About The Forecasting Company

We are on a mission to create the forecasting foundation model to rule them all. Forecasting drives critical decisions worldwide - impacting staffing, supply chain management, finance and more. Our solution provides companies with the models, platform and APIs they need to easily generate the most accurate forecasts possible, helping to significantly reduce waste and enabling smarter, more confident decisions.

Who we’re looking for

The forecasting model is at the heart of our technology. As a founding MLE, you will build, train and deploy large foundation model architectures: implement and combine ideas from the literature, push the state of the art, and ultimately deploy your model for our customers to use in production. Our goal is for our models to be the best for our customers’ use cases - including for capabilities that do not exist yet in academic models.

You love your craft, have high standards, stay up-to-date with the latest ideas in ML, and know when to make trade-offs to ship. You live and breathe neural networks, and speak PyTorch or Jax. You are used to diving deep in large amounts of data, and you know what you train your models on. Bonus if you have experience building solid ML infrastructure.

You are passionate about your craft, maintain high standards, stay current with the latest tech and know when to make trade-offs to deliver results efficiently. We do not believe great engineers are “jack of all trades”, but rather that they excel at diving deep into complex topics quickly, leveraging a broad range of experiences to solve challenging problems. You are also open to exploring new concepts, technologies, and enjoy quickly throwing prototypes together to kick the tires. You prefer quick feedback loops, rather than aiming for perfection on the first try.

What you’ll be doing

Architect and train time-series foundation models using diverse datasets, integrating multimodal inputs like numerical time series, text, location and image data

Design reproducible experiments to verify, compare and combine ideas from the literature.

Contribute to building our data exploration tools to understand (lagged) correlations between different data sources, data sparsity, weather patterns, consumer trends…

Add data sources you find interesting to our train or test datasets.

Deploy models for use in our API and platform - getting into the gritty details if exporting to ONNX requires some custom operation or torch.compile fails

Gather and act on user feedback, iterating on model capabilities to maximize customer satisfaction and impact.

Mentor and guide future team members, helping shape a high-performing and diverse science and engineering culture as the team grows.

Full description : https://app.dover.com/apply/theforecastingcompany/9a6d0f8e-879b-496b-9b7b-c39d6afbd7de

ABOUT THE ROLE

You would be working as part of our Applied Research team, focused on turning pre-trained LLMs into well-aligned and highly capable AI systems for coding and software development. This is a hands-on role where you'll work across a variety of efforts, including: Building data pipelines for coding use cases, researching and implementing fine-tuning algorithms, training reward models, and more. You will have access to thousands of GPUs in this team.

YOUR MISSION

To turn pre-trained LLMs into well-aligned and highly capable AI systems.

RESPONSIBILITIES

• Research and experiment on ways to specialize foundational models to coding use cases
• Build and maintain data and training pipelines
• Keep up with latest research, and be familiar with state of the art in LLMs, alignment, synthetic data generation, code generation
• Design, analyze, and iterate on training/fine-tuning/data generation experiments
• Write high-quality, pragmatic code
• Work as part of a team: plan future steps, discuss, and communicate clearly with your peers

SKILLS & EXPERIENCE

• Experience with Large Language Models (LLM)
• Deep knowledge of Transformers
• Strong deep learning fundamentals
• Good taste in data
• Fine-tuning experience with LLMs
• Extensively used and probed LLMs, familiarity of their capabilities and limitations
• Knowledge of distributed training
• Strong machine learning and engineering background
• Research experience
• Experience in proposing and evaluating novel research ideas
• Familiar with, or contributed to the state of the art in multiple of the following topics: Fine-tuning and alignment of LLMs, synthetic data generation, continual learning, RLHF, code generation
• Is comfortable in a rapidly iterating environment
• Is reasonably opinionated
• Programming experience: Linux, Strong algorithmic skills, Python with PyTorch or Jax. Use modern tools and are always looking to improve
• Strong critical thinking and ability to question code quality policies when applicable
• Prior experience in non-ML programming, especially not in Python - is a nice to have

PROCESS

• Intro call with one of our Founding Engineers
• Technical Interview(s) with one of our Founding Engineers
• Team fit call with the People team
• Final interview with one of our Founding Engineers

BENEFITS

• Fully remote work & flexible hours
• 37 days/year of vacation & holidays
• Health insurance allowance for you and dependents
• Company-provided equipment
• Wellbeing, always-be-learning and home office allowances
• Frequent team get togethers
• Great diverse & inclusive people-first culture

The role
Nebius is hiring a driven and industry-savvy Lifesciences Solutions Partner - US to join our growing Healthcare & Life Sciences (HCLS) team. As a strategic connector between the Global Head of HCLS and regional Account Executives (AEs), you will play a pivotal role in accelerating go-to-market execution, deepening client engagement, and ensuring our cloud and AI solutions align with the business, scientific, and regulatory needs of the life sciences ecosystem. You will manage strategic client relationships, identify and develop new business opportunities, and collaborate with partners - with a strong focus on the Pharmaceutical, Biotechnology, Drug Development, and Genomics segments. Your ability to understand complex scientific and business challenges, craft tailored solutions, and thrive in a fast-moving, innovation-led environment will define your success. This role combines consultative selling, industry expertise, and commercial execution, helping customers unlock the full potential of the Nebius platform.

You’re welcome to work remotely from United States.

Your responsibilities will include:
- Demonstrate a deep understanding of Nebius and the value to our customers. - Own and grow your territory: Maintain and deliver against a strategic plan for region/territory. Help AE’s qualify and prioritise opportunities through an HCLS and compliance lens. Lead and - support strategic discussions with pharma and biotech. - Client Engagement: Develop deep relationships with key stakeholders across the enterprise, positioning our AI and cloud solutions to address client-specific challenges. Act as a trusted advisor to pharma and biotech clients, driving engagement and long-term relationships. Identify opportunities to apply AI/ML, HPC, and data platforms in drug discovery and clinical operations. - Deal Support & Sales Acceleration: Partner with Account Executives to shape account strategy, value messaging, and proposal content that will secure deals to meet revenue targets. Help qualify and prioritise opportunities through an HC&LS and compliance lens. Support complex deal cycles where domain credibility and regulatory insight are critical. - Solution Selling: Demonstrate the value of AI and cloud solutions through consultative selling, product demonstrations, and presentations. - Regional Representation: Represent Nebius AI at regional and industry events, and customer meetings. - Market Knowledge: Stay updated on industry trends, emerging technologies, and competitive landscape to position our solutions effectively. - Forecast with accuracy; progress deals through the Salesforce sales process and deliver against ACV / activity targets.

We expect you to have:
- Proven Experience: 8+ years of experience in B2B sales, particularly in AI, cloud, or data infrastructure, with a clear hunter track record. - Passion and desire to work in a startup culture, directly impacting the growth of the company -Comfortable selling cloud platforms (AWS, Azure, Google Cloud), AI solutions, and related technologies. - Strong commercial acumen: value mapping, negotiation, multi‑year deals, and exec-level‑ storytelling. - High energy, enthusiasm, and evidence of consistent growth vs. quota. - CRM Proficiency: Experience with CRM tools such as Salesforce, HubSpot, or similar.

Ability to travel as needed. - It will be an added bonus if you have:
- 5 - 10 years in pharma, biotech, or life sciences, ideally in consulting, GTM, product, or pre-sales roles. - Deep understanding of drug discovery and development processes, scientific data workflows, and regulatory frameworks. - Proven ability to communicate complex scientific and technical concepts to non-technical stakeholders. - Previous experience in a high-growth, start-up environment ideally selling cloud, AI/ML or HPC solutions. - Exposure to SaaS models or cloud infrastructure sales. - Experience selling to mid-market or enterprise-level clients

Preference for on-site candidates in San Mateo, CA but remote possible.

BigHat is hiring ML interns for summer 2026! We've got an awesome high-throughput wetlab that pumps proprietary data into custom data and ML Ops infra to power our weekly design-build-train loop. Come solve hard-enough-to-be-fun problems in protein engineering in service of helping patients!

Location USA, WA, Seattle

Description As part of the AWS Applied AI Solutions organization, we're advancing the frontier of visual reasoning and agentic AI technologies. Our vision is to develop sophisticated AI systems that can understand, interpret, and reason about visual information at human-like levels, enabling breakthrough applications across multiple industries.

As a Principal Research Scientist, you'll drive research in visual reasoning and generative AI, defining novel research directions while ensuring impact through practical applications. Your day-to-day work will involve developing theoretical frameworks, conducting deep technical investigations, and validating complex hypotheses through rigorous experimentation. You'll mentor junior scientists in research methodology and experimental design while collaborating with cross-functional teams to translate research insights into scalable solutions.

Your expertise and guidance will be instrumental in shaping both the technical direction of our visual AI initiatives and the development of junior research talent on the team.

Amsterdam

As a Quantitative Research Intern, you will get to work with our research team of mathematicians, scientists and technologists, to help develop the models that drive Optiver’s trading. You will tackle a practical research challenge that has impact and directly influences Optiver’s trading decisions. In our business, where the markets are always evolving, you will use your skills to predict its movements.

What you’ll do Led by our in-house education team, you will delve into trading fundamentals and engage in research projects that make a real difference. You will enhance your understanding of trading principles and gain hands-on experience by trading on live markets using real Optiver technology, with simulated capital. For the ten-week internship, you will get support from experienced researchers during your research project work, providing you exposure to a variety of areas, including: • Deep dive into trading and research fundamentals, from theoretical concepts to financial markets, strategies and cutting-edge technology • Using statistical models and machine learning to develop trading algorithms • Leveraging big data technologies to analyse trading strategies and financial instruments to identify trading opportunities • Combining quantitative analysis and high-performance implementation to ensure efficiency and accuracy of your models • Gain exposure to various trading and research desks and experience the financial markets first-hand Based on your performance during the internship, you could receive an offer to join our firm full-time after your studies.

What you’ll get You’ll join a culture of collaboration and excellence, where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talented colleagues, working collectively to tackle the toughest problems in the financial markets. In addition, you’ll receive: • A highly competitive internship compensation package • Optiver-covered flights and accommodation in the city centre for the duration of the internship • Extensive office perks, including breakfast and lunch, world-class barista coffee and Friday afternoon drinks • The opportunity to participate in sports and leisure activities, along with social events exclusively organised for your intern cohort

Who you are • Penultimate year student in Mathematics, Statistics, Computer Science, Physics or a related STEM field, with the ability to work full time upon graduation in 2027 • Solid foundation in mathematics, probability and statistics • Excellent research, analytical and modelling skills • Independent research experience • Proficiency in any programming language • Knowledge of machine learning, time-series analysis and pattern recognition is a plus • Strong interest in working in a fast-paced, collaborative environment • Fluent in English with strong written and verbal communication skills

Diversity statement Optiver is committed to diversity and inclusion. We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities. Please let us know if you would like to request any reasonable adjustments by contacting the Recruitment team via the contact form, selecting “Reasonable Adjustments” as the subject of your inquiry.

For answers to some of our most frequently asked questions, refer to our Campus FAQs.

For applicants based in India, our entry route is via the placement office internship hiring season (July/August).

*We accept one application per role per year. If you have previously applied to this position during this season and have been unsuccessful, you can reapply once the next recruitment season begins in 2026.

Bala Cynwyd (Philadelphia Area), Pennsylvania United States & New York, New York United States

Overview Susquehanna is expanding the Machine Learning group and seeking exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis, natural language understanding, and more. Your work will directly influence our trading strategies and decision-making processes.

This is a unique opportunity to work at the intersection of cutting-edge research and real-world impact, leveraging one of the highest-quality financial datasets in the industry.

What You’ll Do

 • Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and scalable deployment

 • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches

 • Design and run experiments using the latest ML tools and frameworks

 • Develop automation tools to streamline research and system development

 • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior

 • Partner with engineering teams to implement and test models in production environments

What we're looking for We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning to produce resilient and high-performing models.

 • PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field

 • Strong track record of applying ML in academic or industry settings, with 5+ years of experience building impactful deep learning systems

 • A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR

 • Strong programming skills in Python and/or C++

 • Practical knowledge of ML libraries and frameworks, such as PyTorch or TensorFlow, especially in production environments

 • Hands-on experience applying deep learning on time series data

 • Strong foundation in mathematics, statistics, and algorithm design

 • Excellent problem-solving skills with a creative, research-driven mindset

 • Demonstrated ability to work collaboratively in team-oriented environments

 • A passion for solving complex problems and a drive to innovate in a fast-paced, competitive environment

Why Join Us?

 • Collaborate with a world-class team of researchers, engineers, and traders

 • Gain access to best-in-class financial data and high-performance computing resources

 • Directly impact real-time trading performance through your work

 • Thrive in a collaborative, intellectually rigorous environment with a global footprint

The annual base pay for this role is $300,000. Susquehanna considers factors such as scope and responsibilities of the position, work experience, education/training, key skills, as well as market and organizational considerations when extending an offer.

Visa sponsorship is available for this position.

New York / Chicago

Flow Traders is committed to leveraging the most recent advances in machine learning, computer science, and AI to generate value in the financial markets. We are looking for Quantitative Researchers to join this challenge.

As a Quantitative Researcher at Flow Traders, you are an expert in mathematics and statistics. You are passionate about translating challenging problems into equations and models, and have the ability to optimize them using cutting-edge computational techniques. You collaborate with a global team of researchers and engineers to design, build, and optimize our next generation of models and trading strategies.

Are you at the top of your quantitative, modeling, and coding game, and excited by the prospect of demonstrating these skills in competitive live markets? Then this opportunity is for you.

USA – Austin, San Jose

Overview

Arm technology is becoming the platform of choice for compute and AI. The Arm System Engineering team’s mission is to architect, design, and develop scalable compute platforms. Our capabilities span hardware and software, interconnects, system management, storage, physical infrastructure, and performance engineering. We lead customer collaborations, technology evaluation, end-to-end architecture, network strategy, and rigorous performance analysis. The team is developing advanced technologies to deliver innovative, high-performance solutions to power high-performance AI/ML applications. Job Overview:

The Performance Engineering Team plays a central role in enabling and optimizing performance across Arm’s compute systems. The team’s charter is to model, measure, and optimize performance at scale, ensuring Arm-based solutions achieve world-class efficiency and throughput for diverse workloads—from AI training and inference to scientific and data-intensive computing Responsibilities:

The System Engineering team is looking for AI/ML, Software, and Performance Engineers that will be responsible for application performance and benchmarking to enable ARM’s customer base in the datacenter space. Ownership of system performance for AI workloads and related software tools Collaboration with platform, interconnect, software, storage and system management engineering teams to perform system-scale testing and validation Oversight of support functions such as programs management, quality, and DevOps. Core Hiring Focus:

We are expanding our Performance Engineering team and seeking individuals passionate about pushing the limits of AI performance on systems built around Arm platforms. Key areas of recruitment include: Performance Modeling Engineers: Develop analytical and simulation-based performance models for large-scale AI/ML systems. AI and ML Performance Engineers: Optimize LLM and generative model training/inference workloads on Arm-based systems. Systems and Networking Engineers: Advance interconnect and collective communication performance across multi-node clusters, reduce system jitter. Storage Performance Engineers: Design and optimize parallel file system and near computer storage solutions for AI/ML workloads. Resilience and Reliability Engineers: Innovate in checkpointing, recovery, and resilient distributed training. Benchmarking Specialists: Lead evaluation and comparison of Arm-based performance using industry-standard metrics. In Return:

Be part of a groundbreaking team influencing the next generation of Arm systems for AI/ML computing! Collaborate with top engineers and vendors to develop industry-leading AI systems. Access professional growth through sophisticated project involvement and multidisciplinary teamwork. Join a company committed to diversity and inclusion, where your work matters and drives global progress!

We are Bagel Labs - a distributed machine learning research lab working toward open-source superintelligence.

Role Overview

You will design and optimize distributed diffusion model training and serving systems.
Your mission is to build scalable, fault-tolerant infrastructure that serves open-source diffusion models across multiple nodes and regions with efficient adaptation support.

Key Responsibilities

• Design and implement distributed diffusion inference systems for image, video, and multimodal generation.
• Architect high-availability clusters with failover, load balancing, and dynamic batching for variable resolutions.
• Build monitoring and observability systems for denoising steps, memory usage, generation latency, and CLIP score tracking.
• Integrate with open-source frameworks such as Diffusers, ComfyUI, and Invoke AI.
• Implement and optimize rectified flow, consistency distillation, and progressive distillation.
• Design distributed systems for ControlNet, IP-Adapter, and multimodal conditioning at scale.
• Build infrastructure for LoRA/LyCORIS adaptation with hot-swapping and memory-efficient merging.
• Optimize VAE decoding pipelines and implement tiled/windowed generation for ultra-high-resolution outputs.
• Document architectural decisions, review code, and publish technical deep-dives on blog.bagel.com.

Who You Might Be

You understand distributed systems and diffusion architectures deeply.
You’re excited about the evolution from DDPM to flow matching to consistency models, and you enjoy building infrastructure that handles complex, variable compute workloads.

Required Skills

1. 5+ years in distributed systems or production ML serving.
2. Hands-on experience with Diffusers, ComfyUI, or similar frameworks in production.
3. Deep understanding of diffusion architectures (U-Net, DiT, rectified flows, consistency models).
4. Experience with distributed GPU orchestration for high-memory workloads.
5. Proven record of optimizing generation latency (CFG, DDIM/DPM solvers, distillation).
6. Familiarity with attention optimization (Flash Attention, xFormers, memory-efficient attention).
7. Strong grasp of adaptation techniques (LoRA, LyCORIS, textual inversion, DreamBooth).
8. Skilled in variable-resolution generation and dynamic batching strategies.

Bonus Skills

• Contributions to open-source diffusion frameworks or research.
• Experience with video diffusion models and temporal consistency optimization.
• Knowledge of quantization techniques (INT8, mixed precision) for diffusion models.
• Experience with SDXL, Stable Cascade, Würstchen, or latent consistency models.
• Distributed training using EDM, v-prediction, or zero-terminal SNR.
• Familiarity with CLIP guidance, perceptual loss, and aesthetic scoring.
• Experience with real-time diffusion inference (consistency or adversarial distillation).
• Published work or talks on diffusion inference optimization.

What We Offer

• Top-of-market compensation
• A deeply technical culture where bold ideas are built, not just discussed
• Remote flexibility within North American time zones
• Ownership of work shaping decentralized AI
• Paid travel to leading ML conferences worldwide

Apply now - help us build the infrastructure for open-source superintelligence.