performance tuning and advocating for Thunder as the go-to optimization layer in ML workflows.
• Work cross-functionally with Lightning’s product and engineering teams to ensure compiler and optimization improvements align with the broader product vision.

What You’ll Need

• Strong expertise with deep learning frameworks such as PyTorch
• Hands-on experience with model optimization techniques, including graph-level optimizations, quantization, pruning, mixed precision, or memory-efficient training.
• Knowledge of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling).
• Familiarity with software engineering practices: designing APIs, building robust tooling, testing, CI/CD for performance-sensitive systems.
• Excellent collaboration and communication skills, with the ability to partner across research, engineering, and external contributors.
• Bachelor’s degree in Computer Science, Engineering, or a related field.

Nice-to-Haves

• Experience with CUDA, Triton, or other GPU programming models for developing custom kernels.
• Deep understanding of deep learning compiler internals (IR design, operator fusion, scheduling, optimization passes) or proven work in performance-critical software.
• Proven track record contributing to open-source projects in ML, HPC, or compiler domains.
• Advanced degree (Master’s or PhD) in machine learning, compilers, or systems highly preferred.

Compensation

Senior Manager, Data Science

Portfolio Management and Loss Mitigation

The Role: 

SoFi is seeking an inspirational and deeply experienced Senior Manager to lead the Portfolio Management and Loss Mitigation function in our Risk Data Science team. Reporting to the Head of Risk Data Science, this role will lead the development, deployment, and governance of portfolio management and loss mitigation models for all credit products.

The ideal candidate is a hands-on leader who can transition the team from traditional modeling to next-generation machine learning platforms, leveraging emerging data sources (e.g., cash flow, alternative bureaus) to significantly improve portfolio performance, reduce losses, and ensure rigorous adherence to Model Risk Management (MRM) standards. This role requires exceptional organizational leadership, an ability to influence stakeholders, and proven success in delivering complex models into a regulated production environment.

What You'll Do: 

• Portfolio Management and Loss Mitigation Excellence: Directly oversee the development and deployment of Next Generation models designed to manage and mitigate portfolio losses while maintaining loss guardrails.
• Drive Next-Generation Capabilities: Incorporate industry trends and advanced techniques (NLP, Graph Mining, LLMs, Deep Learning) to solve complex, high-impact risk problems where established principles may not fully apply.
• Alternative Data Strategy: Spearhead the evaluation and integration of alternative data sources (Macro-Economic variables, tri-bureau, LexisNexis, cash flow data) to enhance predictive power across all credit products.
• Lead the current team of high-performing Staff and Senior Data Scientists. Recruit, mentor, and foster talent through deliberate interactions, succession planning, and creating a high-accountability, low-ego culture.
• Model Risk Management (MRM): Act as the primary owner for all models in the portfolio, ensuring robust documentation, monitoring, and successfully navigating the 2nd Line of Defense (2LOD) review and approval process (SR 11-7 familiarity is mandatory).
• Stakeholder Alignment: Interact and negotiate with senior management and external stakeholders to reconcile competing views and drive critical, high-impact business decisions.
• Automation and Efficiency: Lead efforts to automate model monitoring and governance processes to create scalable and auditable infrastructure.

What You'll Need:

• Experience: 8+ years of progressive experience in credit risk, modeling, and data science within a regulated financial institution (FinTech, Bank, or similar), with at least 3 years in a people management role, managing technical staff.
• Education: Master’s or Ph.D. degree in a quantitative field (Statistics, Computer Science, Engineering, Operations Research, etc.).
• Technical Acumen: Deep expertise in advanced statistical and machine learning modeling techniques (e.g., Gradient Boosting, Deep Learning, Causal Inference).
• Regulatory Knowledge: Detailed working knowledge of model risk management standards (e.g., SR 11-7) and the ability to operate within a highly regulated environment.
• Tools & Platforms: Expert-level proficiency in Python (PySpark, scikit-learn, TensorFlow/PyTorch) and SQL/data warehouse technologies (e.g., Snowflake, Hive). Familiarity with modern MLOps platforms and cloud computing (AWS).
• 3+ years of progressive people management experience is required, including the ability to recruit, mentor, and foster talent within a team of high-performing Staff and Senior Data Scientists.
• Communication: Ability to distill highly complex analytical concepts into clear, concise, and compelling narratives for non-technical leadership.

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

Key Responsibilities:

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.

Qualifications:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.

The annual base salary range for this position is $175,000 to $300,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

#LI-BL1

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

Key Responsibilities:

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.

Qualifications:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.

The annual base salary range for this position is $175,000 to $300,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

#LI-BL1

About the Role

We're hiring an Executive Business Partner to support several technical leaders out of our San Francisco office. You will help our team stay focused and organized, managing personal logistics and any tasks that might fall through the cracks. 

This is a non-traditional EA role, requiring creativity in adapting to different people’s work styles and the new challenges that emerge at a fast-moving startup. The role entails real autonomy in making decisions without tight supervision.

What You’ll Do

• Manage calendars, schedule meetings, and coordinate travel for 3-4 technical leaders
• Serve as the primary point of contact between your supported leaders and the rest of the company
• Support recruiting coordination efforts
• Track projects and commitments so nothing falls through the cracks

Skills and Qualifications

Minimum qualifications:

• 4+ years of executive or administrative support experience, ideally in AI labs or tech startups
• Track record of supporting technical leaders such as engineers or researchers

Preferred qualifications:

• Track record supporting a diverse team, adapting assistance and communication styles to individual needs
• Experience managing a satellite office and coordinating across time zones
• Experience adapting to a fast-changing role and meeting challenges proactively
• Proven professionalism and discretion with sensitive or personal information
• Having followed an executive to a new company

Logistics

• Location: This role is based in San Francisco, CA.
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $200,000 - $250,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Position Summary: 

Join our mission to infuse cutting-edge AI/ML/GenAI into pharmacy benefits as a Machine Learning Engineer.  We are looking for a software engineer with machine learning expertise to join us in expanding our AI capabilities, enabling increased productivity and magical experiences in our products and services. 

In this role you will be expected to design and implement complex AI systems that leverage ML models for NLP, NLG, multimodal data analysis, chatbots, and RAG-based QnA. The ideal candidate should be passionate about applying AI/ML concepts to difficult problems and develop scalable solutions. We want people who like working in a collaborative team environment and enjoy creating practical, efficient, and high-performance software that leverages Large Language Models (LLM), Multimodal Language Models(MLM), and other ML models and techniques to build amazing capabilities for our customers, partners, and employees. Most importantly, we are a mission-oriented, high-growth startup and we are looking for folks that are excited to be part of our journey to make lasting impact towards transforming healthcare. 

Responsibilities: 

• Develop and productionize machine learning (ML) solutions in the fields of Document understanding, Search and QnA, GenAI, Virtual Agents, etc. 

• Develop and maintain backend services using Python, focusing on AI-driven applications. 

• Design and implement APIs for seamless integration with AI models and services. 

• Develop tools for large-scale data processing and ETL and contribute to extracting insights from data to help guide ML systems development 

• Participate in code reviews, testing, and quality assurance processes. 

• Troubleshoot and resolve technical issues related to AI model, integration, deployment and backend services. 

• Develop algorithms to ensure the integrity and robustness of ML solutions by developing automated testing and validation processes. 

• Document and communicate development processes and implementation details with peers and supervisors 

• Ensure the security and compliance of healthcare data, adhering to HIPAA regulations. 

Required Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field. 

• 2+ years experience in the industry with a strong focus on ML solutions development and production deployment is a plus. 

• Good understanding of Machine Learning fundamentals such as Deep Neural Networks, Transformer-based LLM and MLMs, Boosted or standard decision trees, RAG, etc.  

• Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development. 

• Proficiency in Python and familiarity with ML libraries such as PyTorch. 

• Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma etc. Some experience in developing and fine-tuning prompts on any of the GenAI services is a plus. 

• Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking. 

• The ability to work collaboratively across multiple disciplines in an extremely fast-paced, startup environment. 

• Good written communication skills that enable collaboration in a remote environment. 

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a large amount of work ahead of us. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

At its core, Stripe is a treasury company, and the Treasury Finance team is key to building the Global Payments and Treasury Network (GPTN). We collaborate closely with product, engineering, sales, and finance teams to create innovative solutions that enhance Stripe’s financial capabilities. As part of Treasury Finance’s AI and Quantitative Analytics team, you’ll work at the intersection of finance, artificial intelligence, and product development, building autonomous agents and intelligent solutions that enhance treasury capabilities and enable us to better serve and scale with the global economy. 

What you’ll do

Within Treasury Finance AI and Quantitative Analytics, you’ll leverage your finance, artificial intelligence (AI) and technical expertise to  build intelligent solutions that enhance Stripe’s treasury capabilities. You’ll develop AI-powered tools and models, autonomous agents, and data products that automate complex workflows and provide actionable insights. Working at the intersection of artificial intelligence, finance, and product development, you'll collaborate with engineering and finance teams to deploy cutting-edge AI applications that scale with Stripe's growth and support our mission of increasing the GDP of the internet. 

You will:

• Apply your treasury and finance domain expertise to identify high-impact opportunities where AI can solve complex liquidity, financial risk management, and capital markets challenges
• Build AI-powered tools and artificial intelligence solutions to automate treasury workflows, enhance risk management, and scale our operations
• Design and deploy autonomous AI agents using frameworks like LangChain and LangGraph that can reason, plan, and execute complex multi-step problems and enhance decision-making processes
• Ship interactive data products and self-service analytics tools using Python frameworks (Streamlit, Dash, Gradio) that provide real-time insights and actionable intelligence
• Work across Treasury Finance and partner teams to scope AI-driven solutions that allow us to optimize and scale our most complex, data-driven financial workflows 
• Explore and prototype cutting-edge AI applications to address emerging treasury challenges

Who you are

We’re looking for a technical problem-solver with treasury and finance expertise who’s excited about leveraging artificial intelligence and who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• Bachelor’s degree in finance, mathematics, computer science, engineering or a related field with 5+ years of work experience in software development, data science, or treasury/finance roles 
• Working familiarity with financial risk and capital markets concepts
• Proficiency in Python and experience with AI/ML frameworks including PyTorch and TensorFlow
• Experience with Large Language Model (LLM) frameworks such as LangChain, LangGraph, or similar tools for building AI applications and agentic systems
• Solid understanding of AI fundamentals, including deep learning, natural language processing, and generative AI
• Results-oriented, with a focus on delivering impact in a fast-paced environment 
• Excellent communication skills for collaborating with product, engineering, and finance teams
• Highly organized, with attention to detail and the ability to manage tight deadlines

Nice to have

• • Familiarity with the payments industry and fintech landscape
  • Experience with Databricks and cloud platforms (AWS, GCP, Azure) 
  • Software engineering experience in Ruby

The role:

The Marketing Data Science (MDS) team is seeking an experienced Machine Learning professional to join the group. This team is essential for supporting SoFi's Marketing organization by providing data-driven decision-making through rigorous analytics, modeling, experimentation, and measurement.

The primary responsibility of this role is to build and scale machine learning models that drive marketing and growth across SoFi's product suite. The focus will be on developing predictive models for key areas: acquisition, conversion, retention, and customer lifetime value (LTV), utilizing behavioral, transactional, and credit data. This role requires strong collaboration across teams to identify the business needs, manage the end-to-end model development cycle, implement the model in production, and monitor performance, while maintaining strict compliance with all regulatory standards.

What you’ll do: 

• Design, develop, and deploy machine learning models to optimize customer acquisition, onboarding, and lifecycle engagement across financial products (e.g., loans, credit cards, money, invest, crypto and etc).
• Build predictive models for critical business outcomes, including Customer Lifetime Value (LTV), conversion propensity, cross-sell/upsell effectiveness, and retention across various channels (e.g., direct mail, email, in-app, Operations).
• Leverage structured and unstructured data (e.g., behavioral signals, transaction data, credit attributes) to drive audience segmentation and personalization at scale. Develop and maintain a robust feature store to accelerate the end-to-end model development lifecycle.
• Build experimentation frameworks to support A/B testing and measure the incremental business impact of the models. Work with the other team members to incorporate the models in marketing/campaign strategies.
• Collaborate with Fair Lending (FL) and Model Risk Management (MRM) to ensure the models and targeting strategies adhere to fair lending laws, privacy regulations, and responsible marketing practices.
• Support the efficient scoring and implementation of models through both real-time and batch processes, ensuring the timely availability of scores for marketing execution and deep analytics.
• Monitor model performance with a focus on business outcomes (conversion, retention, revenue, risk-adjusted returns) and continuously iterate based on insights.
• Partner closely with team members within MDS and key business stakeholders on defining business requirements, prioritizing the model pipeline, providing development updates, and monitoring performance.
• Stay at the forefront of modeling, data, and AI innovations to support scalable marketing growth in financial services.

What you’ll need:

• Bachelor's degree in Computer Science, Math, Physics, Engineering, or a quantitative field required; Master's degree preferred.
• 8+ years of experience in machine learning, data science or applied AI, ideally in marketing, fintech, or consumer tech.
• Strong proficiency in Python, SQL, and ML frameworks (scikit-learn, PyTorch, TensorFlow).
• Familiarity with ML governance, model monitoring, and compliance frameworks (MRM, Fair Lending, etc.) is a plus.
• Experience with LLMs, prompt engineering, and AI application development preferred.
• Demonstrated experience with experimentation and hypothesis testing.
• High intellectual curiosity and a quick learner of new technical skills.
• Ability to initiate and drive projects independently to completion, and ability to clearly communicate technical information to diverse audiences.
• Strong relationship-building and collaborative skills.
• Ability to thrive in a dynamic, cross-functional environment with keen attention to detail.

The Role:

As a Software Engineer on our Cloud Infrastructure team, you'll be at the forefront, architecting and building the foundational systems that power Fireworks AI's revolutionary generative AI platform. You'll spearhead the creation of one of the world's first virtual clouds, seamlessly serving AI workloads across the globe and every cloud provider. Your mission: to deliver unparalleled reliability, efficiency, and scalability, fueling the world's most innovative AI products.This is a highly technical role requiring deep expertise in distributed systems, cloud-native infrastructure, and machine learning platforms. You’ll partner closely with engineering partners, product teams, and infrastructure stakeholders to design solutions that balance performance, cost-efficiency, and operational simplicity across compute, storage, and networking layers.

Key Responsibilities:

• 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, 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.

Minimum qualifications:

• 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.

Preferred qualifications:

• Master’s or PhD in Computer Science or related field.
• Experience leading infrastructure projects supporting large-scale ML/AI workloads or high-throughput systems.
• Familiarity with infrastructure-as-code and CI/CD tooling (e.g., Terraform, ArgoCD, GitOps).
• Track record of driving system performance, reliability, and cost-efficiency improvements.
• Contributions to open-source cloud or ML infrastructure projects a plus.

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding, and ads optimization that shape the future of streaming.

We are seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging advanced ML engineering with world-class infrastructure design. In this role, you will own the strategic direction and execution for scaling our machine learning capabilities while ensuring our distributed systems and infrastructure can support innovation at massive scale. You will combine technical depth with leadership excellence to guide teams that deliver both foundational ML systems and high-performance distributed services.

What You'll Do: 

• Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth.
• Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization.
• Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
• Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence.
• Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences.
• Support best practices in experimentation, evaluation, and ML system monitoring.
• Ensure cost efficiency, scalability, and performance in ML infrastructure investments.

Your Background:

• 10+ years of industry experience spanning machine learning engineering and distributed systems.
• 3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams.
• MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience.
• Proven expertise in building and deploying end-to-end ML systems at scale, including recommendation and personalization systems.
• Strong background in distributed systems architecture, including low-latency services, streaming platforms, and large-scale serving.
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and ML infrastructure technologies.
• Track record of delivering high-quality, scalable, and fault-tolerant systems.
• Excellent communication skills and ability to influence product and technical strategy.
• Proven experience deploying large-scale serving systems on AWS and demonstrated expertise in leveraging Databricks for large-scale data processing and ML workflows

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences.  With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming.

We are seeking a highly skilled Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions.

What You'll Do:

• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences.
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Your Background:

• 8+ years of industry experience building production Machine Learning systems
• MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
• Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
• Proficiency in building and deploying full-stack machine learning pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Solid understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning.
• Ability to deep dive into individual components and systems, as well as understand the overall architecture of machine learning solutions.

#LI-Hybrid #LI-SC1

What We're Looking For

We are seeking a driven, goal-oriented, and quick-learning Sales Development Representative (SDR) to join our team. In this role, you'll be at the forefront of generating new business opportunities by engaging with prospective clients and building a robust sales pipeline. You'll work closely with the team to identify, qualify, and nurture leads, ensuring a seamless handoff to our account executives. This will be an outbound role with a heavy emphasis on prospecting.

This role is based out of our New York City office, with an in-office requirement of at least 4 days per week and occasional team and company offsites.

What You’ll Do

• Become an expert on our sales process with an in-depth understanding of our target markets, value proposition, and pricing models to not only meet but exceed both quarterly and annual goals.
• Focus on high-quality, consistent outbound prospecting across all market segments to build the sales pipeline and optimize the customer funnel.
• Qualify and develop outbound sales leads.
• Collaborate with the sales and marketing teams to build and optimize email campaigns with targeted messaging for specific user personas and improve the lead qualification process.
• Analyze industry trends and customer segmentation to identify and capitalize on new sources of qualified leads.
• Utilize a CRM system to track activity and manage sales opportunities.

What You’ll Need

• 1-3 years of experience in sales development, business development, or a similar role, preferably within the tech or AI industry.
• Proven track record of meeting or exceeding sales targets through outbound prospecting and lead generation efforts.
• Strong understanding of sales processes and CRM tools (experience with Salesforce or HubSpot is a plus).
• Excellent communication and interpersonal skills, with the ability to craft persuasive messaging and build rapport with prospective clients.
• Highly organized and detail-oriented, with the ability to manage multiple tasks and priorities in a fast-paced environment.
• Self-motivated and driven, with a passion for learning and continuous improvement.
• Team-oriented mindset, with the ability to collaborate effectively with cross-functional teams, including marketing, sales, and customer success.
• A goal-oriented attitude, with a strong desire to grow within a dynamic, high-growth company.

Compensation

The base salary range for this role is $60,000-$80,000, in addition to a variable component and meaningful equity. 

What We're Looking For

We are seeking a highly skilled Lead Research Engineer to work on optimizing training and inference workloads on compute accelerators and clusters, through  the Lightning Thunder compiler and the broader PyTorch Lightning ecosystem. This role sits at the intersection of deep learning research, compiler development, and large-scale system optimization. You’ll be shaping technology that pushes the boundaries of model performance and efficiency, creating foundational software that will impact the entire machine learning ecosystem.

This role is based in one of our hubs (NYC, SF, or London), with a minimum of 2 in-office days per week and occasional team and company offsites.

What You’ll Do

• Develop performance-oriented model optimizations at multiple levels:
• Graph-level (e.g., operator fusion, kernel scheduling, memory planning)
• Kernel-level (CUDA, Triton, custom operators for specialized hardware)
• System-level (distributed training across GPUs/TPUs, inference serving at scale)
• Advance the Thunder compiler by building optimization passes, graph transformations, and integration hooks to accelerate training and inference workloads.
• Work across the software stack to ensure optimizations are accessible to end users through clean APIs, automated tooling, and seamless integration with PyTorch Lightning.
  Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.
• Collaborate with hardware vendors and ecosystem partners to ensure Thunder runs efficiently across diverse backends (NVIDIA, AMD, TPU, specialized accelerators).
• Contribute to open-source projects by developing new features, improving documentation, and supporting community adoption.
• Engage with researchers and engineers in the community, providing guidance on performance tuning and advocating for Thunder as the go-to optimization layer in ML workflows.
• Work cross-functionally with Lightning’s product and engineering teams to ensure compiler and optimization improvements align with the broader product vision.

What You’ll Need

• Strong expertise with deep learning frameworks such as PyTorch
• Hands-on experience with model optimization techniques, including graph-level optimizations, quantization, pruning, mixed precision, or memory-efficient training.
• Knowledge of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling).
• Familiarity with software engineering practices: designing APIs, building robust tooling, testing, CI/CD for performance-sensitive systems.
• Excellent collaboration and communication skills, with the ability to partner across research, engineering, and external contributors.
• Bachelor’s degree in Computer Science, Engineering, or a related field

Nice-to-Haves

• Experience with CUDA, Triton, or other GPU programming models for developing custom kernels.
• Deep understanding of deep learning compiler internals (IR design, operator fusion, scheduling, optimization passes) or proven work in performance-critical software.
• Proven track record contributing to open-source projects in ML, HPC, or compiler domains.
• Advanced degree (Master’s or PhD) in machine learning, compilers, or systems highly preferred.

Compensation

Position Summary

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities.

Primary Responsibilities

• Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
• Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
• Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
• Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
• Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
• Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
• Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
• Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.

Qualifications

• Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 6+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
• Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with CI/CD pipelines, testing automation, and version control using Git.
• Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.
• Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
• Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

This is an exempt position. The annualized base pay range for this role is expected to be between $140,000-$180,000.  Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set.  The base pay is just one component of the total compensation package for employees.  Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.