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Machine Learning Engineer - Contract (12 Months)

Location: Remote (India)
Contract Length: 12 months

We are looking for an experienced Machine Learning Engineer to join a global data and analytics programme, supporting the development of predictive automation solutions using large-scale commercial datasets. This role focuses on building, deploying, and optimising machine learning models on an enterprise data platform, with a strong emphasis on PySpark and distributed data processing.

This is a hands-on engineering role suited to someone who enjoys working with complex, multi-source data and translating advanced analytics into real-world commercial impact.

What you'll be doing:

• Designing and implementing machine learning and deep learning models using PySpark on an enterprise data platform.

• Solving complex analytical problems using large, distributed commercial datasets.

• Building and maintaining end-to-end ML pipelines, from data ingestion through to deployment and monitoring.

• Collaborating closely with commercial, analytics, and data engineering stakeholders to align technical solutions with business goals.

• Identifying data drift, bias, and performance issues, and ensuring models generalise effectively in production.

• Delivering actionable outputs such as segmentation, targeting, and optimisation recommendations.

• Keeping up to date with advances in machine learning, big data, and automation technologies.

Key responsibilities:

• Ingesting, cleaning, and transforming large-scale datasets using PySpark.

• Developing, evaluating, documenting, and monitoring ML models in a production environment.

• Designing and optimising ETL and data pipelines to support scalable ML operations.

• Working with stakeholders to define requirements and translate business objectives into technical solutions.

• Customising or extending ML libraries (e.g. PySpark MLlib) to maximise analytical value.

Skills & experience required:

• Strong experience working with large-scale, distributed data sets.

• Advanced knowledge of statistics, probability, algorithms, and machine learning concepts.

• Hands-on experience with PySpark and big data processing.

• Strong Python development skills.

• Experience with enterprise data platforms and data modelling.

• Excellent communication and collaboration skills.

• Proactive mindset with a willingness to learn new tools and technologies.

Desirable experience:

• Background in machine learning within pharmaceutical, life sciences, or commercial analytics environments.

• Practical experience deploying ML solutions on enterprise data platforms.

• Exposure to additional ML frameworks such as PyTorch or Keras (nice to have).

• Bachelor's or higher degree in computer science, mathematics, engineering, or a related quantitative field.

• Leading a team of data engineers while staying hands-on with design and coding
• Owning the technical direction and architecture of data pipelines and platforms
• Building and maintaining production-grade data workflows using Python, SQL, Snowflake, and DBT
• Designing cloud-based data solutions on AWS using serverless and event-driven patterns
• Setting engineering standards around code quality, testing, CI/CD, and documentation
• Ensuring data platforms are reliable, observable, and cost-efficient in production
• Working closely with business and technical stakeholders to turn requirements into working solutions
• Mentoring engineers and raising the overall engineering bar

• Strong experience in Python and SQL, with a software engineering mindset
• Proven hands-on experience with Snowflake and modern data modelling practices
• Solid background in data engineering (ETL/ELT, orchestration, data quality)
• Practical experience building data systems on AWS
• Experience defining architecture and making technical trade-offs
• Confidence leading technical initiatives and supporting other engineers
• Strong communication skills and comfort working with non-technical stakeholders

• Experience with workflow orchestration tools (e.g. Airflow)
• Exposure to APIs, streaming data, or real-time processing
• Familiarity with regulated or compliance-heavy environments
• Experience improving platform reliability, performance, or cloud costs

• High-impact position with real ownership and influence
• A balance of leadership and hands-on technical work
• Opportunity to shape long-term data architecture and standards
• Collaborative environment with complex, real-world data challenges

Machine Learning Scientist - Recommendations

Fully Remote contract (UK / Europe), Outside IR35, 6 months initially

We're partnering with a consumer-facing technology company operating at significant scale, supporting millions of users across multiple products. They are looking for a Machine Learning Scientist to join a high-impact People Recommendations team, working at the intersection of applied research and real-world product delivery. This role suits someone who enjoys taking ideas from research to production, owning the full model lifecycle and pushing the boundaries of modern recommendation systems in a two-sided marketplace.

As a Machine Learning Scientist, you'll design and evolve recommendation algorithms that directly shape how users connect and interact. You'll work closely with Data Scientists, Machine Learning Engineers, and Platform Engineers, acting as the bridge between research intuition and production reality. You'll have real ownership, autonomy, and influence, from early-stage experimentation through to models running in production at scale.

What You'll Be Doing

Research & Prototype (0 → 1)

• Explore novel approaches to bilateral matching, user representation learning, and preference modelling
• Prototype new architectures tailored to two-sided marketplaces
• Stay close to state-of-the-art research and translate relevant advances from paper to prototype

Build & Ship (1 → N)

• Evolve and improve production recommendation models, including Transformer and Graph Neural Network (GNN) based approaches
• Design robust evaluation frameworks that reflect real marketplace success
• Run large-scale experiments to test hypotheses and iterate quickly

Collaborate & Influence

• Partner with Product to define what's technically feasible and what trade-offs matter
• Work closely with ML Engineers to ensure models are reliable, scalable, and production-ready
• Mentor others, contribute to technical direction, and influence best practice across the team

What We're Looking For

Essential:

• MSc or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field
• Strong experience designing, training, and shipping modern deep learning models
• Production-level Python skills and experience with frameworks such as PyTorch
• End-to-end ML lifecycle experience: feature engineering, training, evaluation, deployment, and monitoring
• Comfortable operating in ambiguous problem spaces and structuring solutions from scratch
• Strong communication skills and ability to work with both technical and non-technical stakeholders
• A thoughtful approach to AI fairness, accountability, and transparency

Nice to Have:

• Experience with recommendation systems, ranking, or search, especially in two-sided marketplaces
• Exposure to modern LLM deployment frameworks (e.g. HuggingFace TGI, vLLM, TensorRT-LLM)
• Production experience on GCP (Vertex AI, BigQuery, GKE)
• Understanding of GPU-powered workloads, container runtimes, and NVIDIA tooling