Machine Learning Engineer
Posted Today by Inclined Inc
Negotiable
Undetermined
Remote
Remote
Summary: The Senior ML Engineer role is focused on bridging software engineering and practical AI, requiring a strong background in Python and machine learning. Candidates must be adept at writing production-ready code and making informed decisions on ML approaches. The position emphasizes hands-on experience with model evaluation and tuning. This is a long-term remote position based in the US, targeting professionals with over 10 years of experience.
Key Responsibilities:
- Write clean, production-ready Python code independently.
- Act as a bridge between software engineering and practical AI.
- Develop complete backend modules end-to-end.
- Make informed decisions on using heuristics, classical ML, or LLM-based approaches.
- Evaluate models in real-world settings and justify trade-offs.
- Apply or tune models across the ML spectrum, including tree-based algorithms, stats, NLP, and vectors.
Key Skills:
- 10+ years of experience in software engineering and machine learning.
- Strong proficiency in Python.
- Experience with SQL, No-SQL, Vector DB, and Azure.
- Hands-on experience with model evaluation and tuning.
- Understanding of ML spectrum and algorithms.
- Ability to justify trade-offs in ML approaches.
Salary (Rate): £60
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: Senior
Industry: IT
Detailed Description From Employer:
Role : Senior ML Engineer
Location : US Remote
Duration : Long Term
Experience : 10+ Years
- A heavy-hitting Python Engineer who writes clean, production-ready code without needing an LLM or a manager to hold their hand. This person will act as the bridge between hardcore software engineering (OOP, CI/CD, observability) and practical AI. They should be comfortable writing complete backend modules end-to-end. SQL, No-SQL, Vector DB, Azure, scaling, workflows.
- Key requirement: Strong applied ML understanding not just integrating APIs.
Candidates should be able to decide when to use heuristics vs classical ML vs LLM-based approaches, and justify trade-offs (accuracy, latency, cost, complexity). Experience evaluating models in real-world settings is important. - ML spectrum (tree-based algos, stats, NLP, vectors) is a huge plus but must include hands-on exposure to applying or tuning models, not just consuming them via APIs.