Research Engineer

• Learn to approach complex challenges with innovative solutions
• Explore emerging technologies including AI, machine learning, and blockchain
• Understand how emerging technologies can enhance SaaS offerings
• Participate in concept ideation, prototyping, testing, and implementation of new features and products
• Conduct market research to identify customer needs, market dynamics, and innovation opportunities
• Help ensure solutions remain competitive and meet evolving demands
• Contribute to SaaS offerings and technology solutions serving banking, retail, travel, telecoms, healthcare, and other industries
• Collaborate in person with teams and participate in the graduate program experience

Graduate R&D Engineer innovating AI-powered SaaS chat commerce at Clickatell

Graduate Research and Development Engineer – Innovation

Graduate R&D Engineer exploring AI, machine learning, and blockchain for Clickatell’s SaaS chat commerce platform. Prototyping products and researching market opportunities in Cape Town.

• Conduct applied research and technical evaluations related to emerging contaminants, microbial water quality, and advanced treatment technologies
• Partner with utilities to pilot, validate, and implement science-based solutions
• Support drinking water and wastewater treatment projects through process evaluations, optimization studies, PFAS investigations and treatment solutions, pilot testing, regulatory compliance assessments, and implementation planning
• Support planning, design, and delivery of municipal water and wastewater infrastructure projects
• Collaborate with multidisciplinary project teams and support project managers in developing scopes, schedules, budgets, and project deliverables
• Participate in client meetings, workshops, business development efforts, and proposal preparation
• Travel periodically for project site visits, conferences, and client engagements
• Contribute to innovative solutions for municipal and utility clients

Applied Research Water Engineer advancing sustainable water solutions for municipal utilities at Arcadis

Applied Research Engineer – Water

Applied Research Water Engineer advancing sustainable water treatment, reuse, and resilience solutions at Arcadis. Supporting research, infrastructure projects, and utility implementation.

• Build, train, and maintain machine learning models for expression-perturbation prediction and downstream functional-endpoint prediction
• Acquire, harmonize, and manage large-scale biological datasets including LINCS L1000, GDSC, Tahoe-100M, and DILImap
• Perform schema harmonization, normalization, and de-duplication across cell, drug, and assay identifiers
• Manage model training pipelines over pooled datasets
• Automate and harden the end-to-end evaluation pipeline
• Implement robust statistical baselines, including cell- and drug-conditioned mean baselines
• Translate ideas from scientific literature, including transformer-based perturbation models and knowledge graph/GNN-based embeddings, into tested code
• Partner with computational biologists, software engineers, and product stakeholders
• Document methods, assumptions, and results
• Communicate findings to technical and non-technical stakeholders

ML Research Engineer building virtual-cell models for SandboxAQ’s drug-discovery AI platform

Senior ML Research Engineer, Virtual Cell

ML Research Engineer building virtual-cell prediction models for SandboxAQ’s drug-discovery AI platform. Managing biological datasets, training pipelines, and rigorous model evaluation.

• Turn real customer requirements into reliable training and deployment workflows
• Work end-to-end across model adaptation and post-training, evaluation, data, and infrastructure
• Build and improve post-training and evaluation workflows for CPT, SFT, RL, and distillation
• Develop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment
• Debug and harden large-scale ML systems, including distributed training, scheduling/execution, checkpointing, observability, and reproducibility
• Improve the Forge codebase through clear APIs, tests, documentation, and maintainable abstractions
• Advance the RL training stack, including high-throughput asynchronous rollout and scalable post-training systems
• Ensure Forge deployment is seamless and adaptable across diverse clients, hardware, software stacks, cloud, and on-premises environments
• Partner with researchers and infrastructure engineers to translate bottlenecks into concrete system improvements
• Collaborate with scientists, engineers, product, and customer-facing teams to ship maintainable and trusted Forge projects

Research Engineer building Forge AI training and deployment workflows at Mistral

Research Engineer, Forge

Research Engineer building Forge workflows for Mistral’s full-stack AI solutions. Improving LLM post-training, evaluation, data pipelines, and scalable deployment systems.

• Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference
• Build systems for queueing, admission control, quotas, priorities, preemption, and topology-aware placement of GPU workloads
• Improve provisioning, allocation, and utilization of heterogeneous GPU resources across clusters
• Place workloads across multiple clusters based on capacity, data locality, hardware requirements, and organizational priorities
• Create self-service workflows for launching, observing, debugging, and reproducing distributed workloads
• Improve GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency
• Develop observability, failure recovery, capacity planning, and operational tooling for critical ML workloads
• Participate in on-call rotations and troubleshoot applications, schedulers, networking, storage, and GPU infrastructure

Research Engineer building Mistral AI’s distributed GPU ML platform

Research Engineer, ML Platform

Research Engineer building Mistral AI’s distributed GPU platform for large-scale training, evaluation, and inference. Improving Kubernetes orchestration, reliability, and researcher self-service workflows.

• Translate customer planning problems involving capacities, costs, lead times, and shift plans into actionable optimization models
• Prepare and validate ERP and Excel export data, identifying incorrect numbers before customers do
• Define modeling abstractions and modeling vocabulary for engineers and customers
• Build and maintain the interface between software and mathematics
• Make optimization answers usable and explain infeasibility, conflicting rules, and the cost of relaxing constraints
• Scale models across sites, periods, constraints, and concurrent customers
• Own features from first line of code through production
• Build, ship, and operate work end to end
• Review teammates' work and collaborate with the math and OR team
• Communicate proactively and raise issues early

Applied Operations Research Engineer building optimization infrastructure for industrial companies

Applied Operations Research Engineer

Applied Operations Research Engineer translating industrial planning problems into production optimization models. Building scalable decision infrastructure for factories using combinatorial optimization.

• Design, build, deploy, and manage secure and scalable generative AI solutions using SaaS and local resources
• Guide internal technical users with best practices and support for SaaS AI services and custom applications
• Design and assist with secure data connectors and ingestion pipelines for enterprise AI access to internal data sources
• Collaborate with Information Security and IT teams to define requirements, implement IAM, network, encryption, logging, and monitoring controls, conduct security reviews, and ensure compliance
• Establish monitoring and alerting and develop operational procedures for deployed AI services
• Optimize AI solutions for performance, scalability, and cost-effectiveness
• Liaise between business stakeholders, technical teams, IT operations, and information security
• Track developments in SaaS AI/ML services, generative AI, and cloud security
• Create and maintain technical documentation, architecture diagrams, and security guidelines
• Contribute to the organization’s AI/ML strategic roadmap
• Develop and present educational materials on effective and safe AI usage

Innovation Engineer building secure generative AI capabilities for GuidePoint Security, a cybersecurity services provider

Innovation Engineer

Innovation Engineer building secure AWS generative AI capabilities for GuidePoint Security, a cybersecurity services provider. Designing AI integrations, security controls, and operational processes for internal teams.

• Own end-to-end delivery of significant search and retrieval projects, including outcome, quality, timeline, and live-system health
• Act as technical lead for a squad of 3–5 engineers by setting direction, breaking down work, reviewing designs and code, and unblocking the team
• Partner with applied scientists and feed production evidence back into research
• Run the exploration → POC → proof of value → productionization loop and determine what to pursue
• Design and build retrieval architectures, ingestion and indexing pipelines, and ranking and re-ranking systems on OpenSearch and Vespa
• Build retrieval infrastructure and search agents for agentic AI workflows, including retrieval APIs, agentic query planning, multi-step retrieval, RAG pipelines, hybrid and semantic retrieval, and query understanding
• Build offline relevance harnesses, golden and labeled sets, online A/B tests, and end-to-end agent quality measurement
• Diagnose retrieval and agent quality failures across the pipeline
• Build and operate production APIs and backend services on AWS
• Identify and communicate timeline and architecture risks
• Influence architecture decisions through design reviews, partner-team alignment, and mentorship

Lead search and retrieval research engineering for Thomson Reuters’ legal, tax, and regulatory intelligence products

Lead Research Engineer, Search & Retrieval

Lead research engineer building production search, retrieval, and agentic AI infrastructure for Thomson Reuters’ legal, tax, and regulatory content.
Leading engineers, experiments, evaluation, and scalable AWS services.

• Collaborate on machine learning projects that enhance detection, prevention, and response to cheating
• Execute experiments and analysis to validate models and measure their impact on players
• Communicate results, impediments, and work impact to the team and stakeholders
• Work with game designers, engineers, and producers to identify anti-cheat opportunities
• Develop new detections from data and improve existing detections
• Support Ricochet through consultation, data analysis, data engineering, and experimentation
• Stay current on machine learning, anti-cheat technology, and statistical analysis advances
• Learn from experts and participate in mentorship opportunities across the team

Machine learning engineer developing anti-cheat systems for Activision’s Call of Duty games

Machine Learning Research Engineer – Central Technology

Machine learning engineer developing anti-cheat detection for Activision’s Call of Duty games. Applying Python, SQL, and production ML models to protect players and improve Ricochet.

• Design, implement and operate high impact machine learning models
• Partner with researchers, engineers, data scientists, and product managers to identify high-value machine-learning applications
• Translate business intuition into data-driven solutions
• Create scalable, production-ready ML solutions
• Take algorithms from initial concept through deployment in Netflix’s large-scale, real-time systems
• Contribute to better infrastructure for developing and deploying ML models
• Advocate for and apply best practices for availability, scalability, operational excellence, and cost management
• Operate and innovate on growth and commerce algorithms in production
• Validate solutions through offline experiments
• Build online A/B tests for production systems

Research Engineer building machine-learning systems for Netflix’s global streaming business

Research Engineer 4/5 – Member Lifecycle and Monetization

Research Engineer developing and deploying machine-learning models for Netflix’s global streaming business.
Improving member lifecycle and monetization through experimentation, algorithms, and scalable real-time systems.

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